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Diomidis Spinellis Publications

CScout: A Refactoring Browser for C

Diomidis Spinellis

Athens University of Economics and Business
Department of Management Science and Technology
Patision 76, GR-104 34 Athens, Greece


Despite its maturity and popularity, the C programming language still lacks tool support for reliably performing even simple refactoring, browsing, or analysis operations. This is primarily due to identifier scope complications introduced by the C preprocessor. The CScout refactoring browser analyses complete program families by tagging the original identifiers with their precise location and classifying them into equivalence classes orthogonal to the C language's namespace and scope extents. A web-based user interface provides programmers with an intuitive source code analysis and navigation front-end, while an SQL-based backend allows more complex source code analysis and manipulation. CScout has been successfully applied to many medium and large proprietary and open source projects identifying thousands of modest refactoring opportunities.
C browser refactoring preprocessor

1  Introduction

C remains the language of choice for developing systems applications, such as operating systems and databases, embedded software, and the majority of open-source projects [44,p. 16]. Despite the language's popularity, tool support for performing even simple refactoring, browsing, or analysis operations is currently lacking. Programmers typically resort to using either simplistic text-based operations that fail to capture the language's semantics, or work on the results of the compilation and linking phase that-due to the effects of preprocessing-do not correctly reflect the original source code. Interestingly, many of the tools in a C programmer's arsenal were designed in the 1970s, and fail to take advantage of the CPU speed and memory capacity of a modern workstation. In this paper we describe how the CScout refactoring browser, running on a powerful workstation, can be used to accurately analyze, browse, and refactor large program families written in C. The theory behind CScout's operation is described in detail elsewhere [45]; this paper focuses on the tool's design, implementation, and application.
CScout can process program families consisting of multiple related projects (we define a project as a collection of C source files that are linked together) correctly handling most of the complexity introduced by the C preprocessor. CScout takes advantage of modern hardware (fast processors, large address spaces, and big memory capacities) to analyze C source code beyond the level of detail and accuracy provided by current IDEs, compilers, and linkers. Specifically, CScout's analysis takes into account both the identifier scopes introduced by the C preprocessor and the C language proper scopes and namespaces.
The objective of this paper is to provide a tour of CScout by describing the domain's challenges, the operation of CScout and its interfaces, the system's design and implementation, and details of CScout's application to a number of large software projects. The main contributions of this paper are the illustration of the types of problems occurring in the analysis of real-life C source code and the types of refactorings that can be achieved, the demonstration through the application of CScout to a number of systems that accurate large-scale analysis of C code is in fact possible, and a discussion of lessons associated with the construction of browsers and refactoring tools for languages, like C and C++, that involve a preprocessing step.

2  Problem Statement

Many features of the C language hinder the precise analysis of programs written in it and complicate the design of corresponding reasoning algorithms [15]. The most important culprits are unrestricted pointers, aliasing, arbitrary type casts, non-local jumps, an underspecified build environment, and the C preprocessor. All features but the last two ones limit our ability to reason about the runtime behavior of programs (see e.g. the article [18] and the references therein). Significantly, the C preprocessor and a compilation environment based on loosely-coupled tools, like make and a language-agnostic linker, also restrict programmers from performing even supposedly trivial operations such as determining the scope of a variable, the type of an identifier, or the extent of a module.

2.1  Preprocessor Complications

In summary, preprocessor macros complicate the notion of scope and the notion of an identifier [11,4,45]. For one, macros and file inclusion create their own scopes. This is for example the case when a single textual macro using a field name that is incidentally identical between two structures that are not otherwise related is applied on variables of those structures. In the following example, a renaming operation of the identifier len will require changing in all three definitions, although in C the members of each data structure belong to a different namespace. struct disk_block int len; /* ... */ db; struct mem_block int len; /* ... */ mb; #define get_block_len(b) ((b).len)
int s = get_block_len(db) + get_block_len(mb);
In addition, new identifiers can be formed at compile time via the preprocessor's concatenation operator. As an example, the following code snippet defines a variable named sysctl_var_sdelay, even though this name does not appear in the source file. #define SYSCTL(x) static int sysctl_var_ ## x SYSCTL(sdelay);
An additional complication comes from the use of conditionally compiled code (see also Sections 4.1 and 7). Such code may or may not be compilable under a given compilation environment, and, often, blocks of such code may be mutually incompatible.

