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-              r5346dce
+              r9a72c4d
 \documentclass[AMA,STIX1COL]{WileyNJD-v2}
-\setlength\typewidth{170mm}
-\setlength\textwidth{170mm}
 \articletype{RESEARCH ARTICLE}%
+\received{12 March 2018}
+\revised{8 May 2018}
+\accepted{28 June 2018}
+\setlength\typewidth{168mm}
+\setlength\textwidth{168mm}
+\received{26 April 2016}
+\revised{6 June 2016}
+\accepted{6 June 2016}
 \raggedbottom
 …
+}
 \title{\texorpdfstring{\protect\CFA : Adding modern programming language features to C}{Cforall : Adding modern programming language features to C}}
+\title{\texorpdfstring{\protect\CFA : Adding Modern Programming Language Features to C}{Cforall : Adding Modern Programming Language Features to C}}
 \author[1]{Aaron Moss}
 \author[1]{Robert Schluntz}
 \author[1]{Peter A. Buhr}
+\author[1]{Peter A. Buhr*}
 \authormark{MOSS \textsc{et al}}
 \address[1]{\orgdiv{Cheriton School of Computer Science}, \orgname{University of Waterloo}, \orgaddress{\state{Waterloo, Ontario}, \country{Canada}}}
 \corres{Peter A. Buhr, Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada. \email{pabuhr{\char`\@}uwaterloo.ca}}
+\address[1]{\orgdiv{Cheriton School of Computer Science}, \orgname{University of Waterloo}, \orgaddress{\state{Waterloo, ON}, \country{Canada}}}
+\corres{*Peter A. Buhr, Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada. \email{pabuhr{\char`\@}uwaterloo.ca}}
 \fundingInfo{Natural Sciences and Engineering Research Council of Canada}
 \abstract[Summary]{
+The C programming language is a foundational technology for modern computing with millions of lines of code implementing everything from hobby projects to commercial operating systems.
+This installation base and the programmers producing it represent a massive software engineering investment spanning decades and likely to continue for decades more.
+Nevertheless, C, which was first standardized almost 30 years ago, lacks many features that make programming in more modern languages safer and more productive.
+The goal of the \CFA project (pronounced ``C for all'') is to create an extension of C that provides modern safety and productivity features while still ensuring strong backward compatibility with C and its programmers.
+Prior projects have attempted similar goals but failed to honor the C programming style;
+for instance, adding object-oriented or functional programming with garbage collection is a nonstarter for many C developers.
+Specifically, \CFA is designed to have an orthogonal feature set based closely on the C programming paradigm, so that \CFA features can be added \emph{incrementally} to existing C code bases, and C programmers can learn \CFA extensions on an as-needed basis, preserving investment in existing code and programmers.
+This paper presents a quick tour of \CFA features, showing how their design avoids shortcomings of similar features in C and other C-like languages.
+Experimental results are presented to validate several of the new features.
+The C programming language is a foundational technology for modern computing with millions of lines of code implementing everything from hobby projects to commercial operating-systems.
+This installation base and the programmers producing it represent a massive software-engineering investment spanning decades and likely to continue for decades more.
+Nevertheless, C, first standardized almost forty years ago, lacks many features that make programming in more modern languages safer and more productive.
+The goal of the \CFA project (pronounced ``C-for-all'') is to create an extension of C that provides modern safety and productivity features while still ensuring strong backwards compatibility with C and its programmers.
+Prior projects have attempted similar goals but failed to honour C programming-style;
+for instance, adding object-oriented or functional programming with garbage collection is a non-starter for many C developers.
+Specifically, \CFA is designed to have an orthogonal feature-set based closely on the C programming paradigm, so that \CFA features can be added \emph{incrementally} to existing C code-bases, and C programmers can learn \CFA extensions on an as-needed basis, preserving investment in existing code and programmers.
+This paper presents a quick tour of \CFA features showing how their design avoids shortcomings of similar features in C and other C-like languages.
+Finally, experimental results are presented to validate several of the new features.
 }%
 \keywords{C, Cforall, generic types, polymorphic functions, tuple types, variadic types}
+\keywords{generic types, tuple types, variadic types, polymorphic functions, C, Cforall}
 \begin{document}
 %\linenumbers                                            % comment out to turn off line numbering
+\linenumbers                                            % comment out to turn off line numbering
 \maketitle
-\vspace*{-10pt}
 \section{Introduction}
 The C programming language is a foundational technology for modern computing with millions of lines of code implementing everything from hobby projects to commercial operating systems.
 This installation base and the programmers producing it represent a massive software engineering investment spanning decades and likely to continue for decades more.
 The TIOBE index~\cite{TIOBE} ranks the top five most \emph{popular} programming languages as Java 15\%, \Textbf{C 12\%}, \Textbf{\CC 5.5\%}, and Python 5\%, \Csharp 4.5\% = 42\%, where the next 50 languages are less than 4\% each with a long tail.
 The top three rankings over the past 30 years are as follows.
+The C programming language is a foundational technology for modern computing with millions of lines of code implementing everything from hobby projects to commercial operating-systems.
+This installation base and the programmers producing it represent a massive software-engineering investment spanning decades and likely to continue for decades more.
+The TIOBE~\cite{TIOBE} ranks the top 5 most \emph{popular} programming languages as: Java 15\%, \Textbf{C 12\%}, \Textbf{\CC 5.5\%}, Python 5\%, \Csharp 4.5\% = 42\%, where the next 50 languages are less than 4\% each with a long tail.
+The top 3 rankings over the past 30 years are:
 \begin{center}
 \setlength{\tabcolsep}{10pt}
+\fontsize{9bp}{11bp}\selectfont
+\lstDeleteShortInline@%
+\begin{tabular}{@{}cccccccc@{}}
+                & 2018  & 2013  & 2008  & 2003  & 1998  & 1993  & 1988  \\
+Java    & 1             & 2             & 1             & 1             & 18    & --    & --    \\
+\lstDeleteShortInline@%
+\begin{tabular}{@{}rccccccc@{}}
+                & 2018  & 2013  & 2008  & 2003  & 1998  & 1993  & 1988  \\ \hline
+Java    & 1             & 2             & 1             & 1             & 18    & -             & -             \\
 \Textbf{C}& \Textbf{2} & \Textbf{1} & \Textbf{2} & \Textbf{2} & \Textbf{1} & \Textbf{1} & \Textbf{1} \\
 \CC             & 3             & 4             & 3             & 3             & 2             & 2             & 5             \\
 …
 Love it or hate it, C is extremely popular, highly used, and one of the few systems languages.
 In many cases, \CC is often used solely as a better C.
 Nevertheless, C, which was first standardized almost 30 years ago~\cite{ANSI89:C}, lacks many features that make programming in more modern languages safer and more productive.
 \CFA (pronounced ``C for all'' and written \CFA or Cforall) is an evolutionary extension of the C programming language that adds modern language features to C, while maintaining source and runtime compatibility in the familiar C programming model.
 The four key design goals for \CFA~\cite{Bilson03} are as follows:
 (1) the behavior of standard C code must remain the same when translated by a \CFA compiler as when translated by a C compiler;
 (2) the standard C code must be as fast and as small when translated by a \CFA compiler as when translated by a C compiler;
 (3) the \CFA code must be at least as portable as standard C code;
 (4) extensions introduced by \CFA must be translated in the most efficient way possible.
 These goals ensure that the existing C code bases can be converted into \CFA incrementally with minimal effort, and C programmers can productively generate \CFA code without training beyond the features being used.
 \CC is used similarly but has the disadvantages of multiple legacy design choices that cannot be updated and active divergence of the language model from C, requiring significant effort and training to incrementally add \CC to a C-based project.
 All language features discussed in this paper are working, except some advanced exception-handling features.
 Not discussed in this paper are the integrated concurrency constructs and user-level threading library~\cite{Delisle18}.
+Nevertheless, C, first standardized almost forty years ago~\cite{ANSI89:C}, lacks many features that make programming in more modern languages safer and more productive.
+\CFA (pronounced ``C-for-all'', and written \CFA or Cforall) is an evolutionary extension of the C programming language that adds modern language-features to C, while maintaining source and runtime compatibility in the familiar C programming model.
+The four key design goals for \CFA~\cite{Bilson03} are:
+(1) The behaviour of standard C code must remain the same when translated by a \CFA compiler as when translated by a C compiler;
+(2) Standard C code must be as fast and as small when translated by a \CFA compiler as when translated by a C compiler;
+(3) \CFA code must be at least as portable as standard C code;
+(4) Extensions introduced by \CFA must be translated in the most efficient way possible.
+These goals ensure existing C code-bases can be converted to \CFA incrementally with minimal effort, and C programmers can productively generate \CFA code without training beyond the features being used.
+\CC is used similarly, but has the disadvantages of multiple legacy design-choices that cannot be updated and active divergence of the language model from C, requiring significant effort and training to incrementally add \CC to a C-based project.
+All languages features discussed in this paper are working, except some advanced exception-handling features.
+Not discussed in this paper are the integrated concurrency-constructs and user-level threading-library~\cite{Delisle18}.
 \CFA is an \emph{open-source} project implemented as a source-to-source translator from \CFA to the gcc-dialect of C~\cite{GCCExtensions}, allowing it to leverage the portability and code optimizations provided by gcc, meeting goals (1)--(3).
+Ultimately, a compiler is necessary for advanced features and optimal performance.
 % @plg2[9]% cd cfa-cc/src; cloc ArgTweak CodeGen CodeTools Common Concurrency ControlStruct Designators GenPoly InitTweak MakeLibCfa.cc MakeLibCfa.h Parser ResolvExpr SymTab SynTree Tuples driver prelude main.cc
 % -------------------------------------------------------------------------------
 …
 % SUM:                           223           8203           8263          46479
 % -------------------------------------------------------------------------------
+The \CFA translator is 200+ files and 46\,000+ lines of code written in C/\CC.
+A translator versus a compiler makes it easier and faster to generate and debug the C object code rather than the intermediate, assembler, or machine code;
+ultimately, a compiler is necessary for advanced features and optimal performance.
+% The translator design is based on the \emph{visitor pattern}, allowing multiple passes over the abstract code-tree, which works well for incrementally adding new feature through additional visitor passes.
+Two key translator components are expression analysis, determining expression validity and what operations are required for its implementation, and code generation, dealing with multiple forms of overloading, polymorphism, and multiple return values by converting them into the C code for a C compiler that supports none of these features.
+Details of these components are available in chapters 2 and 3 in the work of Bilson~\cite{Bilson03} and form the base for the current \CFA translator.
+The \CFA translator is 200+ files and 46,000+ lines of code written in C/\CC.
+Starting with a translator versus a compiler makes it easier and faster to generate and debug C object-code rather than intermediate, assembler or machine code.
+The translator design is based on the \emph{visitor pattern}, allowing multiple passes over the abstract code-tree, which works well for incrementally adding new feature through additional visitor passes.
+At the heart of the translator is the type resolver, which handles the polymorphic function/type overload-resolution.
 % @plg2[8]% cd cfa-cc/src; cloc libcfa
 % -------------------------------------------------------------------------------
 …
 % SUM:                           100           1895           2785          11763
 % -------------------------------------------------------------------------------
 The \CFA runtime system is 100+ files and 11\,000+ lines of code, written in \CFA.
+The \CFA runtime system is 100+ files and 11,000+ lines of code, written in \CFA.
 Currently, the \CFA runtime is the largest \emph{user} of \CFA providing a vehicle to test the language features and implementation.
 % @plg2[6]% cd cfa-cc/src; cloc tests examples benchmark
 …
 % SUM:                           290          13175           3400          27776
 % -------------------------------------------------------------------------------
+% The \CFA tests are 290+ files and 27,000+ lines of code.
+% The tests illustrate syntactic and semantic features in \CFA, plus a growing number of runtime benchmarks.
+% The tests check for correctness and are used for daily regression testing of 3800+ commits.
+Finally, it is impossible to describe a programming language without usage before definition.
+Therefore, syntax and semantics appear before explanations;
+hence, patience is necessary until sufficient details are presented and discussed.
+Similarly, a detailed comparison with other programming languages is postponed until Section~\ref{s:RelatedWork}.
+\vspace*{-6pt}
+The \CFA tests are 290+ files and 27,000+ lines of code.
+The tests illustrate syntactic and semantic features in \CFA, plus a growing number of runtime benchmarks.
+The tests check for correctness and are used for daily regression testing of 3800+ commits.
+Finally, it is impossible to describe a programming language without usages before definitions.
+Therefore, syntax and semantics appear before explanations, and related work (Section~\ref{s:RelatedWork}) is deferred until \CFA is presented;
+hence, patience is necessary until details are discussed.
 \section{Polymorphic Functions}
 \CFA introduces both ad hoc and parametric polymorphism to C, with a design originally formalized by Ditchfield~\cite{Ditchfield92} and first implemented by Bilson~\cite{Bilson03}.
 Shortcomings are identified in the existing approaches to generic and variadic data types in C-like languages and how these shortcomings are avoided in \CFA.
 Specifically, the solution is both reusable and type checked, as well as conforming to the design goals of \CFA with ergonomic use of existing C abstractions.
+\CFA introduces both ad-hoc and parametric polymorphism to C, with a design originally formalized by Ditchfield~\cite{Ditchfield92}, and first implemented by Bilson~\cite{Bilson03}.
+Shortcomings are identified in existing approaches to generic and variadic data types in C-like languages and how these shortcomings are avoided in \CFA.
+Specifically, the solution is both reusable and type-checked, as well as conforming to the design goals of \CFA with ergonomic use of existing C abstractions.
 The new constructs are empirically compared with C and \CC approaches via performance experiments in Section~\ref{sec:eval}.
+\vspace*{-6pt}
+\subsection{Name overloading}
+\subsection{Name Overloading}
 \label{s:NameOverloading}
 \begin{quote}
 ``There are only two hard things in Computer Science: cache invalidation and \emph{naming things}.''---Phil Karlton
+There are only two hard things in Computer Science: cache invalidation and \emph{naming things} -- Phil Karlton
 \end{quote}
 \vspace{-9pt}
 C already has a limited form of ad hoc polymorphism in its basic arithmetic operators, which apply to a variety of different types using identical syntax.
+C already has a limited form of ad-hoc polymorphism in its basic arithmetic operators, which apply to a variety of different types using identical syntax.
 \CFA extends the built-in operator overloading by allowing users to define overloads for any function, not just operators, and even any variable;
 Section~\ref{sec:libraries} includes a number of examples of how this overloading simplifies \CFA programming relative to C.
 Code generation for these overloaded functions and variables is implemented by the usual approach of mangling the identifier names to include a representation of their type, while \CFA decides which overload to apply based on the same ``usual arithmetic conversions'' used in C to disambiguate operator overloads.
+\newpage
+As an example:
 \begin{cfa}
 int max = 2147483647;                                           $\C[4in]{// (1)}$
 …
 int max( int a, int b ) { return a < b ? b : a; }  $\C{// (3)}$
 double max( double a, double b ) { return a < b ? b : a; }  $\C{// (4)}\CRT$
 max( 7, -max );                                         $\C[3in]{// uses (3) and (1), by matching int from constant 7}$
+max( 7, -max );                                         $\C{// uses (3) and (1), by matching int from constant 7}$
 max( max, 3.14 );                                       $\C{// uses (4) and (2), by matching double from constant 3.14}$
 max( max, -max );                                       $\C{// ERROR, ambiguous}$
 int m = max( max, -max );                       $\C{// uses (3) and (1) twice, by matching return type}\CRT$
+int m = max( max, -max );                       $\C{// uses (3) and (1) twice, by matching return type}$
 \end{cfa}
 …
 In some cases, hundreds of names can be reduced to tens, resulting in a significant cognitive reduction.
 In the above, the name @max@ has a consistent meaning, and a programmer only needs to remember the single concept: maximum.
 To prevent significant ambiguities, \CFA uses the return type in selecting overloads, \eg in the assignment to @m@, the compiler uses @m@'s type to unambiguously select the most appropriate call to function @max@ (as does Ada).
+To prevent significant ambiguities, \CFA uses the return type in selecting overloads, \eg in the assignment to @m@, the compiler use @m@'s type to unambiguously select the most appropriate call to function @max@ (as does Ada).
 As is shown later, there are a number of situations where \CFA takes advantage of available type information to disambiguate, where other programming languages generate ambiguities.
 \Celeven added @_Generic@ expressions (see section~6.5.1.1 of the ISO/IEC 9899~\cite{C11}), which is used with preprocessor macros to provide ad hoc polymorphism;
+\Celeven added @_Generic@ expressions~\cite[\S~6.5.1.1]{C11}, which is used with preprocessor macros to provide ad-hoc polymorphism;
 however, this polymorphism is both functionally and ergonomically inferior to \CFA name overloading.
+The macro wrapping the generic expression imposes some limitations, for instance, it cannot implement the example above, because the variables @max@ are ambiguous with the functions @max@.
+The macro wrapping the generic expression imposes some limitations;
+\eg, it cannot implement the example above, because the variables @max@ are ambiguous with the functions @max@.
 Ergonomic limitations of @_Generic@ include the necessity to put a fixed list of supported types in a single place and manually dispatch to appropriate overloads, as well as possible namespace pollution from the dispatch functions, which must all have distinct names.
 \CFA supports @_Generic@ expressions for backward compatibility, but it is an unnecessary mechanism.
+\CFA supports @_Generic@ expressions for backwards compatibility, but it is an unnecessary mechanism. \TODO{actually implement that}
 % http://fanf.livejournal.com/144696.html
 …
+\vspace*{-10pt}
+\subsection{\texorpdfstring{\protect\lstinline{forall} functions}{forall functions}}
+\subsection{\texorpdfstring{\protect\lstinline{forall} Functions}{forall Functions}}
 \label{sec:poly-fns}
 The signature feature of \CFA is parametric-polymorphic functions~\cite{forceone:impl,Cormack90,Duggan96} with functions generalized using a @forall@ clause (giving the language its name).
+The signature feature of \CFA is parametric-polymorphic functions~\cite{forceone:impl,Cormack90,Duggan96} with functions generalized using a @forall@ clause (giving the language its name):
 \begin{cfa}
 `forall( otype T )` T identity( T val ) { return val; }
 …
 This @identity@ function can be applied to any complete \newterm{object type} (or @otype@).
 The type variable @T@ is transformed into a set of additional implicit parameters encoding sufficient information about @T@ to create and return a variable of that type.
 The \CFA implementation passes the size and alignment of the type represented by an @otype@ parameter, as well as an assignment operator, constructor, copy constructor, and destructor.
 If this extra information is not needed, for instance, for a pointer, the type parameter can be declared as a \newterm{data type} (or @dtype@).
 In \CFA, the polymorphic runtime cost is spread over each polymorphic call, because more arguments are passed to polymorphic functions;
 the experiments in Section~\ref{sec:eval} show this overhead is similar to \CC virtual function calls.
 A design advantage is that, unlike \CC template functions, \CFA polymorphic functions are compatible with C \emph{separate compilation}, preventing compilation and code bloat.
 Since bare polymorphic types provide a restricted set of available operations, \CFA provides a \newterm{type assertion}~\cite[pp.~37-44]{Alphard} mechanism to provide further type information, where type assertions may be variable or function declarations that depend on a polymorphic type variable.
 For example, the function @twice@ can be defined using the \CFA syntax for operator overloading.
+The \CFA implementation passes the size and alignment of the type represented by an @otype@ parameter, as well as an assignment operator, constructor, copy constructor and destructor.
+If this extra information is not needed, \eg for a pointer, the type parameter can be declared as a \newterm{data type} (or @dtype@).
+In \CFA, the polymorphic runtime-cost is spread over each polymorphic call, because more arguments are passed to polymorphic functions;
+the experiments in Section~\ref{sec:eval} show this overhead is similar to \CC virtual-function calls.
+A design advantage is that, unlike \CC template-functions, \CFA polymorphic-functions are compatible with C \emph{separate compilation}, preventing compilation and code bloat.
+Since bare polymorphic-types provide a restricted set of available operations, \CFA provides a \newterm{type assertion}~\cite[pp.~37-44]{Alphard} mechanism to provide further type information, where type assertions may be variable or function declarations that depend on a polymorphic type-variable.
+For example, the function @twice@ can be defined using the \CFA syntax for operator overloading:
 \begin{cfa}
 forall( otype T `| { T ?+?(T, T); }` ) T twice( T x ) { return x `+` x; }  $\C{// ? denotes operands}$
 int val = twice( twice( 3.7 ) );  $\C{// val == 14}$
 \end{cfa}
+This works for any type @T@ with a matching addition operator.
+The polymorphism is achieved by creating a wrapper function for calling @+@ with the @T@ bound to @double@ and then passing this function to the first call of @twice@.
+There is now the option of using the same @twice@ and converting the result into @int@ on assignment or creating another @twice@ with the type parameter @T@ bound to @int@ because \CFA uses the return type~\cite{Cormack81,Baker82,Ada} in its type analysis.
+The first approach has a late conversion from @double@ to @int@ on the final assignment, whereas the second has an early conversion to @int@.
+\CFA minimizes the number of conversions and their potential to lose information;
+hence, it selects the first approach, which corresponds with C programmer intuition.
+which works for any type @T@ with a matching addition operator.
+The polymorphism is achieved by creating a wrapper function for calling @+@ with @T@ bound to @double@, then passing this function to the first call of @twice@.
+There is now the option of using the same @twice@ and converting the result to @int@ on assignment, or creating another @twice@ with type parameter @T@ bound to @int@ because \CFA uses the return type~\cite{Cormack81,Baker82,Ada} in its type analysis.
+The first approach has a late conversion from @double@ to @int@ on the final assignment, while the second has an early conversion to @int@.
+\CFA minimizes the number of conversions and their potential to lose information, so it selects the first approach, which corresponds with C-programmer intuition.
 Crucial to the design of a new programming language are the libraries to access thousands of external software features.
 Like \CC, \CFA inherits a massive compatible library base, where other programming languages must rewrite or provide fragile interlanguage communication with C.
 A simple example is leveraging the existing type-unsafe (@void *@) C @bsearch@ to binary search a sorted float array.
+Like \CC, \CFA inherits a massive compatible library-base, where other programming languages must rewrite or provide fragile inter-language communication with C.
+A simple example is leveraging the existing type-unsafe (@void *@) C @bsearch@ to binary search a sorted float array:
 \begin{cfa}
 void * bsearch( const void * key, const void * base, size_t nmemb, size_t size,
 …
 double * val = (double *)bsearch( &key, vals, 10, sizeof(vals[0]), comp ); $\C{// search sorted array}$
 \end{cfa}
+This can be augmented simply with generalized, type-safe, \CFA-overloaded wrappers.
+which can be augmented simply with generalized, type-safe, \CFA-overloaded wrappers:
 \begin{cfa}
 forall( otype T | { int ?<?( T, T ); } ) T * bsearch( T key, const T * arr, size_t size ) {
 …
 \end{cfa}
 The nested function @comp@ provides the hidden interface from typed \CFA to untyped (@void *@) C, plus the cast of the result.
+% FIX
+Providing a hidden @comp@ function in \CC is awkward as lambdas do not use C calling conventions and template declarations cannot appear in block scope.
+In addition, an alternate kind of return is made available: position versus pointer to found element.
+\CC's type system cannot disambiguate between the two versions of @bsearch@ because it does not use the return type in overload resolution, nor can \CC separately compile a template @bsearch@.
+Providing a hidden @comp@ function in \CC is awkward as lambdas do not use C calling-conventions and template declarations cannot appear at block scope.
+As well, an alternate kind of return is made available: position versus pointer to found element.
+\CC's type-system cannot disambiguate between the two versions of @bsearch@ because it does not use the return type in overload resolution, nor can \CC separately compile a template @bsearch@.
 \CFA has replacement libraries condensing hundreds of existing C functions into tens of \CFA overloaded functions, all without rewriting the actual computations (see Section~\ref{sec:libraries}).
 …
 \end{cfa}
 Call site inferencing and nested functions provide a localized form of inheritance.
+Call-site inferencing and nested functions provide a localized form of inheritance.
 For example, the \CFA @qsort@ only sorts in ascending order using @<@.
 However, it is trivial to locally change this behavior.
+However, it is trivial to locally change this behaviour:
 \begin{cfa}
 forall( otype T | { int ?<?( T, T ); } ) void qsort( const T * arr, size_t size ) { /* use C qsort */ }
 int main() {
         int ?<?( double x, double y ) { return x `>` y; } $\C{// locally override behavior}$
+        int ?<?( double x, double y ) { return x `>` y; } $\C{// locally override behaviour}$
         qsort( vals, 10 );                                                      $\C{// descending sort}$
+}
 \end{cfa}
 The local version of @?<?@ performs @?>?@ overriding the built-in @?<?@ so it is passed to @qsort@.
 Therefore, programmers can easily form local environments, adding and modifying appropriate functions, to maximize the reuse of other existing functions and types.
 To reduce duplication, it is possible to distribute a group of @forall@ (and storage-class qualifiers) over functions/types, such that each block declaration is prefixed by the group (see the example in Appendix~\ref{s:CforallStack}).
+Hence, programmers can easily form local environments, adding and modifying appropriate functions, to maximize reuse of other existing functions and types.
+To reduce duplication, it is possible to distribute a group of @forall@ (and storage-class qualifiers) over functions/types, so each block declaration is prefixed by the group (see example in Appendix~\ref{s:CforallStack}).
 \begin{cfa}
 forall( otype `T` ) {                                                   $\C{// distribution block, add forall qualifier to declarations}$
 …
+\vspace*{-2pt}
 \subsection{Traits}
+\CFA provides \newterm{traits} to name a group of type assertions, where the trait name allows specifying the same set of assertions in multiple locations, preventing repetition mistakes at each function declaration.
+\CFA provides \newterm{traits} to name a group of type assertions, where the trait name allows specifying the same set of assertions in multiple locations, preventing repetition mistakes at each function declaration:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \end{cquote}
 Note that the @sumable@ trait does not include a copy constructor needed for the right side of @?+=?@ and return;
 it is provided by @otype@, which is syntactic sugar for the following trait.
+Note, the @sumable@ trait does not include a copy constructor needed for the right side of @?+=?@ and return;
+it is provided by @otype@, which is syntactic sugar for the following trait:
 \begin{cfa}
 trait otype( dtype T | sized(T) ) {  // sized is a pseudo-trait for types with known size and alignment
 …
 };
 \end{cfa}
 Given the information provided for an @otype@, variables of polymorphic type can be treated as if they were a complete type: stack allocatable, default or copy initialized, assigned, and deleted.
 In summation, the \CFA type system uses \newterm{nominal typing} for concrete types, matching with the C type system, and \newterm{structural typing} for polymorphic types.
+Given the information provided for an @otype@, variables of polymorphic type can be treated as if they were a complete type: stack-allocatable, default or copy-initialized, assigned, and deleted.
+In summation, the \CFA type-system uses \newterm{nominal typing} for concrete types, matching with the C type-system, and \newterm{structural typing} for polymorphic types.
 Hence, trait names play no part in type equivalence;
 the names are simply macros for a list of polymorphic assertions, which are expanded at usage sites.
 Nevertheless, trait names form a logical subtype hierarchy with @dtype@ at the top, where traits often contain overlapping assertions, \eg operator @+@.
 Traits are used like interfaces in Java or abstract base classes in \CC, but without the nominal inheritance relationships.
 Instead, each polymorphic function (or generic type) defines the structural type needed for its execution (polymorphic type key), and this key is fulfilled at each call site from the lexical environment, which is similar to the Go~\cite{Go} interfaces.
 Hence, new lexical scopes and nested functions are used extensively to create local subtypes, as in the @qsort@ example, without having to manage a nominal inheritance hierarchy.
+Nevertheless, trait names form a logical subtype-hierarchy with @dtype@ at the top, where traits often contain overlapping assertions, \eg operator @+@.
+Traits are used like interfaces in Java or abstract base-classes in \CC, but without the nominal inheritance-relationships.
+Instead, each polymorphic function (or generic type) defines the structural type needed for its execution (polymorphic type-key), and this key is fulfilled at each call site from the lexical environment, which is similar to Go~\cite{Go} interfaces.
+Hence, new lexical scopes and nested functions are used extensively to create local subtypes, as in the @qsort@ example, without having to manage a nominal-inheritance hierarchy.
 % (Nominal inheritance can be approximated with traits using marker variables or functions, as is done in Go.)
 …
 A significant shortcoming of standard C is the lack of reusable type-safe abstractions for generic data structures and algorithms.
 Broadly speaking, there are three approaches to implement abstract data structures in C.
 One approach is to write bespoke data structures for each context in which they are needed.
 While this approach is flexible and supports integration with the C type checker and tooling, it is also tedious and error prone, especially for more complex data structures.
 A second approach is to use @void *@-based polymorphism, \eg the C standard library functions @bsearch@ and @qsort@, which allow for the reuse of code with common functionality.
 However, basing all polymorphism on @void *@ eliminates the type checker's ability to ensure that argument types are properly matched, often requiring a number of extra function parameters, pointer indirection, and dynamic allocation that is otherwise not needed.
 A third approach to generic code is to use preprocessor macros, which does allow the generated code to be both generic and type checked, but errors may be difficult to interpret.
+Broadly speaking, there are three approaches to implement abstract data-structures in C.
+One approach is to write bespoke data-structures for each context in which they are needed.
+While this approach is flexible and supports integration with the C type-checker and tooling, it is also tedious and error-prone, especially for more complex data structures.
+A second approach is to use @void *@-based polymorphism, \eg the C standard-library functions @bsearch@ and @qsort@, which allow reuse of code with common functionality.
+However, basing all polymorphism on @void *@ eliminates the type-checker's ability to ensure that argument types are properly matched, often requiring a number of extra function parameters, pointer indirection, and dynamic allocation that is not otherwise needed.
+A third approach to generic code is to use preprocessor macros, which does allow the generated code to be both generic and type-checked, but errors may be difficult to interpret.
 Furthermore, writing and using preprocessor macros is unnatural and inflexible.
 \CC, Java, and other languages use \newterm{generic types} to produce type-safe abstract data types.
 \CFA generic types integrate efficiently and naturally with the existing polymorphic functions, while retaining backward compatibility with C and providing separate compilation.
+\CC, Java, and other languages use \newterm{generic types} to produce type-safe abstract data-types.
+\CFA generic types integrate efficiently and naturally with the existing polymorphic functions, while retaining backwards compatibility with C and providing separate compilation.
 However, for known concrete parameters, the generic-type definition can be inlined, like \CC templates.
 A generic type can be declared by placing a @forall@ specifier on a @struct@ or @union@ declaration and instantiated using a parenthesized list of types after the type name.
+A generic type can be declared by placing a @forall@ specifier on a @struct@ or @union@ declaration, and instantiated using a parenthesized list of types after the type name:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \CFA classifies generic types as either \newterm{concrete} or \newterm{dynamic}.
 Concrete types have a fixed memory layout regardless of type parameters, whereas dynamic types vary in memory layout depending on their type parameters.
+Concrete types have a fixed memory layout regardless of type parameters, while dynamic types vary in memory layout depending on their type parameters.
 A \newterm{dtype-static} type has polymorphic parameters but is still concrete.
 Polymorphic pointers are an example of dtype-static types;
 given some type variable @T@, @T@ is a polymorphic type, as is @T *@, but @T *@ has a fixed size and can, therefore, be represented by @void *@ in code generation.
 \CFA generic types also allow checked argument constraints.
 For example, the following declaration of a sorted set type ensures the set key supports equality and relational comparison.
+given some type variable @T@, @T@ is a polymorphic type, as is @T *@, but @T *@ has a fixed size and can therefore be represented by @void *@ in code generation.
+\CFA generic types also allow checked argument-constraints.
+For example, the following declaration of a sorted set-type ensures the set key supports equality and relational comparison:
 \begin{cfa}
 forall( otype Key | { _Bool ?==?(Key, Key); _Bool ?<?(Key, Key); } ) struct sorted_set;
 …
 \subsection{Concrete generic types}
 The \CFA translator template expands concrete generic types into new structure types, affording maximal inlining.
 To enable interoperation among equivalent instantiations of a generic type, the translator saves the set of instantiations currently in scope and reuses the generated structure declarations where appropriate.
 A function declaration that accepts or returns a concrete generic type produces a declaration for the instantiated structure in the same scope, which all callers may reuse.
 For example, the concrete instantiation for @pair( const char *, int )@ is
+\subsection{Concrete Generic-Types}
+The \CFA translator template-expands concrete generic-types into new structure types, affording maximal inlining.
+To enable inter-operation among equivalent instantiations of a generic type, the translator saves the set of instantiations currently in scope and reuses the generated structure declarations where appropriate.
+A function declaration that accepts or returns a concrete generic-type produces a declaration for the instantiated structure in the same scope, which all callers may reuse.
+For example, the concrete instantiation for @pair( const char *, int )@ is:
 \begin{cfa}
 struct _pair_conc0 {
 …
 \end{cfa}
 A concrete generic type with dtype-static parameters is also expanded to a structure type, but this type is used for all matching instantiations.
 In the above example, the @pair( F *, T * )@ parameter to @value@ is such a type; its expansion is below, and it is used as the type of the variables @q@ and @r@ as well, with casts for member access where appropriate.
+A concrete generic-type with dtype-static parameters is also expanded to a structure type, but this type is used for all matching instantiations.
+In the above example, the @pair( F *, T * )@ parameter to @value@ is such a type; its expansion is below and it is used as the type of the variables @q@ and @r@ as well, with casts for member access where appropriate:
 \begin{cfa}
 struct _pair_conc1 {
 …
+\subsection{Dynamic generic types}
+Though \CFA implements concrete generic types efficiently, it also has a fully general system for dynamic generic types.
+As mentioned in Section~\ref{sec:poly-fns}, @otype@ function parameters (in fact, all @sized@ polymorphic parameters) come with implicit size and alignment parameters provided by the caller.
+Dynamic generic types also have an \newterm{offset array} containing structure-member offsets.
+A dynamic generic @union@ needs no such offset array, as all members are at offset 0, but size and alignment are still necessary.
+Access to members of a dynamic structure is provided at runtime via base displacement addressing
+% FIX
+using the structure pointer and the member offset (similar to the @offsetof@ macro), moving a compile-time offset calculation to runtime.
+\subsection{Dynamic Generic-Types}
+Though \CFA implements concrete generic-types efficiently, it also has a fully general system for dynamic generic types.
+As mentioned in Section~\ref{sec:poly-fns}, @otype@ function parameters (in fact all @sized@ polymorphic parameters) come with implicit size and alignment parameters provided by the caller.
+Dynamic generic-types also have an \newterm{offset array} containing structure-member offsets.
+A dynamic generic-@union@ needs no such offset array, as all members are at offset 0, but size and alignment are still necessary.
+Access to members of a dynamic structure is provided at runtime via base-displacement addressing with the structure pointer and the member offset (similar to the @offsetof@ macro), moving a compile-time offset calculation to runtime.
 The offset arrays are statically generated where possible.
 If a dynamic generic type is declared to be passed or returned by value from a polymorphic function, the translator can safely assume that the generic type is complete (\ie has a known layout) at any call site, and the offset array is passed from the caller;
+If a dynamic generic-type is declared to be passed or returned by value from a polymorphic function, the translator can safely assume the generic type is complete (\ie has a known layout) at any call-site, and the offset array is passed from the caller;
 if the generic type is concrete at the call site, the elements of this offset array can even be statically generated using the C @offsetof@ macro.
 As an example, the body of the second @value@ function is implemented as
+As an example, the body of the second @value@ function is implemented as:
 \begin{cfa}
 _assign_T( _retval, p + _offsetof_pair[1] ); $\C{// return *p.second}$
 \end{cfa}
+\newpage
+\noindent
+Here, @_assign_T@ is passed in as an implicit parameter from @otype T@, and takes two @T *@ (@void *@ in the generated code), a destination and a source, and @_retval@ is the pointer to a caller-allocated buffer for the return value, the usual \CFA method to handle dynamically sized return types.
+@_offsetof_pair@ is the offset array passed into @value@;
+this array is generated at the call site as
+@_assign_T@ is passed in as an implicit parameter from @otype T@, and takes two @T *@ (@void *@ in the generated code), a destination and a source; @_retval@ is the pointer to a caller-allocated buffer for the return value, the usual \CFA method to handle dynamically-sized return types.
+@_offsetof_pair@ is the offset array passed into @value@; this array is generated at the call site as:
 \begin{cfa}
 size_t _offsetof_pair[] = { offsetof( _pair_conc0, first ), offsetof( _pair_conc0, second ) }
 \end{cfa}
 In some cases, the offset arrays cannot be statically generated.
 For instance, modularity is generally provided in C by including an opaque forward declaration of a structure and associated accessor and mutator functions in a header file, with the actual implementations in a separately compiled @.c@ file.