2.2  Code Reuse Complications

Parnas [38] defines a program family as a set of programs that should be studied by first considering the common properties of the set and then determining individual properties of family members (see also the work by Weiss and Lai [61]). When analyzing C source code for browsing and refactoring purposes we are interested in program families consisting of programs that through their build process reuse common elements of source code. This is a property of what has been termed the build-time software architecture view [57]. We have identified three interesting instances of source code sharing in such families.
Figure 1: Program family relationships in the Free BSD implementation of the Unix utilities.
Program configurations   Often the same source code base is used to derive a number of program configurations. As an example, the Free BSD kernel source code is used as a basis for creating kernels for five processor architectures. Major parts of the source code are the same among the different architectures, while the compilation is influenced by architecture-dependent macros specifying properties such as the architecture's native element size (32 or 64 bits) and the "endianess" of the memory layout (the order in which an integer's bytes are stored in memory).
Ad-hoc code reuse   In many cases elements of a source code base are reused to create various executable programs. Although code reuse is typically realized by creating a common library (such as the Unix libraries math, dbm, termcap, and telnet), which is linked with each program requiring the given functionality, there are cases where a simpler and less structured approach is adopted. The example in Figure 1 illustrates some dependencies between three (supposedly separate) Unix programs where CScout was applied: test, sh, and cp. Among them the condition evaluation utility test and the shell sh share the source file test.c, while two source files both include the header err.h.
Version branches   When there is a supported maintenance branch among different releases of the same program, then the same source code (with typically small differences between release-dependent versions) is reused among the different releases.
In all three cases we described, the sharing and the differentiation of the source code does not typically happen through mechanisms of the C language, but through extra-linguistic facilities. The most important of these are compiler invocation options that set macros and include file paths, symbolic links across files and directories, environment variables affecting the build process, macros hard-coded in the compiler, and the automated copying of files as part of the build process. Despite these complications, a viable tool should allow browsing and propagate refactoring operations across all files in a given program family.

2.3  Problem Impact

Due to the previously described problems, programmers are currently working with methods and tools that are neither sound nor complete. The typical textual search for an identifier in a source code base may fail to locate identifier instances that are dynamically constructed, or will also locate identifiers that reside in a different scope or namespace. When working with a compiler or IDE-constructed symbol table there is another problem. Many C implementations treat preprocessing as a separate phase and fail to pass information about C macros down through the other compilation phases. Therefore, a more sophisticated search using such a symbol table database will fail to match all macro instances, while its results will be difficult to match against the original source code. Consequently, program maintenance and evolution suffer, because programmers, unsupported by the tools they use, are reluctant to perform even a simple rename-function refactoring. Anecdotal evidence supports our observation: consider mutilated identifier names such as that of the Unix creat system call that still persist, decades after the reasons for their original names have become irrelevant [7,p. 60]. The readability of existing code slowly decays as layers of deprecated historical practice accumulate [23,pp. 4-6, 184] and even more macro definitions are used to provide compatibility bridges between legacy and modern code.