 \CFA supports this pattern for generic types, but the caller does not know the actual layout or size of the dynamic generic type and only holds it by a pointer.
+In some cases the offset arrays cannot be statically generated.
+For instance, modularity is generally provided in C by including an opaque forward-declaration of a structure and associated accessor and mutator functions in a header file, with the actual implementations in a separately-compiled @.c@ file.
+\CFA supports this pattern for generic types, but the caller does not know the actual layout or size of the dynamic generic-type, and only holds it by a pointer.
 The \CFA translator automatically generates \newterm{layout functions} for cases where the size, alignment, and offset array of a generic struct cannot be passed into a function from that function's caller.
 These layout functions take as arguments pointers to size and alignment variables and a caller-allocated array of member offsets, as well as the size and alignment of all @sized@ parameters to the generic structure (un@sized@ parameters are forbidden from being used in a context that affects layout).
 …
 Whether a type is concrete, dtype-static, or dynamic is decided solely on the @forall@'s type parameters.
 This design allows opaque forward declarations of generic types, \eg @forall(otype T)@ @struct Box@ -- like in C, all uses of @Box(T)@ can be separately compiled, and callers from other translation units know the proper calling conventions to use.
+If the definition of a structure type is included in deciding whether a generic type is dynamic or concrete, some further types may be recognized as dtype-static (\eg @forall(otype T)@ @struct unique_ptr { T * p }@ does not depend on @T@ for its layout, but the existence of an @otype@ parameter means that it \emph{could}.);
+however, preserving separate compilation (and the associated C compatibility) in the existing design is judged to be an appropriate trade-off.
+If the definition of a structure type is included in deciding whether a generic type is dynamic or concrete, some further types may be recognized as dtype-static (\eg @forall(otype T)@ @struct unique_ptr { T * p }@ does not depend on @T@ for its layout, but the existence of an @otype@ parameter means that it \emph{could}.), but preserving separate compilation (and the associated C compatibility) in the existing design is judged to be an appropriate trade-off.
 …
+}
 \end{cfa}
+Since @pair( T *, T * )@ is a concrete type, there are no implicit parameters passed to @lexcmp@;
+hence, the generated code is identical to a function written in standard C using @void *@, yet the \CFA version is type checked to ensure members of both pairs and arguments to the comparison function match in type.
+Another useful pattern enabled by reused dtype-static type instantiations is zero-cost \newterm{tag structures}.
+Sometimes, information is only used for type checking and can be omitted at runtime.
+Since @pair( T *, T * )@ is a concrete type, there are no implicit parameters passed to @lexcmp@, so the generated code is identical to a function written in standard C using @void *@, yet the \CFA version is type-checked to ensure the members of both pairs and the arguments to the comparison function match in type.
+Another useful pattern enabled by reused dtype-static type instantiations is zero-cost \newterm{tag-structures}.
+Sometimes information is only used for type-checking and can be omitted at runtime, \eg:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
                                                         half_marathon;
 scalar(litres) two_pools = pool + pool;
 `marathon + pool;` // ERROR, mismatched types
+`marathon + pool;`      // ERROR, mismatched types
 \end{cfa}
 \end{tabular}
 \lstMakeShortInline@%
 \end{cquote}
+Here, @scalar@ is a dtype-static type;
+hence, all uses have a single structure definition, containing @unsigned long@, and can share the same implementations of common functions like @?+?@.
+@scalar@ is a dtype-static type, so all uses have a single structure definition, containing @unsigned long@, and can share the same implementations of common functions like @?+?@.
 These implementations may even be separately compiled, unlike \CC template functions.
 However, the \CFA type checker ensures matching types are used by all calls to @?+?@, preventing nonsensical computations like adding a length to a volume.
+However, the \CFA type-checker ensures matching types are used by all calls to @?+?@, preventing nonsensical computations like adding a length to a volume.
 …
 \label{sec:tuples}
 In many languages, functions can return, at most, one value;
+In many languages, functions can return at most one value;
 however, many operations have multiple outcomes, some exceptional.
 Consider C's @div@ and @remquo@ functions, which return the quotient and remainder for a division of integer and float values, respectively.
 …
 double r = remquo( 13.5, 5.2, &q );                     $\C{// return remainder, alias quotient}$
 \end{cfa}
 Here, @div@ aggregates the quotient/remainder in a structure, whereas @remquo@ aliases a parameter to an argument.
+@div@ aggregates the quotient/remainder in a structure, while @remquo@ aliases a parameter to an argument.
 Both approaches are awkward.
+% FIX
+Alternatively, a programming language can directly support returning multiple values, \eg \CFA provides the following.
+Alternatively, a programming language can directly support returning multiple values, \eg in \CFA:
 \begin{cfa}
 [ int, int ] div( int num, int den );           $\C{// return two integers}$
 …
 This approach is straightforward to understand and use;
 therefore, why do few programming languages support this obvious feature or provide it awkwardly?
 To answer, there are complex consequences that cascade through multiple aspects of the language, especially the type system.
 This section shows these consequences and how \CFA handles them.
+To answer, there are complex consequences that cascade through multiple aspects of the language, especially the type-system.
+This section show these consequences and how \CFA handles them.
 \subsection{Tuple Expressions}
 The addition of multiple-return-value functions (MRVFs) is \emph{useless} without a syntax for accepting multiple values at the call site.
+The addition of multiple-return-value functions (MRVF) are \emph{useless} without a syntax for accepting multiple values at the call-site.
 The simplest mechanism for capturing the return values is variable assignment, allowing the values to be retrieved directly.
 As such, \CFA allows assigning multiple values from a function into multiple variables, using a square-bracketed list of lvalue expressions (as above), called a \newterm{tuple}.
 However, functions also use \newterm{composition} (nested calls), with the direct consequence that MRVFs must also support composition to be orthogonal with single-returning-value functions (SRVFs), \eg, \CFA provides the following.
+However, functions also use \newterm{composition} (nested calls), with the direct consequence that MRVFs must also support composition to be orthogonal with single-returning-value functions (SRVF), \eg:
 \begin{cfa}
 printf( "%d %d\n", div( 13, 5 ) );                      $\C{// return values seperated into arguments}$
 \end{cfa}
 Here, the values returned by @div@ are composed with the call to @printf@ by flattening the tuple into separate arguments.
 However, the \CFA type-system must support significantly more complex composition.
+However, the \CFA type-system must support significantly more complex composition:
 \begin{cfa}
 [ int, int ] foo$\(_1\)$( int );                        $\C{// overloaded foo functions}$
 …
 `bar`( foo( 3 ), foo( 3 ) );
 \end{cfa}
+The type resolver only has the tuple return types to resolve the call to @bar@ as the @foo@ parameters are identical, which involves unifying the possible @foo@ functions with @bar@'s parameter list.
+No combination of @foo@s is an exact match with @bar@'s parameters;
+thus, the resolver applies C conversions.
+% FIX
+The type-resolver only has the tuple return-types to resolve the call to @bar@ as the @foo@ parameters are identical, which involves unifying the possible @foo@ functions with @bar@'s parameter list.
+No combination of @foo@s are an exact match with @bar@'s parameters, so the resolver applies C conversions.
 The minimal cost is @bar( foo@$_1$@( 3 ), foo@$_2$@( 3 ) )@, giving (@int@, {\color{ForestGreen}@int@}, @double@) to (@int@, {\color{ForestGreen}@double@}, @double@) with one {\color{ForestGreen}safe} (widening) conversion from @int@ to @double@ versus ({\color{red}@double@}, {\color{ForestGreen}@int@}, {\color{ForestGreen}@int@}) to ({\color{red}@int@}, {\color{ForestGreen}@double@}, {\color{ForestGreen}@double@}) with one {\color{red}unsafe} (narrowing) conversion from @double@ to @int@ and two safe conversions.
 \subsection{Tuple variables}
+\subsection{Tuple Variables}
 An important observation from function composition is that new variable names are not required to initialize parameters from an MRVF.
+\CFA also allows declaration of tuple variables that can be initialized from an MRVF, since it can be awkward to declare multiple variables of different types.
+\newpage
+\CFA also allows declaration of tuple variables that can be initialized from an MRVF, since it can be awkward to declare multiple variables of different types, \eg:
 \begin{cfa}
 [ int, int ] qr = div( 13, 5 );                         $\C{// tuple-variable declaration and initialization}$
 [ double, double ] qr = div( 13.5, 5.2 );
 \end{cfa}
 Here, the tuple variable name serves the same purpose as the parameter name(s).
+where the tuple variable-name serves the same purpose as the parameter name(s).
 Tuple variables can be composed of any types, except for array types, since array sizes are generally unknown in C.
 One way to access the tuple variable components is with assignment or composition.
+One way to access the tuple-variable components is with assignment or composition:
 \begin{cfa}
 [ q, r ] = qr;                                                          $\C{// access tuple-variable components}$
 printf( "%d %d\n", qr );
 \end{cfa}
 \CFA also supports \newterm{tuple indexing} to access single components of a tuple expression.
+\CFA also supports \newterm{tuple indexing} to access single components of a tuple expression:
 \begin{cfa}
 [int, int] * p = &qr;                                           $\C{// tuple pointer}$
 …
 \subsection{Flattening and restructuring}
+\subsection{Flattening and Restructuring}
 In function call contexts, tuples support implicit flattening and restructuring conversions.
 Tuple flattening recursively expands a tuple into the list of its basic components.
 Tuple structuring packages a list of expressions into a value of tuple type.
+Tuple structuring packages a list of expressions into a value of tuple type, \eg:
 \begin{cfa}
 int f( int, int );
 …
 h( x, y );                                                                      $\C{// flatten and structure}$
 \end{cfa}
 In the call to @f@, @x@ is implicitly flattened so the components of @x@ are passed as two arguments.
+In the call to @f@, @x@ is implicitly flattened so the components of @x@ are passed as the two arguments.
 In the call to @g@, the values @y@ and @10@ are structured into a single argument of type @[int, int]@ to match the parameter type of @g@.
 Finally, in the call to @h@, @x@ is flattened to yield an argument list of length 3, of which the first component of @x@ is passed as the first parameter of @h@, and the second component of @x@ and @y@ are structured into the second argument of type @[int, int]@.
+The flexible structure of tuples permits a simple and expressive function call syntax to work seamlessly with both SRVFs and MRVFs with any number of arguments of arbitrarily complex structure.
+\subsection{Tuple assignment}
+\enlargethispage{-10pt}
+The flexible structure of tuples permits a simple and expressive function call syntax to work seamlessly with both SRVF and MRVF, and with any number of arguments of arbitrarily complex structure.
+\subsection{Tuple Assignment}
 An assignment where the left side is a tuple type is called \newterm{tuple assignment}.
 There are two kinds of tuple assignment depending on whether the right side of the assignment operator has a tuple type or a nontuple type, called \newterm{multiple} and \newterm{mass assignment}, respectively.
+There are two kinds of tuple assignment depending on whether the right side of the assignment operator has a tuple type or a non-tuple type, called \newterm{multiple} and \newterm{mass assignment}, respectively.
 \begin{cfa}
 int x = 10;
 …
 [y, x] = 3.14;                                                          $\C{// mass assignment}$
 \end{cfa}
 Both kinds of tuple assignment have parallel semantics, so that each value on the left and right sides is evaluated before any assignments occur.
+Both kinds of tuple assignment have parallel semantics, so that each value on the left and right side is evaluated before any assignments occur.
 As a result, it is possible to swap the values in two variables without explicitly creating any temporary variables or calling a function, \eg, @[x, y] = [y, x]@.
 This semantics means mass assignment differs from C cascading assignment (\eg @a = b = c@) in that conversions are applied in each individual assignment, which prevents data loss from the chain of conversions that can happen during a cascading assignment.
+For example, @[y, x] = 3.14@ performs the assignments @y = 3.14@ and @x = 3.14@, yielding @y == 3.14@ and @x == 3@, whereas C cascading assignment @y = x = 3.14@ performs the assignments @x = 3.14@ and @y = x@, yielding @3@ in @y@ and @x@.
+For example, @[y, x] = 3.14@ performs the assignments @y = 3.14@ and @x = 3.14@, yielding @y == 3.14@ and @x == 3@;
+whereas, C cascading assignment @y = x = 3.14@ performs the assignments @x = 3.14@ and @y = x@, yielding @3@ in @y@ and @x@.
 Finally, tuple assignment is an expression where the result type is the type of the left-hand side of the assignment, just like all other assignment expressions in C.
 This example shows mass, multiple, and cascading assignment used in one expression.
+This example shows mass, multiple, and cascading assignment used in one expression:
 \begin{cfa}
 [void] f( [int, int] );
 …
 \subsection{Member access}
 It is also possible to access multiple members from a single expression using a \newterm{member access}.
 The result is a single tuple-valued expression whose type is the tuple of the types of the members.
+\subsection{Member Access}
+It is also possible to access multiple members from a single expression using a \newterm{member-access}.
+The result is a single tuple-valued expression whose type is the tuple of the types of the members, \eg:
 \begin{cfa}
 struct S { int x; double y; char * z; } s;
 …
 [int, int, int] y = x.[2, 0, 2];                        $\C{// duplicate: [y.0, y.1, y.2] = [x.2, x.0.x.2]}$
 \end{cfa}
 It is also possible for a member access to contain other member accesses.
+It is also possible for a member access to contain other member accesses, \eg:
 \begin{cfa}
 struct A { double i; int j; };
 …
 Tuples also integrate with \CFA polymorphism as a kind of generic type.
 Due to the implicit flattening and structuring conversions involved in argument passing, @otype@ and @dtype@ parameters are restricted to matching only with nontuple types.
+Due to the implicit flattening and structuring conversions involved in argument passing, @otype@ and @dtype@ parameters are restricted to matching only with non-tuple types, \eg:
 \begin{cfa}
 forall( otype T, dtype U ) void f( T x, U * y );
 f( [5, "hello"] );
 \end{cfa}
 Here, @[5, "hello"]@ is flattened, giving argument list @5, "hello"@, and @T@ binds to @int@ and @U@ binds to @const char@.
+where @[5, "hello"]@ is flattened, giving argument list @5, "hello"@, and @T@ binds to @int@ and @U@ binds to @const char@.
 Tuples, however, may contain polymorphic components.
 For example, a plus operator can be written to sum two triples.
 …
 g( 5, 10.21 );
 \end{cfa}
-\newpage
 Hence, function parameter and return lists are flattened for the purposes of type unification allowing the example to pass expression resolution.
 This relaxation is possible by extending the thunk scheme described by Bilson~\cite{Bilson03}.
 …
 \subsection{Variadic tuples}
+\subsection{Variadic Tuples}
 \label{sec:variadic-tuples}
 To define variadic functions, \CFA adds a new kind of type parameter, \ie @ttype@ (tuple type).
 Matching against a @ttype@ parameter consumes all the remaining argument components and packages them into a tuple, binding to the resulting tuple of types.
 In a given parameter list, there must be, at most, one @ttype@ parameter that occurs last, which matches normal variadic semantics, with a strong feeling of similarity to \CCeleven variadic templates.
+To define variadic functions, \CFA adds a new kind of type parameter, @ttype@ (tuple type).
+Matching against a @ttype@ parameter consumes all remaining argument components and packages them into a tuple, binding to the resulting tuple of types.
+In a given parameter list, there must be at most one @ttype@ parameter that occurs last, which matches normal variadic semantics, with a strong feeling of similarity to \CCeleven variadic templates.
 As such, @ttype@ variables are also called \newterm{argument packs}.
 …
 Since nothing is known about a parameter pack by default, assertion parameters are key to doing anything meaningful.
 Unlike variadic templates, @ttype@ polymorphic functions can be separately compiled.
 For example, the following is a generalized @sum@ function.
+For example, a generalized @sum@ function:
 \begin{cfa}
 int sum$\(_0\)$() { return 0; }
 …
 \end{cfa}
 Since @sum@\(_0\) does not accept any arguments, it is not a valid candidate function for the call @sum(10, 20, 30)@.
 In order to call @sum@\(_1\), @10@ is matched with @x@, and the argument resolution moves on to the argument pack @rest@, which consumes the remainder of the argument list, and @Params@ is bound to @[20, 30]@.
+In order to call @sum@\(_1\), @10@ is matched with @x@, and the argument resolution moves on to the argument pack @rest@, which consumes the remainder of the argument list and @Params@ is bound to @[20, 30]@.
 The process continues until @Params@ is bound to @[]@, requiring an assertion @int sum()@, which matches @sum@\(_0\) and terminates the recursion.
 Effectively, this algorithm traces as @sum(10, 20, 30)@ $\rightarrow$ @10 + sum(20, 30)@ $\rightarrow$ @10 + (20 + sum(30))@ $\rightarrow$ @10 + (20 + (30 + sum()))@ $\rightarrow$ @10 + (20 + (30 + 0))@.
 It is reasonable to take the @sum@ function a step further to enforce a minimum number of arguments.
+It is reasonable to take the @sum@ function a step further to enforce a minimum number of arguments:
 \begin{cfa}
 int sum( int x, int y ) { return x + y; }
 …
+}
 \end{cfa}
 One more step permits the summation of any sumable type with all arguments of the same type.
+One more step permits the summation of any sumable type with all arguments of the same type:
 \begin{cfa}
 trait sumable( otype T ) {
 …
 This example showcases a variadic-template-like decomposition of the provided argument list.
 The individual @print@ functions allow printing a single element of a type.
 The polymorphic @print@ allows printing any list of types, where each individual type has a @print@ function.
+The polymorphic @print@ allows printing any list of types, where as each individual type has a @print@ function.
 The individual print functions can be used to build up more complicated @print@ functions, such as @S@, which cannot be done with @printf@ in C.
 This mechanism is used to seamlessly print tuples in the \CFA I/O library (see Section~\ref{s:IOLibrary}).
 Finally, it is possible to use @ttype@ polymorphism to provide arbitrary argument forwarding functions.
 For example, it is possible to write @new@ as a library function.
+For example, it is possible to write @new@ as a library function:
 \begin{cfa}
 forall( otype R, otype S ) void ?{}( pair(R, S) *, R, S );
 …
 \end{cfa}
 The @new@ function provides the combination of type-safe @malloc@ with a \CFA constructor call, making it impossible to forget constructing dynamically allocated objects.
 This function provides the type safety of @new@ in \CC, without the need to specify the allocated type again, due to return-type inference.
+This function provides the type-safety of @new@ in \CC, without the need to specify the allocated type again, thanks to return-type inference.
 …
 Tuples are implemented in the \CFA translator via a transformation into \newterm{generic types}.
+For each $N$, the first time an $N$-tuple is seen in a scope, a generic type with $N$ type parameters is generated.
+For example, the following
+For each $N$, the first time an $N$-tuple is seen in a scope a generic type with $N$ type parameters is generated, \eg:
 \begin{cfa}
 [int, int] f() {
 …
+}
 \end{cfa}
 is transformed into
+is transformed into:
 \begin{cfa}
 forall( dtype T0, dtype T1 | sized(T0) | sized(T1) ) struct _tuple2 {
 …
 The various kinds of tuple assignment, constructors, and destructors generate GNU C statement expressions.
 A variable is generated to store the value produced by a statement expression, since its members may need to be constructed with a nontrivial constructor and it may need to be referred to multiple time, \eg in a unique expression.
+A variable is generated to store the value produced by a statement expression, since its members may need to be constructed with a non-trivial constructor and it may need to be referred to multiple time, \eg in a unique expression.
 The use of statement expressions allows the translator to arbitrarily generate additional temporary variables as needed, but binds the implementation to a non-standard extension of the C language.
 However, there are other places where the \CFA translator makes use of GNU C extensions, such as its use of nested functions, so this restriction is not new.
 …
 \section{Control Structures}
 \CFA identifies inconsistent, problematic, and missing control structures in C, as well as extends, modifies, and adds control structures to increase functionality and safety.
 \subsection{\texorpdfstring{\protect\lstinline@if@ statement}{if statement}}
 The @if@ expression allows declarations, similar to the @for@ declaration expression.
+\CFA identifies inconsistent, problematic, and missing control structures in C, and extends, modifies, and adds control structures to increase functionality and safety.
+\subsection{\texorpdfstring{\protect\lstinline{if} Statement}{if Statement}}
+The @if@ expression allows declarations, similar to @for@ declaration expression:
 \begin{cfa}
 if ( int x = f() ) ...                                          $\C{// x != 0}$
 …
 \end{cfa}
 Unless a relational expression is specified, each variable is compared not equal to 0, which is the standard semantics for the @if@ expression, and the results are combined using the logical @&&@ operator.\footnote{\CC only provides a single declaration always compared not equal to 0.}
 The scope of the declaration(s) is local to the @if@ statement but exists within both the ``then'' and ``else'' clauses.
 \subsection{\texorpdfstring{\protect\lstinline@switch@ statement}{switch statement}}
+The scope of the declaration(s) is local to the @if@ statement but exist within both the ``then'' and ``else'' clauses.
+\subsection{\texorpdfstring{\protect\lstinline{switch} Statement}{switch Statement}}
 There are a number of deficiencies with the C @switch@ statements: enumerating @case@ lists, placement of @case@ clauses, scope of the switch body, and fall through between case clauses.
 C has no shorthand for specifying a list of case values, whether the list is noncontiguous or contiguous\footnote{C provides this mechanism via fall through.}.
 \CFA provides a shorthand for a noncontiguous list:
+C has no shorthand for specifying a list of case values, whether the list is non-contiguous or contiguous\footnote{C provides this mechanism via fall through.}.
+\CFA provides a shorthand for a non-contiguous list:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \lstMakeShortInline@%
 \end{cquote}
+for a contiguous list:\footnote{gcc has the same mechanism but awkward syntax, \lstinline@2 ...42@, as a space is required after a number;
+otherwise, the first period is a decimal point.}
+for a contiguous list:\footnote{gcc has the same mechanism but awkward syntax, \lstinline@2 ...42@, as a space is required after a number, otherwise the first period is a decimal point.}
 \begin{cquote}
 \lstDeleteShortInline@%
 …
+}
 \end{cfa}
 \CFA precludes this form of transfer \emph{into} a control structure because it causes an undefined behavior, especially with respect to missed initialization, and provides very limited functionality.
 C allows placement of declaration within the @switch@ body and unreachable code at the start, resulting in an undefined behavior.
+\CFA precludes this form of transfer \emph{into} a control structure because it causes undefined behaviour, especially with respect to missed initialization, and provides very limited functionality.
+C allows placement of declaration within the @switch@ body and unreachable code at the start, resulting in undefined behaviour:
 \begin{cfa}
 switch ( x ) {
 …
 C @switch@ provides multiple entry points into the statement body, but once an entry point is selected, control continues across \emph{all} @case@ clauses until the end of the @switch@ body, called \newterm{fall through};
+@case@ clauses are made disjoint by the @break@
+\newpage
+\noindent
+statement.
+@case@ clauses are made disjoint by the @break@ statement.
 While fall through \emph{is} a useful form of control flow, it does not match well with programmer intuition, resulting in errors from missing @break@ statements.
 For backward compatibility, \CFA provides a \emph{new} control structure, \ie @choose@, which mimics @switch@, but reverses the meaning of fall through (see Figure~\ref{f:ChooseSwitchStatements}), similar to Go.
+For backwards compatibility, \CFA provides a \emph{new} control structure, @choose@, which mimics @switch@, but reverses the meaning of fall through (see Figure~\ref{f:ChooseSwitchStatements}), similar to Go.
 \begin{figure}
 \centering
-\fontsize{9bp}{11bp}\selectfont
 \lstDeleteShortInline@%
 \begin{tabular}{@{}l|@{\hspace{\parindentlnth}}l@{}}
 …
 \end{tabular}
 \lstMakeShortInline@%
 \caption{\lstinline|choose| versus \lstinline|switch| statements}
+\caption{\lstinline|choose| versus \lstinline|switch| Statements}
 \label{f:ChooseSwitchStatements}
-\vspace*{-11pt}
 \end{figure}
 Finally, Figure~\ref{f:FallthroughStatement} shows @fallthrough@ may appear in contexts other than terminating a @case@ clause and have an explicit transfer label allowing separate cases but common final code for a set of cases.
+Finally, Figure~\ref{f:FallthroughStatement} shows @fallthrough@ may appear in contexts other than terminating a @case@ clause, and have an explicit transfer label allowing separate cases but common final-code for a set of cases.
 The target label must be below the @fallthrough@ and may not be nested in a control structure, \ie @fallthrough@ cannot form a loop, and the target label must be at the same or higher level as the containing @case@ clause and located at the same level as a @case@ clause;
 the target label may be case @default@, but only associated with the current @switch@/@choose@ statement.
 …
 \begin{figure}
 \centering
-\fontsize{9bp}{11bp}\selectfont
 \lstDeleteShortInline@%
 \begin{tabular}{@{}l|@{\hspace{\parindentlnth}}l@{}}
 …
 \end{tabular}
 \lstMakeShortInline@%
 \caption{\lstinline|fallthrough| statement}
+\caption{\lstinline|fallthrough| Statement}
 \label{f:FallthroughStatement}
-\vspace*{-11pt}
 \end{figure}
+\vspace*{-8pt}
+\subsection{\texorpdfstring{Labeled \protect\lstinline@continue@ / \protect\lstinline@break@}{Labeled continue / break}}
+\subsection{\texorpdfstring{Labelled \protect\lstinline{continue} / \protect\lstinline{break}}{Labelled continue / break}}
 While C provides @continue@ and @break@ statements for altering control flow, both are restricted to one level of nesting for a particular control structure.
 Unfortunately, this restriction forces programmers to use @goto@ to achieve the equivalent control flow for more than one level of nesting.
 To prevent having to switch to the @goto@, \CFA extends @continue@ and @break@ with a target label to support static multilevel exit~\cite{Buhr85}, as in Java.
+Unfortunately, this restriction forces programmers to use @goto@ to achieve the equivalent control-flow for more than one level of nesting.
+To prevent having to switch to the @goto@, \CFA extends the @continue@ and @break@ with a target label to support static multi-level exit~\cite{Buhr85}, as in Java.
 For both @continue@ and @break@, the target label must be directly associated with a @for@, @while@ or @do@ statement;
 for @break@, the target label can also be associated with a @switch@, @if@ or compound (@{}@) statement.
 Figure~\ref{f:MultiLevelExit} shows @continue@ and @break@ indicating the specific control structure and the corresponding C program using only @goto@ and labels.
 The innermost loop has seven exit points, which cause a continuation or termination of one or more of the seven nested control structures.
+Figure~\ref{f:MultiLevelExit} shows @continue@ and @break@ indicating the specific control structure, and the corresponding C program using only @goto@ and labels.
+The innermost loop has 7 exit points, which cause continuation or termination of one or more of the 7 nested control-structures.
 \begin{figure}
-\fontsize{9bp}{11bp}\selectfont
 \lstDeleteShortInline@%
 \begin{tabular}{@{\hspace{\parindentlnth}}l|@{\hspace{\parindentlnth}}l@{\hspace{\parindentlnth}}l@{}}
 …
 \end{tabular}
 \lstMakeShortInline@%
 \caption{Multilevel exit}
+\caption{Multi-level Exit}
 \label{f:MultiLevelExit}
-\vspace*{-5pt}
 \end{figure}
 With respect to safety, both labeled @continue@ and @break@ are @goto@ restricted in the following ways.
 \begin{list}{$\bullet$}{\topsep=4pt\itemsep=0pt\parsep=0pt}
+With respect to safety, both labelled @continue@ and @break@ are a @goto@ restricted in the following ways:
+\begin{itemize}
 \item
 They cannot create a loop, which means only the looping constructs cause looping.
 …
 \item
 They cannot branch into a control structure.
+This restriction prevents missing declarations and/or initializations at the start of a control structure resulting in an undefined behavior.
+\end{list}
+The advantage of the labeled @continue@/@break@ is allowing static multilevel exits without having to use the @goto@ statement and tying control flow to the target control structure rather than an arbitrary point in a program.
+Furthermore, the location of the label at the \emph{beginning} of the target control structure informs the reader (eye candy) that complex control flow is
+occurring in the body of the control structure.
+This restriction prevents missing declarations and/or initializations at the start of a control structure resulting in undefined behaviour.
+\end{itemize}
+The advantage of the labelled @continue@/@break@ is allowing static multi-level exits without having to use the @goto@ statement, and tying control flow to the target control structure rather than an arbitrary point in a program.
+Furthermore, the location of the label at the \emph{beginning} of the target control structure informs the reader (eye candy) that complex control-flow is occurring in the body of the control structure.
 With @goto@, the label is at the end of the control structure, which fails to convey this important clue early enough to the reader.
 Finally, using an explicit target for the transfer instead of an implicit target allows new constructs to be added or removed without affecting the existing constructs.
+Finally, using an explicit target for the transfer instead of an implicit target allows new constructs to be added or removed without affecting existing constructs.
 Otherwise, the implicit targets of the current @continue@ and @break@, \ie the closest enclosing loop or @switch@, change as certain constructs are added or removed.
+\vspace*{-5pt}
+\subsection{Exception handling}
+The following framework for \CFA exception handling is in place, excluding some runtime type information and virtual functions.
+\subsection{Exception Handling}
+The following framework for \CFA exception-handling is in place, excluding some runtime type-information and virtual functions.
 \CFA provides two forms of exception handling: \newterm{fix-up} and \newterm{recovery} (see Figure~\ref{f:CFAExceptionHandling})~\cite{Buhr92b,Buhr00a}.
 Both mechanisms provide dynamic call to a handler using dynamic name lookup, where fix-up has dynamic return and recovery has static return from the handler.
+Both mechanisms provide dynamic call to a handler using dynamic name-lookup, where fix-up has dynamic return and recovery has static return from the handler.
 \CFA restricts exception types to those defined by aggregate type @exception@.
 The form of the raise dictates the set of handlers examined during propagation: \newterm{resumption propagation} (@resume@) only examines resumption handlers (@catchResume@); \newterm{terminating propagation} (@throw@) only examines termination handlers (@catch@).
 If @resume@ or @throw@ has no exception type, it is a reresume/rethrow, which means that the current exception continues propagation.
+If @resume@ or @throw@ have no exception type, it is a reresume/rethrow, meaning the currently exception continues propagation.
 If there is no current exception, the reresume/rethrow results in a runtime error.
 \begin{figure}
-\fontsize{9bp}{11bp}\selectfont
-\lstDeleteShortInline@%
 \begin{cquote}
+\lstDeleteShortInline@%
 \begin{tabular}{@{}l|@{\hspace{\parindentlnth}}l@{}}
 \multicolumn{1}{@{}c|@{\hspace{\parindentlnth}}}{\textbf{Resumption}}   & \multicolumn{1}{c@{}}{\textbf{Termination}}   \\
 …
 \end{cfa}
 \end{tabular}
+\lstMakeShortInline@%
 \end{cquote}
+\lstMakeShortInline@%
+\caption{\CFA exception handling}
+\caption{\CFA Exception Handling}
 \label{f:CFAExceptionHandling}
-\vspace*{-5pt}
 \end{figure}
 The set of exception types in a list of catch clauses may include both a resumption and a termination handler.
+The set of exception types in a list of catch clause may include both a resumption and termination handler:
 \begin{cfa}
 try {
 …
 The termination handler is available because the resumption propagation did not unwind the stack.
 An additional feature is conditional matching in a catch clause.
+An additional feature is conditional matching in a catch clause:
 \begin{cfa}
 try {
 …
    catch ( IOError err ) { ... }                        $\C{// handler error from other files}$
 \end{cfa}
 Here, the throw inserts the failing file handle into the I/O exception.
 Conditional catch cannot be trivially mimicked by other mechanisms because once an exception is caught, handler clauses in that @try@ statement are no longer eligible.
 The resumption raise can specify an alternate stack on which to raise an exception, called a \newterm{nonlocal raise}.
+where the throw inserts the failing file-handle into the I/O exception.
+Conditional catch cannot be trivially mimicked by other mechanisms because once an exception is caught, handler clauses in that @try@ statement are no longer eligible..
+The resumption raise can specify an alternate stack on which to raise an exception, called a \newterm{nonlocal raise}:
 \begin{cfa}
 resume( $\emph{exception-type}$, $\emph{alternate-stack}$ )
 …
 Nonlocal raise is restricted to resumption to provide the exception handler the greatest flexibility because processing the exception does not unwind its stack, allowing it to continue after the handler returns.
 To facilitate nonlocal raise, \CFA provides dynamic enabling and disabling of nonlocal exception propagation.
 The constructs for controlling propagation of nonlocal exceptions are the @enable@ and @disable@ blocks.
+To facilitate nonlocal raise, \CFA provides dynamic enabling and disabling of nonlocal exception-propagation.
+The constructs for controlling propagation of nonlocal exceptions are the @enable@ and the @disable@ blocks:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \begin{cfa}
 enable $\emph{exception-type-list}$ {
         // allow nonlocal raise
+        // allow non-local raise
+}
 \end{cfa}
 …
 \begin{cfa}
 disable $\emph{exception-type-list}$ {
         // disallow nonlocal raise
+        // disallow non-local raise
+}
 \end{cfa}
 …
 The arguments for @enable@/@disable@ specify the exception types allowed to be propagated or postponed, respectively.
 Specifying no exception type is shorthand for specifying all exception types.
+Both @enable@ and @disable@ blocks can be nested;
+turning propagation on/off on entry and on exit, the specified exception types are restored to their prior state.
+Coroutines and tasks start with nonlocal exceptions disabled, allowing handlers to be put in place, before nonlocal exceptions are explicitly enabled.
+Both @enable@ and @disable@ blocks can be nested, turning propagation on/off on entry, and on exit, the specified exception types are restored to their prior state.
+Coroutines and tasks start with non-local exceptions disabled, allowing handlers to be put in place, before non-local exceptions are explicitly enabled.
 \begin{cfa}
 void main( mytask & t ) {                                       $\C{// thread starts here}$
         // nonlocal exceptions disabled
         try {                                                                   $\C{// establish handles for nonlocal exceptions}$
                 enable {                                                        $\C{// allow nonlocal exception delivery}$
+        // non-local exceptions disabled
+        try {                                                                   $\C{// establish handles for non-local exceptions}$
+                enable {                                                        $\C{// allow non-local exception delivery}$
                         // task body
+                }
 …
 \end{cfa}
 Finally, \CFA provides a Java-like  @finally@ clause after the catch clauses.
+Finally, \CFA provides a Java like  @finally@ clause after the catch clauses:
 \begin{cfa}
 try {
 …
+}
 \end{cfa}
 The finally clause is always executed, \ie, if the try block ends normally or if an exception is raised.
+The finally clause is always executed, i.e., if the try block ends normally or if an exception is raised.
 If an exception is raised and caught, the handler is run before the finally clause.
 Like a destructor (see Section~\ref{s:ConstructorsDestructors}), a finally clause can raise an exception but not if there is an exception being propagated.
 Mimicking the @finally@ clause with mechanisms like Resource Aquisition Is Initialization (RAII) is nontrivial when there are multiple types and local accesses.
 \subsection{\texorpdfstring{\protect\lstinline{with} statement}{with statement}}
+Mimicking the @finally@ clause with mechanisms like RAII is non-trivial when there are multiple types and local accesses.
+\subsection{\texorpdfstring{\protect\lstinline{with} Statement}{with Statement}}
 \label{s:WithStatement}
 Heterogeneous data are often aggregated into a structure/union.
 To reduce syntactic noise, \CFA provides a @with@ statement (see section~4.F in the Pascal User Manual and Report~\cite{Pascal}) to elide aggregate member qualification by opening a scope containing the member identifiers.
+Heterogeneous data is often aggregated into a structure/union.
+To reduce syntactic noise, \CFA provides a @with@ statement (see Pascal~\cite[\S~4.F]{Pascal}) to elide aggregate member-qualification by opening a scope containing the member identifiers.
 \begin{cquote}
 \vspace*{-\baselineskip}%???
 …
 Object-oriented programming languages only provide implicit qualification for the receiver.
 In detail, the @with@ statement has the form
+In detail, the @with@ statement has the form:
 \begin{cfa}
 $\emph{with-statement}$:
 …
 \end{cfa}
 and may appear as the body of a function or nested within a function body.
 Each expression in the expression list provides a type and object.
+Each expression in the expression-list provides a type and object.
 The type must be an aggregate type.
 (Enumerations are already opened.)
 The object is the implicit qualifier for the open structure members.
+The object is the implicit qualifier for the open structure-members.
 All expressions in the expression list are open in parallel within the compound statement, which is different from Pascal, which nests the openings from left to right.
 The difference between parallel and nesting occurs for members with the same name and type.