3  Related Work

Tools that aid program code analysis and transformation operations are often termed browsers [19,pp. 297-307] and refactoring browsers [40] respectively. Related work on object-oriented design refactoring [56] asserts that it is generally not possible to handle all problems introduced by preprocessing in large software applications. However, as we shall see in the following sections, advances in hardware capabilities are now making it possible to implement useful refactoring tools that address the complications of the C programming language. The main advantage of our approach is the correct handling of preprocessor constructs, so, although we have only tested the approach on different variants of C programs, (K&R C, ANSI C, and C99 [28,1,25]) it is, in principle, also applicable to programs written in C++ [53], Cyclone [27], PL/I and many assembly-code dialects.
Reference [10] provides a complete empirical analysis of the C preprocessor use, a categorization of macro bodies, and a description of common erroneous macros found in existing programs. Two theoretical approaches proposed for dealing with the problems of the C preprocessor involve the use of mathematical concept analysis for handling cases where the preprocessor is used for configuration management [43], and the definition of an abstract language for capturing the abstractions for the C preprocessor in a way that allows formal analysis [11]. The two-way mapping between preprocessor tokens and C-proper identifiers used by CScout was first suggested by Livadas and Small [34].
Table 1: Comparison of C and C++ Refactoring and Transformation Tools
CScout Xrefactory Proteus CDT Refactor!
Number of supported refactorings 4 11 5 150
Handle C namespaces
Rename preprocessor identifiers ×
Handle scopes introduced by the C preprocessor ×××
Handle identifiers created by the C preprocessor ××××
C++ support ×
Yacc support ××××
User environment Web Emacs - Eclipse Visual Studio
Reference [59] [60] [42] [9]
A number of tools support the refactoring and transformation of C and C++ code. A summary of their capabilities appears in Table 1; below we provide a brief description of each tool in comparison to CScout.
A tool adopting an approach similar to ours is Vittek's Xrefactory [59]. Its functionality is integrated with the Emacs editor [51]. Compared to CScout, Xrefactory supports C++, and thus also offers a number of additional refactorings: field and method moving, pushing down and pulling up fields and methods, and the encapsulation of fields. However, Xrefactory is unable to handle identifiers generated during the preprocessing stage; its author writes that deciding how to handle the renaming of an identifier that is constructed from parts of other identifiers is, in general, an unsolvable problem. The case refers to the renaming of the identifier sysctl_var_sdelay we showed in Section 2.1 into, say, foo. Vittek, correctly writes that there is no way to perform this renaming in a natural way. We sidestep this restriction by only allowing the renaming of an identifier's constituent parts. Thus, in this case, a CScout's user can rename individually the identifier's sysctl_var part and the sdelay part, with each renaming affecting the other corresponding parts in the program.
Another related tool, Proteus [60], analyzes C and C++ code, faithfully preserving preprocessor directives, comments, and formatting information by integrating these elements into an abstract syntax tree ( AST). This has the advantage of allowing more sophisticated transformations than those that CScout can perform. The changes are specified using a domain-specific language, YATL-Yet Another Transformation Language. Proteus handles all preprocessor directives as layout elements. Consequently, because Proteus does not consider and handle macro definitions as first-class entities these cannot be changed. Furthermore, the code reconstructed from the AST can differ from the original one, even if no transforms were applied; the authors conducted three large studies and found that 2.0%-4.5% of the lines differed.
In the recent years two IDEs have evolved to support the refactoring C and C++ code through add-on modules. Compared to CScout these support C++ and offer many more refactoring operations, but with less fidelity. The Eclipse C/C++ Development Tooling ( CDT) project features the following refactorings: extract constant, extract function, generate getters and setters, hide method, and implement method [42]. The refactoring support of Visual Studio 2008 does not support C, but a third-party add-on Refactor! supports C/C++, offering 150 refactorings [9]. However, the most recent versions of these two systems (Eclipse CDT 5.0.2-2009-02-13 and Refactor! for Visual Studio 3.2-2009-02-27) cannot handle the preprocessor complications listed in Section 2.1. Specifically, when attempting to rename identifiers appearing in Section's 2.1 source examples Eclipse CDT reports "The selected name could not be analyzed", whereas Refactor! renames the identifier specified, but fails to rename other associated instances.
Other related work has proposed the integration of multiple approaches, views, and perspectives into a single environment [2], the full integration of preprocessor directives in the internal representation [16,14], the use of an abstract syntax graph for communicating semantic information [30], and the use of a GXL [21] schema for representing either a static or a dynamic view of preprocessor directives [58].
The handling of multiple configurations implemented through preprocessor directives that CScout implements, has also been studied in other contexts, such as the removal of preprocessor conditionals through partial evaluation [5], the type checking of conditionally compiled code [3], and the use of symbolic execution to determine the conditions associated with particular lines of code [22].

4  The CScout Refactoring Browser

To be able to map and rename identifiers across program families accurately and efficiently CScout integrates in a single processing engine functions of a build tool (such as make or ant), a C preprocessor, a C compiler front-end, a parser of yacc files, a linker, a relational database export facility, and a web-based GUI.
Table 2: File and Function Metrics that CScout Collects
File Metrics
  • Number of: statements, copies of the file, defined project-scoped functions, defined file-scoped (static) functions, defined project-scoped variables, defined file-scoped (static) variables, complete aggregate (struct/union) declarations, declared aggregate (struct/union) members, complete enumeration declarations, declared enumeration elements, directly included files
File and Function Metrics
  • Number of: characters, comment characters, space characters, line comments, block comments, lines, character strings, unprocessed lines, preprocessed tokens, compiled tokens, C preprocessor directives, processed C preprocessor conditionals (ifdef, if, elif), defined C preprocessor function-like macros (e.g. max(a, b)), defined C preprocessor object-like macros (e.g. EOF)
  • Maximum number of characters in a line
Function Metrics
  • Number of: statements or declarations, operators, unique operators, numeric constants, character literals, else clauses, global namespace occupants at function's top, parameters
  • Number of statements by type: if, switch, break, for, while, do, continue, goto, return
  • Number of labels by type: goto, case, default
  • Number of identifiers by type: project-scoped, file-scoped (static), macro, object (identifiers having a value) and object-like macros, label
  • Number unique of identifiers by type: project-scoped, file-scoped, macro, object and object-like
  • Maximum level of statement nesting
  • Fan-in and fan-out
  • Complexity: cyclomatic, extended cyclomatic, and maximum (including switch statements) cyclomatic
CScout as a source code analysis tool can:
More importantly, CScout helps programmers in refactoring code by identifying dead objects to remove, and can automatically perform accurate global rename identifier, add parameter, remove parameter, and change parameter order refactorings [20]. One might question whether support for a few simple refactoring types merits calling CScout a refactoring tool. To answer this, consider that the rename identifier operation is by far the most common refactoring operation performed in practice [36], and that performing refactoring operations reliably on production C source code is very tricky. Specifically, CScout will automatically rename identifiers and refactor function arguments Uniquely, CScout will rename identifiers occurring in macro bodies and even parts of other identifiers, when these are created through the C preprocessor's token concatenation feature.

4.1  Source Code Processing

Figure 2: CScout system operation.
Table 3: The #pragma Directives of the CScout Processing Script
Pragma Action
echo string Display the string on CScout's standard output when the directive is processed.
ro_prefix string Add string to the list of filename prefixes that mark read-only files. This is a global setting used for bifurcating the source code into the system's (read-only) files and the application's (writable) files.
project string Set the name of the current project (linkage unit) to string. All identifiers and files processed from then on will be set to belong to the given project.
block_enter Enter a nested scope block. Two blocks are supported, the first block_enter will enter the project scope (linkage unit); the second encountered nested block_enter will enter the file scope (compilation unit).
block_exit Exit a nested scope block. The number of block_enter pragmasshould match the number of block_exit pragmas and there should never be more than two block_enter pragmas in effect.
process string Analyze (CScout's equivalent to compiling) the C source file named string.
pushd string Set the current directory to string, saving the previous current directory in a stack. From that point onward, all relative file accesses will search the specified file from the given directory.
popd Restore the current directory to the one in effect before a previously pushed directory. The number of pushd pragmas should match the number of popd pragmas.
includepath string Add string to the list of directories used for searching included files (the include path).
clear_include Clear the include path, allowing the specification of a new one from scrarch.
clear_defines Clear all defined macros allowing the specification of new ones from scrarch. Should normally be executed before processing a new file. Note that macros can be defined in the processing script using the normal #define C preprocessor directive.
Figure 2 illustrates the model of CScout's operation. The operation is directed by a processing script, which contains a sequence of imperative processing commands. These commands setup an environment for processing each source code file. The environment is defined by the current directory, the header file directory search path, externally defined macros, and the linkage unit name to be associated with global identifiers. The script is a C file comprised mostly of #define directives and CScout-specific #pragma directives (see Table 3). In cases where the source code involves multiple configurations implemented through conditional compilation the script will contain directives to process the source code multiple times, once for each configuration with different options (defined macros or include file paths) set in each pass.
Creating the processing script is not trivial; for a large project, like the Linux kernel, the (automatically generated) script can be more than half a million lines long. The script can be created in three ways.
  1. A CScout companion program, csmake, can monitor compiler, archiver, and linker invocations in a make-driven build process, and thereby gather data to automatically create the processing script. This method has been used for processing all code listed in Table 4 (apart from the Solaris and Windows kernels), as well as tens of other Unix-based systems.
  2. A declarative specification of the source components, compiler options, and file locations required to build the members of a program family is processed by the CScout workspace compiler cswc. This method offers precise control of CScout's processing. It is also useful in cases when csmake is not compatible with the platform's compilation process; csmake currently handles the programs make, gcc, cc, ld, ar, and mv running in a POSIX shell environment. A 27-line csmake specification has been used for processing the Unix utilities illustrated in Figure 1 and a 125-line specification for processing a 350 KLOC proprietary CAD system.
  3. The build process can be instrumented to record the commands executed. This transcript can then be semi-automatically converted into the CScout processing script. For instance, a 74-line Perl script was used to convert the 1,149-line output of Microsoft's nmake program compiling the Windows Research Kernel into a 51,288-line Cscout processing script. Similarly, a 137-line Perl script was used to convert the 26,704-line output of Sun's dmake program [54] compiling the OpenSolaris kernel into a 140,552-line Cscout processing script.
As a by-product of the processing CScout generates a list of error and warning messages in a standard format that typical editors (like vi and Emacs) and IDEs can process. These warnings go beyond what a typical compiler will detect and report
Many worthwhile maintenance activities can be performed by processing this standardized error report. In one case we automatically processed those warnings to remove 765 superfluous #include directives (out of a total of 5429) from a 190 KLOC CAD program [50], thereby increasing its maintainability by reducing namespace pollution.
After processing all source files, CScout can operate as a web server, allowing members of a team to browse and modify the files comprising the program family. All changes performed through the web interface (currently rename operations on identifiers and various function argument refactorings) are mirrored in an in-memory copy of the source code. These changes can then be committed back to the source code files, optionally issuing commands for a version control system. When CScout writes back the refactored source code the only changes made are the renamed identifiers and the changed function arguments. Therefore, CScout's effect on the source code's formatting is negligible. A separate backend enables CScout to export its data structures to a relational database management system for further processing.