+The difference between parallel and nesting occurs for members with the same name and type:
 \begin{cfa}
 struct S { int `i`; int j; double m; } s, w;    $\C{// member i has same type in structure types S and T}$
 …
+}
 \end{cfa}
 For parallel semantics, both @s.i@ and @t.i@ are visible and, therefore, @i@ is ambiguous without qualification;
 for nested semantics, @t.i@ hides @s.i@ and, therefore, @i@ implies @t.i@.
+For parallel semantics, both @s.i@ and @t.i@ are visible, so @i@ is ambiguous without qualification;
+for nested semantics, @t.i@ hides @s.i@, so @i@ implies @t.i@.
 \CFA's ability to overload variables means members with the same name but different types are automatically disambiguated, eliminating most qualification when opening multiple aggregates.
 Qualification or a cast is used to disambiguate.
 There is an interesting problem between parameters and the function body @with@.
+There is an interesting problem between parameters and the function-body @with@, \eg:
 \begin{cfa}
 void ?{}( S & s, int i ) with ( s ) {           $\C{// constructor}$
 …
+}
 \end{cfa}
 Here, the assignment @s.i = i@ means @s.i = s.i@, which is meaningless, and there is no mechanism to qualify the parameter @i@, making the assignment impossible using the function body @with@.
 To solve this problem, parameters are treated like an initialized aggregate
+Here, the assignment @s.i = i@ means @s.i = s.i@, which is meaningless, and there is no mechanism to qualify the parameter @i@, making the assignment impossible using the function-body @with@.
+To solve this problem, parameters are treated like an initialized aggregate:
 \begin{cfa}
 struct Params {
 …
 } params;
 \end{cfa}
+\newpage
+and implicitly opened \emph{after} a function body open, to give them higher priority
+and implicitly opened \emph{after} a function-body open, to give them higher priority:
 \begin{cfa}
 void ?{}( S & s, int `i` ) with ( s ) `{` `with( $\emph{\color{red}params}$ )` {
 …
 } `}`
 \end{cfa}
 Finally, a cast may be used to disambiguate among overload variables in a @with@ expression
+Finally, a cast may be used to disambiguate among overload variables in a @with@ expression:
 \begin{cfa}
 with ( w ) { ... }                                                      $\C{// ambiguous, same name and no context}$
 with ( (S)w ) { ... }                                           $\C{// unambiguous, cast}$
 \end{cfa}
 and @with@ expressions may be complex expressions with type reference (see Section~\ref{s:References}) to aggregate
+and @with@ expressions may be complex expressions with type reference (see Section~\ref{s:References}) to aggregate:
 \begin{cfa}
 struct S { int i, j; } sv;
 …
 \CFA attempts to correct and add to C declarations, while ensuring \CFA subjectively ``feels like'' C.
 An important part of this subjective feel is maintaining C's syntax and procedural paradigm, as opposed to functional and object-oriented approaches in other systems languages such as \CC and Rust.
 Maintaining the C approach means that C coding patterns remain not only useable but idiomatic in \CFA, reducing the mental burden of retraining C programmers and switching between C and \CFA development.
+Maintaining the C approach means that C coding-patterns remain not only useable but idiomatic in \CFA, reducing the mental burden of retraining C programmers and switching between C and \CFA development.
 Nevertheless, some features from other approaches are undeniably convenient;
 \CFA attempts to adapt these features to the C paradigm.
 \subsection{Alternative declaration syntax}
+\subsection{Alternative Declaration Syntax}
 C declaration syntax is notoriously confusing and error prone.
 For example, many C programmers are confused by a declaration as simple as the following.
+For example, many C programmers are confused by a declaration as simple as:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \lstMakeShortInline@%
 \end{cquote}
 Is this an array of five pointers to integers or a pointer to an array of five integers?
+Is this an array of 5 pointers to integers or a pointer to an array of 5 integers?
 If there is any doubt, it implies productivity and safety issues even for basic programs.
 Another example of confusion results from the fact that a function name and its parameters are embedded within the return type, mimicking the way the return value is used at the function's call site.
 For example, a function returning a pointer to an array of integers is defined and used in the following way.
+For example, a function returning a pointer to an array of integers is defined and used in the following way:
 \begin{cfa}
 int `(*`f`())[`5`]` {...};                                      $\C{// definition}$
 …
 While attempting to make the two contexts consistent is a laudable goal, it has not worked out in practice.
+\newpage
+\CFA provides its own type, variable, and function declarations, using a different syntax~\cite[pp.~856--859]{Buhr94a}.
+The new declarations place qualifiers to the left of the base type, whereas C declarations place qualifiers to the right.
+\CFA provides its own type, variable and function declarations, using a different syntax~\cite[pp.~856--859]{Buhr94a}.
+The new declarations place qualifiers to the left of the base type, while C declarations place qualifiers to the right.
 The qualifiers have the same meaning but are ordered left to right to specify a variable's type.
 \begin{cquote}
 …
 \lstMakeShortInline@%
 \end{cquote}
 The only exception is bit-field specification, which always appears to the right of the base type.
+The only exception is bit-field specification, which always appear to the right of the base type.
 % Specifically, the character @*@ is used to indicate a pointer, square brackets @[@\,@]@ are used to represent an array or function return value, and parentheses @()@ are used to indicate a function parameter.
 However, unlike C, \CFA type declaration tokens are distributed across all variables in the declaration list.
 For instance, variables @x@ and @y@ of type pointer to integer are defined in \CFA as
+For instance, variables @x@ and @y@ of type pointer to integer are defined in \CFA as follows:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \end{comment}
 All specifiers (@extern@, @static@, \etc) and qualifiers (@const@, @volatile@, \etc) are used in the normal way with the new declarations and also appear left to right.
+All specifiers (@extern@, @static@, \etc) and qualifiers (@const@, @volatile@, \etc) are used in the normal way with the new declarations and also appear left to right, \eg:
 \begin{cquote}
 \lstDeleteShortInline@%
 \begin{tabular}{@{}l@{\hspace{2\parindentlnth}}l@{\hspace{2\parindentlnth}}l@{}}
 \multicolumn{1}{@{}c@{\hspace{2\parindentlnth}}}{\textbf{\CFA}} & \multicolumn{1}{c@{\hspace{2\parindentlnth}}}{\textbf{C}}     \\
 \begin{cfa}[basicstyle=\linespread{0.9}\fontsize{9bp}{12bp}\selectfont\sf]
+\begin{cfa}
 extern const * const int x;
 static const * [5] const int y;
 \end{cfa}
+&
 \begin{cfa}[basicstyle=\linespread{0.9}\fontsize{9bp}{12bp}\selectfont\sf]
+\begin{cfa}
 int extern const * const x;
 static const int (* const y)[5]
 \end{cfa}
+&
 \begin{cfa}[basicstyle=\linespread{0.9}\fontsize{9bp}{12bp}\selectfont\sf]
+\begin{cfa}
 // external const pointer to const int
 // internal const pointer to array of 5 const int
 …
 \end{cquote}
 Specifiers must appear at the start of a \CFA function declaration\footnote{\label{StorageClassSpecifier}
 The placement of a storage-class specifier other than at the beginning of the declaration specifiers in a declaration is an obsolescent feature (see section~6.11.5(1) in ISO/IEC 9899~\cite{C11}).}.
+The placement of a storage-class specifier other than at the beginning of the declaration specifiers in a declaration is an obsolescent feature.~\cite[\S~6.11.5(1)]{C11}}.
 The new declaration syntax can be used in other contexts where types are required, \eg casts and the pseudo-function @sizeof@:
 …
 The syntax of the new function-prototype declaration follows directly from the new function-definition syntax;
 also, parameter names are optional.
+as well, parameter names are optional, \eg:
 \begin{cfa}
 [ int x ] f ( /* void */ );             $\C[2.5in]{// returning int with no parameters}$
 …
 [ * int, int ] j ( int );               $\C{// returning pointer to int and int with int parameter}$
 \end{cfa}
 This syntax allows a prototype declaration to be created by cutting and pasting the source text from the function-definition header (or vice versa).
 Like C, it is possible to declare multiple function prototypes in a single declaration, where the return type is distributed across \emph{all} function names in the declaration list.
+This syntax allows a prototype declaration to be created by cutting and pasting source text from the function-definition header (or vice versa).
+Like C, it is possible to declare multiple function-prototypes in a single declaration, where the return type is distributed across \emph{all} function names in the declaration list, \eg:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \lstMakeShortInline@%
 \end{cquote}
 Here, \CFA allows the last function in the list to define its body.
 The syntax for pointers to \CFA functions specifies the pointer name on the right.
+where \CFA allows the last function in the list to define its body.
+The syntax for pointers to \CFA functions specifies the pointer name on the right, \eg:
 \begin{cfa}
 * [ int x ] () fp;                              $\C{// pointer to function returning int with no parameters}$
 …
 * [ * int, int ] ( int ) jp;    $\C{// pointer to function returning pointer to int and int with int parameter}\CRT$
 \end{cfa}
+\newpage
+\noindent
+Note that the name of the function pointer is specified last, as for other variable declarations.
+Finally, new \CFA declarations may appear together with C declarations in the same program block but cannot be mixed within a specific declaration.
+Therefore, a programmer has the option of either continuing to use traditional C declarations or taking advantage of the new style.
+Clearly, both styles need to be supported for some time due to existing C-style header files, particularly for UNIX-like systems.
+Note, the name of the function pointer is specified last, as for other variable declarations.
+Finally, new \CFA declarations may appear together with C declarations in the same program block, but cannot be mixed within a specific declaration.
+Therefore, a programmer has the option of either continuing to use traditional C declarations or take advantage of the new style.
+Clearly, both styles need to be supported for some time due to existing C-style header-files, particularly for UNIX-like systems.
 …
 All variables in C have an \newterm{address}, a \newterm{value}, and a \newterm{type};
 at the position in the program's memory denoted by the address, there exists a sequence of bits (the value), with the length and semantic meaning of this bit sequence defined by the type.
 The C type system does not always track the relationship between a value and its address;
 a value that does not have a corresponding address is called an \newterm{rvalue} (for ``right-hand value''), whereas a value that does have an address is called an \newterm{lvalue} (for ``left-hand value'').
 For example, in @int x; x = 42;@ the variable expression @x@ on the left-hand side of the assignment is an lvalue, whereas the constant expression @42@ on the right-hand side of the assignment is an rvalue.
 Despite the nomenclature of ``left-hand'' and ``right-hand'', an expression's classification as an lvalue or an rvalue is entirely dependent on whether it has an address or not; in imperative programming, the address of a value is used for both reading and writing (mutating) a value, and as such, lvalues can be converted into rvalues and read from, but rvalues cannot be mutated because they lack a location to store the updated value.
+The C type-system does not always track the relationship between a value and its address;
+a value that does not have a corresponding address is called a \newterm{rvalue} (for ``right-hand value''), while a value that does have an address is called a \newterm{lvalue} (for ``left-hand value'').
+For example, in @int x; x = 42;@ the variable expression @x@ on the left-hand-side of the assignment is a lvalue, while the constant expression @42@ on the right-hand-side of the assignment is a rvalue.
+Despite the nomenclature of ``left-hand'' and ``right-hand'', an expression's classification as lvalue or rvalue is entirely dependent on whether it has an address or not; in imperative programming, the address of a value is used for both reading and writing (mutating) a value, and as such, lvalues can be converted to rvalues and read from, but rvalues cannot be mutated because they lack a location to store the updated value.
 Within a lexical scope, lvalue expressions have an \newterm{address interpretation} for writing a value or a \newterm{value interpretation} to read a value.
 For example, in @x = y@, @x@ has an address interpretation, whereas @y@ has a value interpretation.
+For example, in @x = y@, @x@ has an address interpretation, while @y@ has a value interpretation.
 While this duality of interpretation is useful, C lacks a direct mechanism to pass lvalues between contexts, instead relying on \newterm{pointer types} to serve a similar purpose.
 In C, for any type @T@ there is a pointer type @T *@, the value of which is the address of a value of type @T@.
+A pointer rvalue can be explicitly \newterm{dereferenced} to the pointed-to lvalue with the dereference operator @*?@, whereas the rvalue representing the address of an lvalue can be obtained with the address-of operator @&?@.
+A pointer rvalue can be explicitly \newterm{dereferenced} to the pointed-to lvalue with the dereference operator @*?@, while the rvalue representing the address of a lvalue can be obtained with the address-of operator @&?@.
 \begin{cfa}
 int x = 1, y = 2, * p1, * p2, ** p3;
 …
 *p2 = ((*p1 + *p2) * (**p3 - *p1)) / (**p3 - 15);
 \end{cfa}
 Unfortunately, the dereference and address-of operators introduce a great deal of syntactic noise when dealing with pointed-to values rather than pointers, as well as the potential for subtle bugs because of pointer arithmetic.
 For both brevity and clarity, it is desirable for the compiler to figure out how to elide the dereference operators in a complex expression such as the assignment to @*p2@ above.
 However, since C defines a number of forms of \newterm{pointer arithmetic}, two similar expressions involving pointers to arithmetic types (\eg @*p1 + x@ and @p1 + x@) may each have well-defined but distinct semantics, introducing the possibility that a programmer may write one when they mean the other and precluding any simple algorithm for elision of dereference operators.
+However, since C defines a number of forms of \newterm{pointer arithmetic}, two similar expressions involving pointers to arithmetic types (\eg @*p1 + x@ and @p1 + x@) may each have well-defined but distinct semantics, introducing the possibility that a programmer may write one when they mean the other, and precluding any simple algorithm for elision of dereference operators.
 To solve these problems, \CFA introduces reference types @T &@;
+a @T &@ has exactly the same value as a @T *@, but where the @T *@ takes the address interpretation by default, a @T &@ takes the value interpretation by default, as below.
+a @T &@ has exactly the same value as a @T *@, but where the @T *@ takes the address interpretation by default, a @T &@ takes the value interpretation by default, as below:
 \begin{cfa}
 int x = 1, y = 2, & r1, & r2, && r3;
 …
 r2 = ((r1 + r2) * (r3 - r1)) / (r3 - 15);       $\C{// implicit dereferencing}$
 \end{cfa}
 Except for auto-dereferencing by the compiler, this reference example is exactly the same as the previous pointer example.
 Hence, a reference behaves like a variable name---an lvalue expression that is interpreted as a value---but also has the type system track the address of that value.
 One way to conceptualize a reference is via a rewrite rule, where the compiler inserts a dereference operator before the reference variable for each reference qualifier in the reference variable declaration;
+thus, the previous example implicitly acts like the following.
+Hence, a reference behaves like a variable name -- an lvalue expression which is interpreted as a value -- but also has the type system track the address of that value.
+One way to conceptualize a reference is via a rewrite rule, where the compiler inserts a dereference operator before the reference variable for each reference qualifier in the reference variable declaration, so the previous example implicitly acts like:
 \begin{cfa}
 `*`r2 = ((`*`r1 + `*`r2) * (`**`r3 - `*`r1)) / (`**`r3 - 15);
 \end{cfa}
 References in \CFA are similar to those in \CC, with important improvements, which can be seen in the example above.
 Firstly, \CFA does not forbid references to references.
 This provides a much more orthogonal design for library \mbox{implementors}, obviating the need for workarounds such as @std::reference_wrapper@.
+This provides a much more orthogonal design for library implementors, obviating the need for workarounds such as @std::reference_wrapper@.
 Secondly, \CFA references are rebindable, whereas \CC references have a fixed address.
 Rebinding allows \CFA references to be default initialized (\eg to a null pointer\footnote{
 While effort has been made into non-null reference checking in \CC and Java, the exercise seems moot for any nonmanaged languages (C/\CC), given that it only handles one of many different error situations, \eg using a pointer after its storage is deleted.}) and point to different addresses throughout their lifetime, like pointers.
+Rebinding allows \CFA references to be default-initialized (\eg to a null pointer\footnote{
+While effort has been made into non-null reference checking in \CC and Java, the exercise seems moot for any non-managed languages (C/\CC), given that it only handles one of many different error situations, \eg using a pointer after its storage is deleted.}) and point to different addresses throughout their lifetime, like pointers.
 Rebinding is accomplished by extending the existing syntax and semantics of the address-of operator in C.
 In C, the address of an lvalue is always an rvalue, as, in general, that address is not stored anywhere in memory and does not itself have an address.
 In \CFA, the address of a @T &@ is an lvalue @T *@, as the address of the underlying @T@ is stored in the reference and can thus be mutated there.
+In C, the address of a lvalue is always a rvalue, as in general that address is not stored anywhere in memory, and does not itself have an address.
+In \CFA, the address of a @T &@ is a lvalue @T *@, as the address of the underlying @T@ is stored in the reference, and can thus be mutated there.
 The result of this rule is that any reference can be rebound using the existing pointer assignment semantics by assigning a compatible pointer into the address of the reference, \eg @&r1 = &x;@ above.
 This rebinding occurs to an arbitrary depth of reference nesting;
 loosely speaking, nested address-of operators produce a nested lvalue pointer up to the depth of the reference.
 These explicit address-of operators can be thought of as ``cancelling out'' the implicit dereference operators, \eg @(&`*`)r1 = &x@ or @(&(&`*`)`*`)r3 = &(&`*`)r1@ or even @(&`*`)r2 = (&`*`)`*`r3@ for @&r2 = &r3@.
+The precise rules are
+More precisely:
 \begin{itemize}
 \item
+If @R@ is an rvalue of type @T &@$_1\cdots$ @&@$_r$, where $r \ge 1$ references (@&@ symbols), than @&R@ has type @T `*`&@$_{\color{red}2}\cdots$ @&@$_{\color{red}r}$, \ie @T@ pointer with $r-1$ references (@&@ symbols).
+if @R@ is an rvalue of type {@T &@$_1 \cdots$@ &@$_r$} where $r \ge 1$ references (@&@ symbols) then @&R@ has type {@T `*`&@$_{\color{red}2} \cdots$@ &@$_{\color{red}r}$}, \\ \ie @T@ pointer with $r-1$ references (@&@ symbols).
 \item
 If @L@ is an lvalue of type @T &@$_1\cdots$ @&@$_l$, where $l \ge 0$ references (@&@ symbols), than @&L@ has type @T `*`&@$_{\color{red}1}\cdots$ @&@$_{\color{red}l}$, \ie @T@ pointer with $l$ references (@&@ symbols).
+if @L@ is an lvalue of type {@T &@$_1 \cdots$@ &@$_l$} where $l \ge 0$ references (@&@ symbols) then @&L@ has type {@T `*`&@$_{\color{red}1} \cdots$@ &@$_{\color{red}l}$}, \\ \ie @T@ pointer with $l$ references (@&@ symbols).
 \end{itemize}
+Since pointers and references share the same internal representation, code using either is equally performant;
+in fact, the \CFA compiler converts references into pointers internally, and the choice between them is made solely on convenience, \eg many pointer or value accesses.
+Since pointers and references share the same internal representation, code using either is equally performant; in fact the \CFA compiler converts references to pointers internally, and the choice between them is made solely on convenience, \eg many pointer or value accesses.
 By analogy to pointers, \CFA references also allow cv-qualifiers such as @const@:
 …
 There are three initialization contexts in \CFA: declaration initialization, argument/parameter binding, and return/temporary binding.
 In each of these contexts, the address-of operator on the target lvalue is elided.
 The syntactic motivation is clearest when considering overloaded operator assignment, \eg @int ?+=?(int &, int)@; given @int x, y@, the expected call syntax is @x += y@, not @&x += y@.
 More generally, this initialization of references from lvalues rather than pointers is an instance of an ``lvalue-to-reference'' conversion rather than an elision of the address-of operator;
+The syntactic motivation is clearest when considering overloaded operator-assignment, \eg @int ?+=?(int &, int)@; given @int x, y@, the expected call syntax is @x += y@, not @&x += y@.
+More generally, this initialization of references from lvalues rather than pointers is an instance of a ``lvalue-to-reference'' conversion rather than an elision of the address-of operator;
 this conversion is used in any context in \CFA where an implicit conversion is allowed.
 Similarly, use of the value pointed to by a reference in an rvalue context can be thought of as a ``reference-to-rvalue'' conversion, and \CFA also includes a qualifier-adding ``reference-to-reference'' conversion, analogous to the @T *@ to @const T *@ conversion in standard C.
 The final reference conversion included in \CFA is an ``rvalue-to-reference'' conversion, implemented by means of an implicit temporary.
+Similarly, use of a the value pointed to by a reference in an rvalue context can be thought of as a ``reference-to-rvalue'' conversion, and \CFA also includes a qualifier-adding ``reference-to-reference'' conversion, analogous to the @T *@ to @const T *@ conversion in standard C.
+The final reference conversion included in \CFA is ``rvalue-to-reference'' conversion, implemented by means of an implicit temporary.
 When an rvalue is used to initialize a reference, it is instead used to initialize a hidden temporary value with the same lexical scope as the reference, and the reference is initialized to the address of this temporary.
 \begin{cfa}
 …
 f( 3, x + y, (S){ 1.0, 7.0 }, (int [3]){ 1, 2, 3 } ); $\C{// pass rvalue to lvalue \(\Rightarrow\) implicit temporary}$
 \end{cfa}
+This allows complex values to be succinctly and efficiently passed to functions, without the syntactic overhead of the explicit definition of a temporary variable or the runtime cost of pass-by-value.
+\CC allows a similar binding, but only for @const@ references; the more general semantics of \CFA are an attempt to avoid the \newterm{const poisoning} problem~\cite{Taylor10}, in which the addition of a @const@ qualifier to one reference requires a cascading chain of added qualifiers.
+\subsection{Type nesting}
+Nested types provide a mechanism to organize associated types and refactor a subset of members into a named aggregate (\eg subaggregates @name@, @address@, @department@, within aggregate @employe@).
+Java nested types are dynamic (apply to objects), \CC are static (apply to the \lstinline[language=C++]@class@), and C hoists (refactors) nested types into the enclosing scope, which means there is no need for type qualification.
+Since \CFA in not object oriented, adopting dynamic scoping does not make sense;
+instead, \CFA adopts \CC static nesting, using the member-selection operator ``@.@'' for type qualification, as does Java, rather than the \CC type-selection operator ``@::@'' (see Figure~\ref{f:TypeNestingQualification}).
+In the C left example, types @C@, @U@ and @T@ are implicitly hoisted outside of type @S@ into the containing block scope.
+In the \CFA right example, the types are not hoisted and accessible.
+This allows complex values to be succinctly and efficiently passed to functions, without the syntactic overhead of explicit definition of a temporary variable or the runtime cost of pass-by-value.
+\CC allows a similar binding, but only for @const@ references; the more general semantics of \CFA are an attempt to avoid the \newterm{const poisoning} problem~\cite{Taylor10}, in which addition of a @const@ qualifier to one reference requires a cascading chain of added qualifiers.
+\subsection{Type Nesting}
+Nested types provide a mechanism to organize associated types and refactor a subset of members into a named aggregate (\eg sub-aggregates @name@, @address@, @department@, within aggregate @employe@).
+Java nested types are dynamic (apply to objects), \CC are static (apply to the \lstinline[language=C++]@class@), and C hoists (refactors) nested types into the enclosing scope, meaning there is no need for type qualification.
+Since \CFA in not object-oriented, adopting dynamic scoping does not make sense;
+instead \CFA adopts \CC static nesting, using the member-selection operator ``@.@'' for type qualification, as does Java, rather than the \CC type-selection operator ``@::@'' (see Figure~\ref{f:TypeNestingQualification}).
 \begin{figure}
 \centering
-\fontsize{9bp}{11bp}\selectfont\sf
 \lstDeleteShortInline@%
 \begin{tabular}{@{}l@{\hspace{3em}}l|l@{}}
 …
 \end{tabular}
 \lstMakeShortInline@%
 \caption{Type nesting / qualification}
+\caption{Type Nesting / Qualification}
 \label{f:TypeNestingQualification}
-\vspace*{-8pt}
 \end{figure}
+\vspace*{-8pt}
+\subsection{Constructors and destructors}
+In the C left example, types @C@, @U@ and @T@ are implicitly hoisted outside of type @S@ into the containing block scope.
+In the \CFA right example, the types are not hoisted and accessible.
+\subsection{Constructors and Destructors}
 \label{s:ConstructorsDestructors}
 One of the strengths (and weaknesses) of C is memory-management control, allowing resource release to be precisely specified versus unknown release with garbage-collected memory management.
+One of the strengths (and weaknesses) of C is memory-management control, allowing resource release to be precisely specified versus unknown release with garbage-collected memory-management.
 However, this manual approach is verbose, and it is useful to manage resources other than memory (\eg file handles) using the same mechanism as memory.
 \CC addresses these issues using RAII, implemented by means of \newterm{constructor} and \newterm{destructor} functions;
+\CC addresses these issues using Resource Aquisition Is Initialization (RAII), implemented by means of \newterm{constructor} and \newterm{destructor} functions;
 \CFA adopts constructors and destructors (and @finally@) to facilitate RAII.
 While constructors and destructors are a common feature of object-oriented programming languages, they are an independent capability allowing \CFA to adopt them while retaining a procedural paradigm.
 Specifically, \CFA constructors and destructors are denoted by name and first parameter type versus name and nesting in an aggregate type.
+While constructors and destructors are a common feature of object-oriented programming-languages, they are an independent capability allowing \CFA to adopt them while retaining a procedural paradigm.
+Specifically, \CFA constructors and destructors are denoted by name and first parameter-type versus name and nesting in an aggregate type.
 Constructor calls seamlessly integrate with existing C initialization syntax, providing a simple and familiar syntax to C programmers and allowing constructor calls to be inserted into legacy C code with minimal code changes.
 …
 The constructor and destructor have return type @void@, and the first parameter is a reference to the object type to be constructed or destructed.
 While the first parameter is informally called the @this@ parameter, as in object-oriented languages, any variable name may be used.
 Both constructors and destructors allow additional parameters after the @this@ parameter for specifying values for initialization/deinitialization\footnote{
 Destruction parameters are useful for specifying storage-management actions, such as deinitialize but not deallocate.}.
 \begin{cfa}[basicstyle=\linespread{0.9}\fontsize{9bp}{11bp}\selectfont\sf]
+Both constructors and destructors allow additional parameters after the @this@ parameter for specifying values for initialization/de-initialization\footnote{
+Destruction parameters are useful for specifying storage-management actions, such as de-initialize but not deallocate.}.
+\begin{cfa}
 struct VLA { int size, * data; };                       $\C{// variable length array of integers}$
 void ?{}( VLA & vla ) with ( vla ) { size = 10;  data = alloc( size ); }  $\C{// default constructor}$
 …
 \end{cfa}
 @VLA@ is a \newterm{managed type}\footnote{
 A managed type affects the runtime environment versus a self-contained type.}: a type requiring a nontrivial constructor or destructor, or with a member of a managed type.
+A managed type affects the runtime environment versus a self-contained type.}: a type requiring a non-trivial constructor or destructor, or with a member of a managed type.
 A managed type is implicitly constructed at allocation and destructed at deallocation to ensure proper interaction with runtime resources, in this case, the @data@ array in the heap.
 For details of the code-generation placement of implicit constructor and destructor calls among complex executable statements, see section~2.2 in the work of Schlintz~\cite{Schluntz17}.
 \CFA also provides syntax for \newterm{initialization} and \newterm{copy}.
+For details of the code-generation placement of implicit constructor and destructor calls among complex executable statements see~\cite[\S~2.2]{Schluntz17}.
+\CFA also provides syntax for \newterm{initialization} and \newterm{copy}:
 \begin{cfa}
 void ?{}( VLA & vla, int size, char fill = '\0' ) {  $\C{// initialization}$
 …
+}
 \end{cfa}
 (Note that the example is purposely simplified using shallow-copy semantics.)
 An initialization constructor call has the same syntax as a C initializer, except that the initialization values are passed as arguments to a matching constructor (number and type of parameters).
+(Note, the example is purposely simplified using shallow-copy semantics.)
+An initialization constructor-call has the same syntax as a C initializer, except the initialization values are passed as arguments to a matching constructor (number and type of paremeters).
 \begin{cfa}
 VLA va = `{` 20, 0 `}`,  * arr = alloc()`{` 5, 0 `}`;
 \end{cfa}
 Note the use of a \newterm{constructor expression} to initialize the storage from the dynamic storage allocation.
+Note, the use of a \newterm{constructor expression} to initialize the storage from the dynamic storage-allocation.
 Like \CC, the copy constructor has two parameters, the second of which is a value parameter with the same type as the first parameter;
 appropriate care is taken to not recursively call the copy constructor when initializing the second parameter.
 …
 \CFA constructors may be explicitly called, like Java, and destructors may be explicitly called, like \CC.
 Explicit calls to constructors double as a \CC-style \emph{placement syntax}, useful for construction of members in user-defined constructors and reuse of existing storage allocations.
 Like the other operators in \CFA, there is a concise syntax for constructor/destructor function calls.
+Like the other operators in \CFA, there is a concise syntax for constructor/destructor function calls:
 \begin{cfa}
+{
 …
 To provide a uniform type interface for @otype@ polymorphism, the \CFA compiler automatically generates a default constructor, copy constructor, assignment operator, and destructor for all types.
 These default functions can be overridden by user-generated versions.
 For compatibility with the standard behavior of C, the default constructor and destructor for all basic, pointer, and reference types do nothing, whereas the copy constructor and assignment operator are bitwise copies;
 if default zero initialization is desired, the default constructors can be overridden.
+For compatibility with the standard behaviour of C, the default constructor and destructor for all basic, pointer, and reference types do nothing, while the copy constructor and assignment operator are bitwise copies;
+if default zero-initialization is desired, the default constructors can be overridden.
 For user-generated types, the four functions are also automatically generated.
 @enum@ types are handled the same as their underlying integral type, and unions are also bitwise copied and no-op initialized and destructed.
 For compatibility with C, a copy constructor from the first union member type is also defined.
 For @struct@ types, each of the four functions is implicitly defined to call their corresponding functions on each member of the struct.
 To better simulate the behavior of C initializers, a set of \newterm{member constructors} is also generated for structures.
 A constructor is generated for each nonempty prefix of a structure's member list to copy-construct the members passed as parameters and default-construct the remaining members.
+For @struct@ types, each of the four functions are implicitly defined to call their corresponding functions on each member of the struct.
+To better simulate the behaviour of C initializers, a set of \newterm{member constructors} is also generated for structures.
+A constructor is generated for each non-empty prefix of a structure's member-list to copy-construct the members passed as parameters and default-construct the remaining members.
 To allow users to limit the set of constructors available for a type, when a user declares any constructor or destructor, the corresponding generated function and all member constructors for that type are hidden from expression resolution;
 similarly, the generated default constructor is hidden upon the declaration of any constructor.
+similarly, the generated default constructor is hidden upon declaration of any constructor.
 These semantics closely mirror the rule for implicit declaration of constructors in \CC\cite[p.~186]{ANSI98:C++}.
+In some circumstance, programmers may not wish to have implicit constructor and destructor generation and calls.
+In these cases, \CFA provides the initialization syntax \lstinline|S x `@=` {}|, and the object becomes unmanaged;
+hence, implicit \mbox{constructor} and destructor calls are not generated.
+In some circumstance programmers may not wish to have implicit constructor and destructor generation and calls.
+In these cases, \CFA provides the initialization syntax \lstinline|S x `@=` {}|, and the object becomes unmanaged, so implicit constructor and destructor calls are not generated.
 Any C initializer can be the right-hand side of an \lstinline|@=| initializer, \eg \lstinline|VLA a @= { 0, 0x0 }|, with the usual C initialization semantics.
 The same syntax can be used in a compound literal, \eg \lstinline|a = (VLA)`@`{ 0, 0x0 }|, to create a C-style literal.
 The point of \lstinline|@=| is to provide a migration path from legacy C code to \CFA, by providing a mechanism to incrementally convert into implicit initialization.
+The point of \lstinline|@=| is to provide a migration path from legacy C code to \CFA, by providing a mechanism to incrementally convert to implicit initialization.
 …
 \section{Literals}
 C already includes limited polymorphism for literals---@0@ can be either an integer or a pointer literal, depending on context, whereas the syntactic forms of literals of the various integer and float types are very similar, differing from each other only in suffix.
 In keeping with the general \CFA approach of adding features while respecting the ``C style'' of doing things, C's polymorphic constants and typed literal syntax are extended to interoperate with user-defined types, while maintaining a backward-compatible semantics.
+C already includes limited polymorphism for literals -- @0@ can be either an integer or a pointer literal, depending on context, while the syntactic forms of literals of the various integer and float types are very similar, differing from each other only in suffix.
+In keeping with the general \CFA approach of adding features while respecting the ``C-style'' of doing things, C's polymorphic constants and typed literal syntax are extended to interoperate with user-defined types, while maintaining a backwards-compatible semantics.
 A simple example is allowing the underscore, as in Ada, to separate prefixes, digits, and suffixes in all \CFA constants, \eg @0x`_`1.ffff`_`ffff`_`p`_`128`_`l@, where the underscore is also the standard separator in C identifiers.
 \CC uses a single quote as a separator, but it is restricted among digits, precluding its use in the literal prefix or suffix, \eg @0x1.ffff@@`'@@ffffp128l@, and causes problems with most integrated development environments (IDEs), which must be extended to deal with this alternate use of the single quote.
+\CC uses a single quote as a separator but it is restricted among digits, precluding its use in the literal prefix or suffix, \eg @0x1.ffff@@`'@@ffffp128l@, and causes problems with most IDEs, which must be extended to deal with this alternate use of the single quote.
 …
 In C, @0@ has the special property that it is the only ``false'' value;
 by the standard, any value that compares equal to @0@ is false, whereas any value that compares unequal to @0@ is true.
 As such, an expression @x@ in any Boolean context (such as the condition of an @if@ or @while@ statement, or the arguments to @&&@, @||@, or @?:@\,) can be rewritten as @x != 0@ without changing its semantics.
+by the standard, any value that compares equal to @0@ is false, while any value that compares unequal to @0@ is true.
+As such, an expression @x@ in any boolean context (such as the condition of an @if@ or @while@ statement, or the arguments to @&&@, @||@, or @?:@\,) can be rewritten as @x != 0@ without changing its semantics.
 Operator overloading in \CFA provides a natural means to implement this truth-value comparison for arbitrary types, but the C type system is not precise enough to distinguish an equality comparison with @0@ from an equality comparison with an arbitrary integer or pointer.
 To provide this precision, \CFA introduces a new type @zero_t@ as the type of literal @0@ (somewhat analagous to @nullptr_t@ and @nullptr@ in \CCeleven);
 …
 With this addition, \CFA rewrites @if (x)@ and similar expressions to @if ( (x) != 0 )@ or the appropriate analogue, and any type @T@ is ``truthy'' by defining an operator overload @int ?!=?( T, zero_t )@.
 \CC makes types truthy by adding a conversion to @bool@;
 prior to the addition of explicit cast operators in \CCeleven, this approach had the pitfall of making truthy types transitively convertible into any numeric type;
+prior to the addition of explicit cast operators in \CCeleven, this approach had the pitfall of making truthy types transitively convertable to any numeric type;
 \CFA avoids this issue.
 …
 \subsection{User literals}
+\subsection{User Literals}
 For readability, it is useful to associate units to scale literals, \eg weight (stone, pound, kilogram) or time (seconds, minutes, hours).
 The left of Figure~\ref{f:UserLiteral} shows the \CFA alternative call syntax (postfix: literal argument before function name), using the backquote, to convert basic literals into user literals.
+The left of Figure~\ref{f:UserLiteral} shows the \CFA alternative call-syntax (postfix: literal argument before function name), using the backquote, to convert basic literals into user literals.
 The backquote is a small character, making the unit (function name) predominate.
 For examples, the multiprecision integer type in Section~\ref{s:MultiPrecisionIntegers} has the following user literals.
+For examples, the multi-precision integer-type in Section~\ref{s:MultiPrecisionIntegers} has user literals:
 {\lstset{language=CFA,moredelim=**[is][\color{red}]{|}{|},deletedelim=**[is][]{`}{`}}
 \begin{cfa}
 …
 y = "12345678901234567890123456789"|`mp| + "12345678901234567890123456789"|`mp|;
 \end{cfa}
 Because \CFA uses a standard function, all types and literals are applicable, as well as overloading and conversions, where @?`@ denotes a postfix-function name and @`@  denotes a postfix-function call.
+Because \CFA uses a standard function, all types and literals are applicable, as well as overloading and conversions, where @?`@ denotes a postfix-function name and @`@ denotes a postfix-function call.
 }%
 \begin{cquote}
 …
 \end{cquote}
 The right of Figure~\ref{f:UserLiteral} shows the equivalent \CC version using the underscore for the call syntax.
+The right of Figure~\ref{f:UserLiteral} shows the equivalent \CC version using the underscore for the call-syntax.
 However, \CC restricts the types, \eg @unsigned long long int@ and @long double@ to represent integral and floating literals.