4.2  Web-Based Interface

A screen dump of the CScout web interface.
Figure 3: A screen dump of the CScout web interface.
The identifier query form.
4(a) The identifier query form.
A function or macro page.
4(b) A function or macro page.
Figure 4: The CScout web front-end in operation.
Included files.
5(a) Included files.
Call graph spanning functions and macros.
5(b) Call graph spanning functions and macros.
Control dependencies between files.
5(c) Control dependencies between files.
Data dependencies between files.
5(d) Data dependencies between files.

Figure 5: CScout-generated graphs for the awk source code file lex.c.
The easiest way to use CScout is through its interactive web-based interface (see Figure 3). Using the SWILL embedded web server library [29], CScout allows the connection of web clients to the tool's HTTP-server interface. Through a set of hyperlinks users can perform the following tasks. The rename functionality can be used to semi automatically perform two important refactoring operations: rename, e.g. Griswold and Notkin's [20] "rename-variable", and remove, e.g. Fowler's [13] "Remove Parameter". Remove refactorings can be trivially performed by hand, after identifiers that occur exactly once have been automatically and accurately identified. Fully automating this process is hard (there are many rare special cases that have to be handled), but performing it by hand is in most cases very easy. The substitution template for function parameters can be used for adding a function argument (with a user-specified default value), for removing a function argument (by omitting its placeholder from the substitution pattern), and for changing the order of a function's arguments.
The web server follows the representational state transfer ( REST) architecture [12], and therefore its URLs can be used for interoperating with other tools. For instance, a build tool could use the URL
to obtain the compile-time dependencies between a project's files. Furthermore, as all web pages that CScout generates are identified by a unique URL, programmers can easily mark important pages (such as a particular identifier that must be refactored, or the result of a specialized form-based query) using their web browser's bookmarking mechanism, or even email an interesting page's URL to a coworker. In fact, many links appearing on CScout's main web page are simply canned hyperlinks to the general queries we previously outlined.

4.3  SQL Backend

Figure 6: The logical schema of the exported database.
CScout can also dump the data structures representing the source code analysis it performed in an SQL script suitable for loading the data into a relational database. There is considerable history behind storing source code in a relational schema both for procedural languages in general [33] and, in particular, for C [6]. We chose to use a relational model over a specialized and more expressive logic query language, along the lines of SOUL [62] or JQuery [26,8], in order to exploit the performance and maturity of existing RDBMS systems for the offline storage of very large data sets-one particular study we performed [48] involved storing and processing more than 160 million records.
Figure 6 shows the most important parts of the corresponding schema. (Four tables associated with reasoning about include file dependencies are omitted.) Through the database one can issue all the queries available on the GUI front-end and many more. For instance, the following simple SQL query will find all type definitions that don't end in "_t" (a common naming convention). select distinct name from ids left join tokens on ids.eid = tokens.eid where ids.typedef and not name like '
The following, more complex, query select name,count(*) as nfile from (select fid,tokens.eid,count(*) as c from tokens group by eid,fid) as cl inner join ids on cl.eid = ids.eid group by ids.eid, order by nfile desc; will show all identifiers, ordered by the number of different files in which they occur (a measure of coupling):
| name    | nfile |
| NULL    |  3292 |
| u       |  2560 |
| printk  |  1922 |
| ...     |   ... |
The program's SQL representation contains all elements of the corresponding source code. Therefore, one can also perform large-scale refactorings through SQL commands. Then, the source code of each file (e.g. file number 42 in the following SQL query) can be fully reconstituted from its refactored parts. select s from ( select name as s,foffset from ids inner join tokens on ids.eid = tokens.eid where fid = 42 union select code as s,foffset from rest where fid = 42 union select comment as s,foffset from comments where fid = 42 union select string as s,foffset from strings where fid = 42 ) order by foffset

5  Design and Implementation

Bringing CScout into life required careful analysis of the principles of its operation, a design that matched the software and computing resources at hand, and substantial implementation work. The major challenges can be divided into: preprocessing and parsing, the enforcement of C namespaces, the handling of C preprocessor complications, the handling of code reuse complications, testing, achieving adequate performance, and keeping the project in a manageable scale.