 After which, user literals must match (no conversions);
 …
 \begin{figure}
 \centering
-\fontsize{9bp}{11bp}\selectfont
 \lstset{language=CFA,moredelim=**[is][\color{red}]{|}{|},deletedelim=**[is][]{`}{`}}
 \lstDeleteShortInline@%
 …
 \end{tabular}
 \lstMakeShortInline@%
 \caption{User literal}
+\caption{User Literal}
 \label{f:UserLiteral}
 \end{figure}
 …
 \label{sec:libraries}
 As stated in Section~\ref{sec:poly-fns}, \CFA inherits a large corpus of library code, where other programming languages must rewrite or provide fragile interlanguage communication with C.
+As stated in Section~\ref{sec:poly-fns}, \CFA inherits a large corpus of library code, where other programming languages must rewrite or provide fragile inter-language communication with C.
 \CFA has replacement libraries condensing hundreds of existing C names into tens of \CFA overloaded names, all without rewriting the actual computations.
 In many cases, the interface is an inline wrapper providing overloading during compilation but of zero cost at runtime.
+In many cases, the interface is an inline wrapper providing overloading during compilation but zero cost at runtime.
 The following sections give a glimpse of the interface reduction to many C libraries.
 In many cases, @signed@/@unsigned@ @char@, @short@, and @_Complex@ functions are available (but not shown) to ensure expression computations remain in a single type, as conversions can distort results.
 …
 C library @limits.h@ provides lower and upper bound constants for the basic types.
 \CFA name overloading is used to condense these typed constants.
+\CFA name overloading is used to condense these typed constants, \eg:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \lstMakeShortInline@%
 \end{cquote}
 The result is a significant reduction in names to access typed constants.
+The result is a significant reduction in names to access typed constants, \eg:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 C library @math.h@ provides many mathematical functions.
 \CFA function overloading is used to condense these mathematical functions.
+\CFA function overloading is used to condense these mathematical functions, \eg:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \lstMakeShortInline@%
 \end{cquote}
 The result is a significant reduction in names to access math functions.
+The result is a significant reduction in names to access math functions, \eg:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \lstMakeShortInline@%
 \end{cquote}
 While \Celeven has type-generic math (see section~7.25 of the ISO/IEC 9899\cite{C11}) in @tgmath.h@ to provide a similar mechanism, these macros are limited, matching a function name with a single set of floating type(s).
+While \Celeven has type-generic math~\cite[\S~7.25]{C11} in @tgmath.h@ to provide a similar mechanism, these macros are limited, matching a function name with a single set of floating type(s).
 For example, it is impossible to overload @atan@ for both one and two arguments;
 instead, the names @atan@ and @atan2@ are required (see Section~\ref{s:NameOverloading}).
 The key observation is that only a restricted set of type-generic macros is provided for a limited set of function names, which do not generalize across the type system, as in \CFA.
+instead the names @atan@ and @atan2@ are required (see Section~\ref{s:NameOverloading}).
+The key observation is that only a restricted set of type-generic macros are provided for a limited set of function names, which do not generalize across the type system, as in \CFA.
 …
 C library @stdlib.h@ provides many general functions.
 \CFA function overloading is used to condense these utility functions.
+\CFA function overloading is used to condense these utility functions, \eg:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \lstMakeShortInline@%
 \end{cquote}
 The result is a significant reduction in names to access the utility functions.
+The result is a significant reduction in names to access utility functions, \eg:
 \begin{cquote}
 \lstDeleteShortInline@%
 …
 \lstMakeShortInline@%
 \end{cquote}
 In addition, there are polymorphic functions, like @min@ and @max@, that work on any type with operator @?<?@ or @?>?@.
+In additon, there are polymorphic functions, like @min@ and @max@, that work on any type with operators @?<?@ or @?>?@.
 The following shows one example where \CFA \emph{extends} an existing standard C interface to reduce complexity and provide safety.
 C/\Celeven provide a number of complex and overlapping storage-management operations to support the following capabilities.
 \begin{list}{}{\itemsep=0pt\parsep=0pt\labelwidth=0pt\leftmargin\parindent\itemindent-\leftmargin\let\makelabel\descriptionlabel}
+C/\Celeven provide a number of complex and overlapping storage-management operation to support the following capabilities:
+\begin{description}%[topsep=3pt,itemsep=2pt,parsep=0pt]
 \item[fill]
 an allocation with a specified character.
 \item[resize]
 an existing allocation to decrease or increase its size.
 In either case, new storage may or may not be allocated, and if there is a new allocation, as much data from the existing allocation are copied.
+In either case, new storage may or may not be allocated and, if there is a new allocation, as much data from the existing allocation is copied.
 For an increase in storage size, new storage after the copied data may be filled.
-\newpage
 \item[align]
 an allocation on a specified memory boundary, \eg, an address multiple of 64 or 128 for cache-line purposes.
 …
 allocation with a specified number of elements.
 An array may be filled, resized, or aligned.
 \end{list}
 Table~\ref{t:StorageManagementOperations} shows the capabilities provided by C/\Celeven allocation functions and how all the capabilities can be combined into two \CFA functions.
 \CFA storage-management functions extend the C equivalents by overloading, providing shallow type safety, and removing the need to specify the base allocation size.
 Figure~\ref{f:StorageAllocation} contrasts \CFA and C storage allocation performing the same operations with the same type safety.
+\end{description}
+Table~\ref{t:StorageManagementOperations} shows the capabilities provided by C/\Celeven allocation-functions and how all the capabilities can be combined into two \CFA functions.
+\CFA storage-management functions extend the C equivalents by overloading, providing shallow type-safety, and removing the need to specify the base allocation-size.
+Figure~\ref{f:StorageAllocation} contrasts \CFA and C storage-allocation performing the same operations with the same type safety.
 \begin{table}
 \caption{Storage-management operations}
+\caption{Storage-Management Operations}
 \label{t:StorageManagementOperations}
 \centering
 \lstDeleteShortInline@%
 \lstMakeShortInline~%
+\begin{tabular}{@{}rrllll@{}}
+\multicolumn{1}{c}{}&           & \multicolumn{1}{c}{fill}      & resize        & align & array \\
+\begin{tabular}{@{}r|r|l|l|l|l@{}}
+\multicolumn{1}{c}{}&           & \multicolumn{1}{c|}{fill}     & resize        & align & array \\
+\hline
 C               & ~malloc~                      & no                    & no            & no            & no    \\
                 & ~calloc~                      & yes (0 only)  & no            & no            & yes   \\
 …
                 & ~memalign~            & no                    & no            & yes           & no    \\
                 & ~posix_memalign~      & no                    & no            & yes           & no    \\
+\hline
 C11             & ~aligned_alloc~       & no                    & no            & yes           & no    \\
+\hline
 \CFA    & ~alloc~                       & yes/copy              & no/yes        & no            & yes   \\
                 & ~align_alloc~         & yes                   & no            & yes           & yes   \\
 …
 \begin{figure}
 \centering
-\fontsize{9bp}{11bp}\selectfont
 \begin{cfa}[aboveskip=0pt,xleftmargin=0pt]
 size_t  dim = 10;                                                       $\C{// array dimension}$
 …
 \end{tabular}
 \lstMakeShortInline@%
 \caption{\CFA versus C storage allocation}
+\caption{\CFA versus C Storage-Allocation}
 \label{f:StorageAllocation}
 \end{figure}
 Variadic @new@ (see Section~\ref{sec:variadic-tuples}) cannot support the same overloading because extra parameters are for initialization.
 Hence, there are @new@ and @anew@ functions for single and array variables, and the fill value is the arguments to the constructor.
+Hence, there are @new@ and @anew@ functions for single and array variables, and the fill value is the arguments to the constructor, \eg:
 \begin{cfa}
 struct S { int i, j; };
 …
 S * as = anew( dim, 2, 3 );                                     $\C{// each array element initialized to 2, 3}$
 \end{cfa}
 Note that \CC can only initialize array elements via the default constructor.
 Finally, the \CFA memory allocator has \newterm{sticky properties} for dynamic storage: fill and alignment are remembered with an object's storage in the heap.
+Note, \CC can only initialize array elements via the default constructor.
+Finally, the \CFA memory-allocator has \newterm{sticky properties} for dynamic storage: fill and alignment are remembered with an object's storage in the heap.
 When a @realloc@ is performed, the sticky properties are respected, so that new storage is correctly aligned and initialized with the fill character.
 …
 \label{s:IOLibrary}
 The goal of \CFA I/O is to simplify the common cases, while fully supporting polymorphism and user-defined types in a consistent way.
+The goal of \CFA I/O is to simplify the common cases, while fully supporting polymorphism and user defined types in a consistent way.
 The approach combines ideas from \CC and Python.
 The \CFA header file for the I/O library is @fstream@.
 …
 \lstMakeShortInline@%
 \end{cquote}
 The \CFA form has half the characters of the \CC form and is similar to Python I/O with respect to implicit separators.
+The \CFA form has half the characters of the \CC form, and is similar to Python I/O with respect to implicit separators.
 Similar simplification occurs for tuple I/O, which prints all tuple values separated by ``\lstinline[showspaces=true]@, @''.
 \begin{cfa}
 …
 \lstMakeShortInline@%
 \end{cquote}
 There is a weak similarity between the \CFA logical-or operator and the Shell pipe operator for moving data, where data flow in the correct direction for input but in the opposite direction for output.
+There is a weak similarity between the \CFA logical-or operator and the Shell pipe-operator for moving data, where data flows in the correct direction for input but the opposite direction for output.
 \begin{comment}
 The implicit separator character (space/blank) is a separator not a terminator.
 …
 \end{itemize}
 \end{comment}
 There are functions to set and get the separator string and manipulators to toggle separation on and off in the middle of output.
 \subsection{Multiprecision integers}
+There are functions to set and get the separator string, and manipulators to toggle separation on and off in the middle of output.
+\subsection{Multi-precision Integers}
 \label{s:MultiPrecisionIntegers}
 \CFA has an interface to the GNU multiple precision (GMP) signed integers~\cite{GMP}, similar to the \CC interface provided by GMP.
 The \CFA interface wraps GMP functions into operator functions to make programming with multiprecision integers identical to using fixed-sized integers.
 The \CFA type name for multiprecision signed integers is @Int@ and the header file is @gmp@.
 Figure~\ref{f:GMPInterface} shows a multiprecision factorial program contrasting the GMP interface in \CFA and C.
 \begin{figure}[b]
+\CFA has an interface to the GMP multi-precision signed-integers~\cite{GMP}, similar to the \CC interface provided by GMP.
+The \CFA interface wraps GMP functions into operator functions to make programming with multi-precision integers identical to using fixed-sized integers.
+The \CFA type name for multi-precision signed-integers is @Int@ and the header file is @gmp@.
+Figure~\ref{f:GMPInterface} shows a multi-precision factorial-program contrasting the GMP interface in \CFA and C.
+\begin{figure}
 \centering
-\fontsize{9bp}{11bp}\selectfont
 \lstDeleteShortInline@%
 \begin{tabular}{@{}l@{\hspace{3\parindentlnth}}l@{}}
 …
 \end{tabular}
 \lstMakeShortInline@%
 \caption{GMP interface \CFA versus C}
+\caption{GMP Interface \CFA versus C}
 \label{f:GMPInterface}
 \end{figure}
-\vspace{-4pt}
 \section{Polymorphism Evaluation}
 \label{sec:eval}
 …
 % Though \CFA provides significant added functionality over C, these features have a low runtime penalty.
 % In fact, it is shown that \CFA's generic programming can enable faster runtime execution than idiomatic @void *@-based C code.
 The experiment is a set of generic-stack microbenchmarks~\cite{CFAStackEvaluation} in C, \CFA, and \CC (see implementations in Appendix~\ref{sec:BenchmarkStackImplementations}).
+The experiment is a set of generic-stack micro-benchmarks~\cite{CFAStackEvaluation} in C, \CFA, and \CC (see implementations in Appendix~\ref{sec:BenchmarkStackImplementations}).
 Since all these languages share a subset essentially comprising standard C, maximal-performance benchmarks should show little runtime variance, differing only in length and clarity of source code.
 A more illustrative comparison measures the costs of idiomatic usage of each language's features.
 Figure~\ref{fig:BenchmarkTest} shows the \CFA benchmark tests for a generic stack based on a singly linked list.
+Figure~\ref{fig:BenchmarkTest} shows the \CFA benchmark tests for a generic stack based on a singly linked-list.
 The benchmark test is similar for the other languages.
 The experiment uses element types @int@ and @pair(short, char)@, and pushes $N=40M$ elements on a generic stack, copies the stack, clears one of the stacks, and finds the maximum value in the other stack.
 \begin{figure}
-\fontsize{9bp}{11bp}\selectfont
 \begin{cfa}[xleftmargin=3\parindentlnth,aboveskip=0pt,belowskip=0pt]
 int main() {
 …
+}
 \end{cfa}
 \caption{\protect\CFA benchmark test}
+\caption{\protect\CFA Benchmark Test}
 \label{fig:BenchmarkTest}
-\vspace*{-10pt}
 \end{figure}
 The structure of each benchmark implemented is C with @void *@-based polymorphism, \CFA with parametric polymorphism, \CC with templates, and \CC using only class inheritance for polymorphism, called \CCV.
+The structure of each benchmark implemented is: C with @void *@-based polymorphism, \CFA with parametric polymorphism, \CC with templates, and \CC using only class inheritance for polymorphism, called \CCV.
 The \CCV variant illustrates an alternative object-oriented idiom where all objects inherit from a base @object@ class, mimicking a Java-like interface;
 hence, runtime checks are necessary to safely downcast objects.
 The most notable difference among the implementations is in memory layout of generic types: \CFA and \CC inline the stack and pair elements into corresponding list and pair nodes, whereas C and \CCV lack such capability and, instead, must store generic objects via pointers to separately allocated objects.
 Note that the C benchmark uses unchecked casts as C has no runtime mechanism to perform such checks, whereas \CFA and \CC provide type safety statically.
+hence runtime checks are necessary to safely down-cast objects.
+The most notable difference among the implementations is in memory layout of generic types: \CFA and \CC inline the stack and pair elements into corresponding list and pair nodes, while C and \CCV lack such a capability and instead must store generic objects via pointers to separately-allocated objects.
+Note, the C benchmark uses unchecked casts as C has no runtime mechanism to perform such checks, while \CFA and \CC provide type-safety statically.
 Figure~\ref{fig:eval} and Table~\ref{tab:eval} show the results of running the benchmark in Figure~\ref{fig:BenchmarkTest} and its C, \CC, and \CCV equivalents.
 The graph plots the median of five consecutive runs of each program, with an initial warm-up run omitted.
+The graph plots the median of 5 consecutive runs of each program, with an initial warm-up run omitted.
 All code is compiled at \texttt{-O2} by gcc or g++ 6.4.0, with all \CC code compiled as \CCfourteen.
 The benchmarks are run on an Ubuntu 16.04 workstation with 16 GB of RAM and a 6-core AMD FX-6300 CPU with 3.5 GHz maximum clock frequency.
 …
 \begin{figure}
 \centering
 \resizebox{0.7\textwidth}{!}{\input{timing}}
 \caption{Benchmark timing results (smaller is better)}
+\input{timing}
+\caption{Benchmark Timing Results (smaller is better)}
 \label{fig:eval}
-\vspace*{-10pt}
 \end{figure}
 \begin{table}
-\vspace*{-10pt}
 \caption{Properties of benchmark code}
 \label{tab:eval}
 \centering
-\vspace*{-4pt}
 \newcommand{\CT}[1]{\multicolumn{1}{c}{#1}}
 \begin{tabular}{lrrrr}
                                                                         & \CT{C}        & \CT{\CFA}     & \CT{\CC}      & \CT{\CCV}             \\
 maximum memory usage (MB)                       & 10\,001       & 2\,502        & 2\,503        & 11\,253               \\
+\begin{tabular}{rrrrr}
+                                                                        & \CT{C}        & \CT{\CFA}     & \CT{\CC}      & \CT{\CCV}             \\ \hline
+maximum memory usage (MB)                       & 10,001        & 2,502         & 2,503         & 11,253                \\
 source code size (lines)                        & 201           & 191           & 125           & 294                   \\
 redundant type annotations (lines)      & 27            & 0                     & 2                     & 16                    \\
 binary size (KB)                                        & 14            & 257           & 14            & 37                    \\
 \end{tabular}
-\vspace*{-16pt}
 \end{table}
+\enlargethispage{-10pt}
+The C and \CCV variants are generally the slowest with the largest memory footprint, due to their less-efficient memory layout and the pointer indirection necessary to implement generic types;
+The C and \CCV variants are generally the slowest with the largest memory footprint, because of their less-efficient memory layout and the pointer-indirection necessary to implement generic types;
 this inefficiency is exacerbated by the second level of generic types in the pair benchmarks.
 By contrast, the \CFA and \CC variants run in roughly equivalent time for both the integer and pair because of the equivalent storage layout, with the inlined libraries (\ie no separate compilation) and greater maturity of the \CC compiler contributing to its lead.
 \CCV is slower than C largely due to the cost of runtime type checking of downcasts (implemented with @dynamic_cast@).
+By contrast, the \CFA and \CC variants run in roughly equivalent time for both the integer and pair because of equivalent storage layout, with the inlined libraries (\ie no separate compilation) and greater maturity of the \CC compiler contributing to its lead.
+\CCV is slower than C largely due to the cost of runtime type-checking of down-casts (implemented with @dynamic_cast@);
 The outlier for \CFA, pop @pair@, results from the complexity of the generated-C polymorphic code.
 The gcc compiler is unable to optimize some dead code and condense nested calls;
 …
 Finally, the binary size for \CFA is larger because of static linking with the \CFA libraries.
 \CFA is also competitive in terms of source code size, measured as a proxy for programmer effort. The line counts in Table~\ref{tab:eval} include implementations of @pair@ and @stack@ types for all four languages for purposes of direct comparison, although it should be noted that \CFA and \CC have prewritten data structures in their standard libraries that programmers would generally use instead. Use of these standard library types has minimal impact on the performance benchmarks, but shrinks the \CFA and \CC benchmarks to 39 and 42 lines, respectively.
+\CFA is also competitive in terms of source code size, measured as a proxy for programmer effort. The line counts in Table~\ref{tab:eval} include implementations of @pair@ and @stack@ types for all four languages for purposes of direct comparison, though it should be noted that \CFA and \CC have pre-written data structures in their standard libraries that programmers would generally use instead. Use of these standard library types has minimal impact on the performance benchmarks, but shrinks the \CFA and \CC benchmarks to 39 and 42 lines, respectively.
 The difference between the \CFA and \CC line counts is primarily declaration duplication to implement separate compilation; a header-only \CFA library would be similar in length to the \CC version.
 On the other hand, C does not have a generic collections library in its standard distribution, resulting in frequent reimplementation of such collection types by C programmers.
 \CCV does not use the \CC standard template library by construction and, in fact, includes the definition of @object@ and wrapper classes for @char@, @short@, and @int@ in its line count, which inflates this count somewhat, as an actual object-oriented language would include these in the standard library;
+On the other hand, C does not have a generic collections-library in its standard distribution, resulting in frequent reimplementation of such collection types by C programmers.
+\CCV does not use the \CC standard template library by construction, and in fact includes the definition of @object@ and wrapper classes for @char@, @short@, and @int@ in its line count, which inflates this count somewhat, as an actual object-oriented language would include these in the standard library;
 with their omission, the \CCV line count is similar to C.
 We justify the given line count by noting that many object-oriented languages do not allow implementing new interfaces on library types without subclassing or wrapper types, which may be similarly verbose.
 Line count is a fairly rough measure of code complexity;
 another important factor is how much type information the programmer must specify manually, especially where that information is not compiler checked.
 Such unchecked type information produces a heavier documentation burden and increased potential for runtime bugs and is much less common in \CFA than C, with its manually specified function pointer arguments and format codes, or \CCV, with its extensive use of un-type-checked downcasts, \eg @object@ to @integer@ when popping a stack.
+Line-count is a fairly rough measure of code complexity;
+another important factor is how much type information the programmer must specify manually, especially where that information is not compiler-checked.
+Such unchecked type information produces a heavier documentation burden and increased potential for runtime bugs, and is much less common in \CFA than C, with its manually specified function pointer arguments and format codes, or \CCV, with its extensive use of un-type-checked downcasts, \eg @object@ to @integer@ when popping a stack.
 To quantify this manual typing, the ``redundant type annotations'' line in Table~\ref{tab:eval} counts the number of lines on which the type of a known variable is respecified, either as a format specifier, explicit downcast, type-specific function, or by name in a @sizeof@, struct literal, or @new@ expression.
 The \CC benchmark uses two redundant type annotations to create a new stack nodes, whereas the C and \CCV benchmarks have several such annotations spread throughout their code.
+The \CC benchmark uses two redundant type annotations to create a new stack nodes, while the C and \CCV benchmarks have several such annotations spread throughout their code.
 The \CFA benchmark is able to eliminate all redundant type annotations through use of the polymorphic @alloc@ function discussed in Section~\ref{sec:libraries}.
+We conjecture that these results scale across most generic data types as the underlying polymorphism implement is constant.
+\vspace*{-8pt}
+We conjecture these results scale across most generic data-types as the underlying polymorphism implement is constant.
 \section{Related Work}
 \label{s:RelatedWork}
 …
 \CC provides three disjoint polymorphic extensions to C: overloading, inheritance, and templates.
 The overloading is restricted because resolution does not use the return type, inheritance requires learning object-oriented programming and coping with a restricted nominal-inheritance hierarchy, templates cannot be separately compiled resulting in compilation/code bloat and poor error messages, and determining how these mechanisms interact and which to use is confusing.
 In contrast, \CFA has a single facility for polymorphic code supporting type-safe separate compilation of polymorphic functions and generic (opaque) types, which uniformly leverage the C procedural paradigm.
+In contrast, \CFA has a single facility for polymorphic code supporting type-safe separate-compilation of polymorphic functions and generic (opaque) types, which uniformly leverage the C procedural paradigm.
 The key mechanism to support separate compilation is \CFA's \emph{explicit} use of assumed type properties.
 Until \CC concepts~\cite{C++Concepts} are standardized (anticipated for \CCtwenty), \CC provides no way of specifying the requirements of a generic function beyond compilation errors during template expansion;
+Until \CC concepts~\cite{C++Concepts} are standardized (anticipated for \CCtwenty), \CC provides no way to specify the requirements of a generic function beyond compilation errors during template expansion;
 furthermore, \CC concepts are restricted to template polymorphism.
 Cyclone~\cite{Grossman06} also provides capabilities for polymorphic functions and existential types, similar to \CFA's @forall@ functions and generic types.
 Cyclone existential types can include function pointers in a construct similar to a virtual function table, but these pointers must be explicitly initialized at some point in the code, which is a tedious and potentially error-prone process.
+Cyclone existential types can include function pointers in a construct similar to a virtual function-table, but these pointers must be explicitly initialized at some point in the code, a tedious and potentially error-prone process.
 Furthermore, Cyclone's polymorphic functions and types are restricted to abstraction over types with the same layout and calling convention as @void *@, \ie only pointer types and @int@.
 In \CFA terms, all Cyclone polymorphism must be dtype-static.
 While the Cyclone design provides the efficiency benefits discussed in Section~\ref{sec:generic-apps} for dtype-static polymorphism, it is more restrictive than \CFA's general model.
+Smith and Volpano~\cite{Smith98} present Polymorphic C, an ML dialect with polymorphic functions, C-like syntax, and pointer types;
+it lacks many of C's features, most notably structure types, and hence, is not a practical C replacement.
+Smith and Volpano~\cite{Smith98} present Polymorphic C, an ML dialect with polymorphic functions, C-like syntax, and pointer types; it lacks many of C's features, however, most notably structure types, and so is not a practical C replacement.
 Objective-C~\cite{obj-c-book} is an industrially successful extension to C.
 However, Objective-C is a radical departure from C, using an object-oriented model with message passing.
+However, Objective-C is a radical departure from C, using an object-oriented model with message-passing.
 Objective-C did not support type-checked generics until recently \cite{xcode7}, historically using less-efficient runtime checking of object types.
 The GObject~\cite{GObject} framework also adds object-oriented programming with runtime type-checking and reference-counting garbage collection to C;
 these features are more intrusive additions than those provided by \CFA, in addition to the runtime overhead of reference counting.
 Vala~\cite{Vala} compiles to GObject-based C, adding the burden of learning a separate language syntax to the aforementioned demerits of GObject as a modernization path for existing C code bases.
 Java~\cite{Java8} included generic types in Java~5, which are type checked at compilation and type erased at runtime, similar to \CFA's.
 However, in Java, each object carries its own table of method pointers, whereas \CFA passes the method pointers separately to maintain a C-compatible layout.
+The GObject~\cite{GObject} framework also adds object-oriented programming with runtime type-checking and reference-counting garbage-collection to C;
+these features are more intrusive additions than those provided by \CFA, in addition to the runtime overhead of reference-counting.
+Vala~\cite{Vala} compiles to GObject-based C, adding the burden of learning a separate language syntax to the aforementioned demerits of GObject as a modernization path for existing C code-bases.
+Java~\cite{Java8} included generic types in Java~5, which are type-checked at compilation and type-erased at runtime, similar to \CFA's.
+However, in Java, each object carries its own table of method pointers, while \CFA passes the method pointers separately to maintain a C-compatible layout.
 Java is also a garbage-collected, object-oriented language, with the associated resource usage and C-interoperability burdens.
 D~\cite{D}, Go, and Rust~\cite{Rust} are modern compiled languages with abstraction features similar to \CFA traits, \emph{interfaces} in D and Go, and \emph{traits} in Rust.
+D~\cite{D}, Go, and Rust~\cite{Rust} are modern, compiled languages with abstraction features similar to \CFA traits, \emph{interfaces} in D and Go and \emph{traits} in Rust.
 However, each language represents a significant departure from C in terms of language model, and none has the same level of compatibility with C as \CFA.
 D and Go are garbage-collected languages, imposing the associated runtime overhead.
 The necessity of accounting for data transfer between managed runtimes and the unmanaged C runtime complicates foreign-function interfaces to C.
 Furthermore, while generic types and functions are available in Go, they are limited to a small fixed set provided by the compiler, with no language facility to define more.
 D restricts garbage collection to its own heap by default, whereas Rust is not garbage collected and, thus, has a lighter-weight runtime more interoperable with C.
+D restricts garbage collection to its own heap by default, while Rust is not garbage-collected, and thus has a lighter-weight runtime more interoperable with C.
 Rust also possesses much more powerful abstraction capabilities for writing generic code than Go.
 On the other hand, Rust's borrow checker provides strong safety guarantees but is complex and difficult to learn and imposes a distinctly idiomatic programming style.
+On the other hand, Rust's borrow-checker provides strong safety guarantees but is complex and difficult to learn and imposes a distinctly idiomatic programming style.
 \CFA, with its more modest safety features, allows direct ports of C code while maintaining the idiomatic style of the original source.
+\vspace*{-18pt}
+\subsection{Tuples/variadics}
+\vspace*{-5pt}
+\subsection{Tuples/Variadics}
 Many programming languages have some form of tuple construct and/or variadic functions, \eg SETL, C, KW-C, \CC, D, Go, Java, ML, and Scala.
 SETL~\cite{SETL} is a high-level mathematical programming language, with tuples being one of the primary data types.
 Tuples in SETL allow subscripting, dynamic expansion, and multiple assignment.
+C provides variadic functions through @va_list@ objects, but the programmer is responsible for managing the number of arguments and their types;
+thus, the mechanism is type unsafe.
+C provides variadic functions through @va_list@ objects, but the programmer is responsible for managing the number of arguments and their types, so the mechanism is type unsafe.
 KW-C~\cite{Buhr94a}, a predecessor of \CFA, introduced tuples to C as an extension of the C syntax, taking much of its inspiration from SETL.
 The main contributions of that work were adding MRVF, tuple mass and multiple assignment, and record-member access.
 \CCeleven introduced @std::tuple@ as a library variadic-template structure.
+\CCeleven introduced @std::tuple@ as a library variadic template structure.
 Tuples are a generalization of @std::pair@, in that they allow for arbitrary length, fixed-size aggregation of heterogeneous values.
 Operations include @std::get<N>@ to extract values, @std::tie@ to create a tuple of references used for assignment, and lexicographic comparisons.
 \CCseventeen proposes \emph{structured bindings}~\cite{Sutter15} to eliminate predeclaring variables and the use of @std::tie@ for binding the results.
 This extension requires the use of @auto@ to infer the types of the new variables; hence, complicated expressions with a nonobvious type must be documented with some other mechanism.
+\CCseventeen proposes \emph{structured bindings}~\cite{Sutter15} to eliminate pre-declaring variables and use of @std::tie@ for binding the results.
+This extension requires the use of @auto@ to infer the types of the new variables, so complicated expressions with a non-obvious type must be documented with some other mechanism.
 Furthermore, structured bindings are not a full replacement for @std::tie@, as it always declares new variables.
 Like \CC, D provides tuples through a library variadic-template structure.
 Go does not have tuples but supports MRVF.
 Java's variadic functions appear similar to C's but are type safe using homogeneous arrays, which are less useful than \CFA's heterogeneously typed variadic functions.
+Java's variadic functions appear similar to C's but are type-safe using homogeneous arrays, which are less useful than \CFA's heterogeneously-typed variadic functions.
 Tuples are a fundamental abstraction in most functional programming languages, such as Standard ML~\cite{sml}, Haskell, and Scala~\cite{Scala}, which decompose tuples using pattern matching.
-\vspace*{-18pt}
 \subsection{C Extensions}
+\vspace*{-5pt}
+\CC is the best known C-based language and is similar to \CFA in that both are extensions to C with source and runtime backward compatibility.
+Specific differences between \CFA and \CC have been identified in prior sections, with a final observation that \CFA has equal or fewer tokens to express the same notion in many cases.
+\CC is the best known C-based language, and is similar to \CFA in that both are extensions to C with source and runtime backwards compatibility.
+Specific difference between \CFA and \CC have been identified in prior sections, with a final observation that \CFA has equal or fewer tokens to express the same notion in many cases.
 The key difference in design philosophies is that \CFA is easier for C programmers to understand by maintaining a procedural paradigm and avoiding complex interactions among extensions.
 \CC, on the other hand, has multiple overlapping features (such as the three forms of polymorphism), many of which have complex interactions with its object-oriented design.
 As a result, \CC has a steep learning curve for even experienced C programmers, especially when attempting to maintain performance equivalent to C legacy code.
 There are several other C extension languages with less usage and even more dramatic changes than \CC.
 \mbox{Objective-C} and Cyclone are two other extensions to C with different design goals than \CFA, as discussed above.
+As a result, \CC has a steep learning curve for even experienced C programmers, especially when attempting to maintain performance equivalent to C legacy-code.
+There are several other C extension-languages with less usage and even more dramatic changes than \CC.
+Objective-C and Cyclone are two other extensions to C with different design goals than \CFA, as discussed above.
 Other languages extend C with more focused features.
 $\mu$\CC~\cite{uC++book}, CUDA~\cite{Nickolls08}, ispc~\cite{Pharr12}, and Sierra~\cite{Leissa14} add concurrent or data-parallel primitives to C or \CC;
 data-parallel features have not yet been added to \CFA, but are easily incorporated within its design, whereas concurrency primitives similar to those in $\mu$\CC have already been added~\cite{Delisle18}.
 Finally, CCured~\cite{Necula02} and Ironclad \CC~\cite{DeLozier13} attempt to provide a more memory-safe C by annotating pointer types with garbage collection information; type-checked polymorphism in \CFA covers several of C's memory-safety issues, but more aggressive approaches such as annotating all pointer types with their nullability or requiring runtime garbage collection are contradictory to \CFA's backward compatibility goals.
+data-parallel features have not yet been added to \CFA, but are easily incorporated within its design, while concurrency primitives similar to those in $\mu$\CC have already been added~\cite{Delisle18}.
+Finally, CCured~\cite{Necula02} and Ironclad \CC~\cite{DeLozier13} attempt to provide a more memory-safe C by annotating pointer types with garbage collection information; type-checked polymorphism in \CFA covers several of C's memory-safety issues, but more aggressive approaches such as annotating all pointer types with their nullability or requiring runtime garbage collection are contradictory to \CFA's backwards compatibility goals.
 \section{Conclusion and Future Work}
 The goal of \CFA is to provide an evolutionary pathway for large C development environments to be more productive and safer, while respecting the talent and skill of C programmers.
 While other programming languages purport to be a better C, they are, in fact, new and interesting languages in their own right, but not C extensions.
 The purpose of this paper is to introduce \CFA, and showcase language features that illustrate the \CFA type system and approaches taken to achieve the goal of evolutionary C extension.
 The contributions are a powerful type system using parametric polymorphism and overloading, generic types, tuples, advanced control structures, and extended declarations, which all have complex interactions.
+The goal of \CFA is to provide an evolutionary pathway for large C development-environments to be more productive and safer, while respecting the talent and skill of C programmers.
+While other programming languages purport to be a better C, they are in fact new and interesting languages in their own right, but not C extensions.
+The purpose of this paper is to introduce \CFA, and showcase language features that illustrate the \CFA type-system and approaches taken to achieve the goal of evolutionary C extension.
+The contributions are a powerful type-system using parametric polymorphism and overloading, generic types, tuples, advanced control structures, and extended declarations, which all have complex interactions.
 The work is a challenging design, engineering, and implementation exercise.
 On the surface, the project may appear as a rehash of similar mechanisms in \CC.
 However, every \CFA feature is different than its \CC counterpart, often with extended functionality, better integration with C and its programmers, and always supporting separate compilation.
 All of these new features are being used by the \CFA development team to build the \CFA runtime system.
+All of these new features are being used by the \CFA development-team to build the \CFA runtime-system.
 Finally, we demonstrate that \CFA performance for some idiomatic cases is better than C and close to \CC, showing the design is practically applicable.
+While all examples in the paper compile and run, there are ongoing efforts to reduce compilation time, provide better debugging, and add more libraries;
+when this work is complete in early 2019, a public beta release will be available at \url{https://github.com/cforall/cforall}.
+There is also new work on a number of \CFA features, including arrays with size, runtime type information, virtual functions, user-defined conversions, and modules.
+While \CFA polymorphic functions use dynamic virtual dispatch with low runtime overhead (see Section~\ref{sec:eval}), it is not as low as \CC template inlining.
+Hence, it may be beneficial to provide a mechanism for performance-sensitive code.
+Two promising approaches are an @inline@ annotation at polymorphic function call sites to create a template specialization of the function (provided the code is visible) or placing an @inline@ annotation on polymorphic function definitions to instantiate a specialized version for some set of types (\CC template specialization).
+ These approaches are not mutually exclusive and allow performance optimizations to be applied only when necessary, without suffering global code bloat.
+In general, we believe separate compilation, producing smaller code, works well with loaded hardware caches, which may offset the benefit of larger inlined code.
+While all examples in the paper compile and run, a public beta-release of \CFA will take 6--8 months to reduce compilation time, provide better debugging, and add a few more libraries.
+There is also new work on a number of \CFA features, including arrays with size, runtime type-information, virtual functions, user-defined conversions, and modules.
+While \CFA polymorphic functions use dynamic virtual-dispatch with low runtime overhead (see Section~\ref{sec:eval}), it is not as low as \CC template-inlining.
+Hence it may be beneficial to provide a mechanism for performance-sensitive code.
+Two promising approaches are an @inline@ annotation at polymorphic function call sites to create a template-specialization of the function (provided the code is visible) or placing an @inline@ annotation on polymorphic function-definitions to instantiate a specialized version for some set of types (\CC template specialization).
+These approaches are not mutually exclusive and allow performance optimizations to be applied only when necessary, without suffering global code-bloat.
+In general, we believe separate compilation, producing smaller code, works well with loaded hardware-caches, which may offset the benefit of larger inlined-code.
 \section{Acknowledgments}
 The authors would like to recognize the design assistance of Glen Ditchfield, Richard Bilson, Thierry Delisle, Andrew Beach, and Brice Dobry on the features described in this paper and thank Magnus Madsen for feedback on the writing.
 Funding for this project was provided by Huawei Ltd (\url{http://www.huawei.com}), and Aaron Moss and Peter Buhr were partially funded by the Natural Sciences and Engineering Research Council of Canada.
+The authors would like to recognize the design assistance of Glen Ditchfield, Richard Bilson, Thierry Delisle, Andrew Beach and Brice Dobry on the features described in this paper, and thank Magnus Madsen for feedback on the writing.
+Funding for this project has been provided by Huawei Ltd.\ (\url{http://www.huawei.com}), and Aaron Moss and Peter Buhr are partially funded by the Natural Sciences and Engineering Research Council of Canada.
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