5.1  Preprocessing and Parsing

Preprocessing C is anything but trivial. CScout's lexical analyzer and the C preprocessor are hand-crafted; converging toward a correct preprocessor proved to be tricky. For many years CScout would be patched to fix misbehaviors occurring in obscure cases of macro invocations. The situation was becoming increasingly difficult, because often fixing one case would break another. In the end we realized that the only way to achieve correct behavior was to locate (through a personal communication with its author) and implement the so-called Prosser's macro expansion algorithm [39]. Almost miraculously all test cases worked correctly, and after two years of use and many millions of processed code, no other problems were reported in the area of macro expansion.
In contrast to C++, C is not difficult to parse, but the grammar supplied as part of the C standards is not suitable for generating yacc-based parsers, because such parsers then contain numerous rule conflicts. CScout's C grammar is based on Roskind's work [41] extended to support the parsing of yacc files, and many C99 [25], gcc, and Microsoft C extensions. It comprises 144 productions and, after 149 revisions, it is 2,670 lines long. The parsing of preprocessor expressions and the C code are handled by two separate btyacc grammars. Btyacc was selected over yacc for its portability, better support for C++, superior handling of syntax errors through backtracking, and the ability to customize it in order to support the side-by-side linking of two separate grammars.
Handling the various language extension dialects hasn't proven to be difficult; probably because CScout is quite permissive in what is accepts. Therefore, currently CScout's input is the union of all possible language extensions. If in the future some extensions are found to be mutually exclusive, this can be handled by adding #pragma directives that will change the handling of the corresponding keywords.

5.2  Enforcement of C Namespaces

The separation of identifiers into C namespaces is achieved through a symbol table containing basic type information for identifiers in the current scope. Furthermore, support for the C99 initializer designators also requires the evaluation of compile-time constants. This non-trivial functionality is needed, because the array position of an initializer can be specified by a compile-time constant. When elements of nested aggregates-structures, unions, and arrays-are specified in comma-separated form without enclosing them in braces, the array position constant must be evaluated in order to determine the type of the next element.
The type checking subsystem is mainly used to identify a tag's underlying structure or union for member access and initialization, and to handle type definitions. In addition, its implementation provided us with a measure of confidence regarding the equivalence class unification operations dictated by the language's semantics.
The symbol table design follows the language's block scoping rules, with special cases handling prototype declarations and compilation and linkage unit visibility. Between the processing of two different projects (linkage units) the complete symbol table is cleared and only equivalence classes remain in memory, thus reducing CScout's memory footprint. This optimization can be performed, because if we ignore extra-linguistic facilities (such as shared libraries, debug symbols, and reflection) linked programs operate as standalone processes and do not depend on any program identifiers for their operation.

5.3  Handling C Preprocessor Complications

The basic principle of CScout's operation is to tag each identifier appearing in the original source code with its precise location (file and offset) and to follow that identifier (or its part when new identifiers are composed by concatenating original ones) across preprocessing, parsing, (partial) semantic analysis, and (notional) linking [45]. To handle the scoping rule mix-ups generated by the C preprocessor (see Section 2.1), every identifier is set to belong to exactly one equivalence class: a set of identifiers that would have to be renamed in concert for the program family to remain semantically and syntactically correct. The notion of an equivalence class is orthogonal to the language's existing namespace and scope extents, taking into account the changes to those extents introduced by the C preprocessor. When each identifier token is read, a new equivalence class for that token is created. Every time a symbol table lookup operation for an identifier matches an existing identifier (e.g. the use of a declared variable or the use of a parameter of a function-like macro) the two corresponding equivalence classes are unified into a single one.
In total, 20 equivalence class unifications are performed by CScout. These can be broadly classified into the following categories: macro formal parameters and their use inside the macro body, macros used within the source code, macros being redefined or becoming undefined, tests for macros being defined, identifiers used in expressions, structure or union member access (direct, through a pointer indirection, or through an initializer designator), declarations using typedef types, application of the typeof operator (a gcc extension) to an identifier, use of structure, union, and enumeration tags, old-style [28] function parameter declarations with the respective formal parameter name, multiple declarations and definitions of objects with compilation or linkage unit scope, and goto labels and targets, respectively.
By classifying all identifiers into equivalence classes, and then creating and merging the classes following the language's rules, we end up with a data structure that can identify many interesting relationships between identifiers.

5.4  Handling Code Reuse Complications

CScout handles the code reuse complications outlined in Section 2.2 by providing an integrated build system that can process multiple linkage units as a whole.
An early design choice based this build system on extending the C language that CScout can process with a few #pragma directives (see Table 3). Making the build language an extension of C means that existing C facilities can be used for a number of tasks. Thus, external macro definitions that other build systems pass to the compiler as flags are simply defined through #define directives. Furthermore, internally-defined macro definitions, such as those handling gcc's built-in intrinsic functions, can be easily introduced simply by processing the file that defines them with a #include directive.
Making the build language textual, rather than GUI-based as is typically the case in many IDEs, means that other more sophisticated tools can create build scripts. This is the case with the cswc, the CScout workspace compiler and csmake, the make-driven build process monitor and build script generator.

5.5  Testing

The complexity of CScout's analysis requires a framework to ensure that it remains functional and correct as the code evolves. The testing of CScout consists of stress and regression testing. Stress testing involves applying CScout to various large open-source systems. Problems in the preprocessor, the parser, or the semantic analysis quickly exhibit themselves as parsing errors or crashes. In addition, by having CScout replace all identifiers of a system with mechanically-derived names and then recompiling and testing the corresponding code builds confidence in CScout's equivalence algorithms and the rename-identifier refactoring.
Regression testing is currently used to verify corner cases and check for accidental errors. The CScout's preprocessor is tested through 70 test cases whose output is then compared with the hand-verified output. The parser and analyzer are further tested through 42 small and large test cases whose complete analysis is stored in an RDBMS and compared with previously verified results.

5.6  Performance

With CScout processing multi-million line projects as a single entity, time and space performance have to be kept within acceptable bounds, with increases at most linearly dependent on the size of the input. Although no fancy algorithms and data structures were used to achieve the CScout's scalability, extreme care was taken to adopt everywhere data structures and corresponding algorithms that would gracefully scale. This was made possible by the C++ STL library. For each data structure we simply chose a container that would handle all operations on its elements efficiently in terms of space and time. Thus, all data lookup operations are either O(1) for accessing data through a pointer indirection or at a vector's known index, or O(logN) for operations on sets and maps. These choices also allow the elegant and efficient expression of complex relationships, using STL functions like set_union, set_intersection, and equal_range. Up to now algorithmic tuning was required only once, to fix a pathological case in the implementation of the C preprocessor macro expansion [46].
The aggressive use of STL complicated CScout's debugging. Navigating STL data structures with gdb is almost impossible, because gdb provides a view of the data structures' implementation details, but not their high-level operations. This problem was solved by implementing a custom logging framework [47]: a lightweight and efficient construct that allowed us to instrument the code with (currently 200) log statements. As the following example shows, writing such a debugpoint statement is trivial: if (DP()) cout << "Gather args returns: " << v << endl; Each debugpoint can be easily enabled at runtime by specifying in a text file its corresponding file name and line number. The overhead of debugpoints can be completely disabled at compile time, but even when they get compiled, if none of them is enabled, their cost is only that of a compare and a jump instruction.

5.7  Project Scale

Implementing a tool of CScout's complexity proved to require considerable effort. CScout has been actively developed for five years, and currently consists of 27 KLOC. Most of the code is written in C++ with Perl being used to implement the CScout processing script generators csmake and cswc. Two more Perl scripts automatically extract from the source code the documentation for the SQL database schema and the reported error messages.
Eight class hierarchies allow for some inheritance-based code reuse. Ordered by decreasing number of classes in each inheritance tree, these cover C's types, graph rendering, the handling of user options, SQL output, query processing, C's tokens, metrics, and functions.
More importantly, CScout benefits from the use of existing mature open source components and tools: the btyacc backtracking variant of the yacc parser generator, the SWILL embedded web server library [29], the dot graph drawing program [17], and the mySQL and PostgreSQL relational database systems. The main advantages of these components were their stability, efficiency, and hassle-free availability. In addition, the source code availability of btyacc and SWILL allowed us to port them to various platforms and to add some minor but essential features: a function to retrieve an HTTP's request URL in SWILL, and the ability for multiple grammars to co-exist in a program in btyacc.

6  Applying CScout

Table 3: Details of representative processed applications.
awk Apache Free BSD Linux Solaris WRK Postgre SQL GDB
httpd kernel kernel kernel
Configurations 1 1 3 1 3 2 1 1
Modules (linkage units) 1 3 1,224 1,563 561 3 92 4
Files 14 96 4,479 8,372 3,851 653 426 564
Lines (thousands) 6.6 59.9 2,599 4,150 3,000 829 578 363
Identifiers (thousands) 10.5 52.6 1,110 1,411 571 127 32 60
Defined functions 170 937 38,371 86,245 39,966 4,820 1,929 7,084
Defined macros 185 1,129 727,410 703,940 136,953 31,908 4,272 6,060
Preprocessor directives 376 6,641 415,710 262,004 173,570 35,246 13,236 20,101
C statements (thousands) 4.3 17.7 948 1,772 1,042 192 70 129
Refactoring opportunities
Unused file-scoped identifiers 20 15 8,853 18,175 4,349 3,893 2,149 2,275
Unused project-scoped identifiers8 8 1,403 1,767 4,459 2,628 2,537 939
Unused macros 4 412 649,825 602,723 75,433 25,948 1,763 2,542
Variables that could be made static47 4 1,185 470 3,460 1,188 29 148
Functions that could be made static10 4 1,971 1,996 5,152 3,294 133 69
CPU time 0.81" 35" 3h 43'40" 7h 26'35" 1h 18'54" 58'53" 3'55" 11'13"
Lines / s 8,148 1,711 194 155 634 235 2,460 539
Required memory ( MB) 21 71 3,707 4,807 1,827 582 463 376
Bytes / line 3,336 1,243 1,496 1,215 639 736 840 1,086
Table 4: Performance measurements' hardware configuration.
Item Description
Computer Custom-made 4U rack-mounted server
CPU 4 × Dual-Core Opteron
CPU clock speed 2.4 GHz
L2 cache 1024k B (per CPU)
System Disks 2 × 36 GB, SATA II, 8 MB cache, 10k RPM, software RAID-1 (mirroring)
Storage Disks 8 × 500 GB, SATA II, 16 MB cache, 7.2k RPM, hardware RAID-10 (4-stripped mirrors)
Database Disks 4 × 300 GB, SATA II, 16 MB cache, 10k RPM, hardware RAID-10 (2-stripped mirrors)
RAID Controller 3ware 9550sx, 12 SATA II ports, 226 MB cache
Operating system Debian 5.0 stable running the 2.6.26-1-amd64 Linux kernel
CScout has been applied on tens of open source and commercial software systems running on a variety of hardware and software platforms [48,49,50]. The workspace sizes range from 6 KLOC (awk) to 4.1 MLOC (the Linux kernel). In all cases CScout was applied on the unmodified source code of each project. (CScout supports the original K&R C [28], ANSI C [1], and many C99 [25], gcc, and Microsoft C extensions.) Details of some representative projects can be seen in Table 4, while data of the hosting hardware appears in Table 5. The projects listed are the following.
The one true awk scripting language.1
Apache httpd
The Apache project web server, version 1.3.27.
The source code of the Free BSD kernel HEAD branch, as of 2006-09-18, in three architecture configurations: i386, AMD64, and SPARC64.
The Linux kernel, version, in its AMD64 configuration.
Sun's OpenSolaris kernel, as of 2007-07-28, in three architecture configurations: Sun4v, Sun4u, and SPARC.
The Microsoft Windows Research Kernel, version 1.2, into two architecture configurations: i386 and AMD64.
The PostgreSQL relational database, version 8.2.5.
The GNU debugger, version 6.7.
In the cases of awk, Apache httpd, GDB, WRK, and GDB the program family included one main project and a number of small peripheral ones (such as add-on modules or post-processing tools) sharing a few source or header files. The three Unix-like kernels (Free BSD, Linux, and OpenSolaris) were different: all consist of a main kernel and hundreds more run time-loadable modules providing functionality for various devices, filesystems, networking protocols, and additional features. Similarly, Postgre SQL included in its build numerous commands, tests, and dynamically-loadable localization libraries. With CScout all linkage units were processed as a single workspace, allowing browsing and refactoring to span elements residing in different linkage units.
Another interesting element of the analysis was the handling of different configurations for Free BSD, OpenSolaris, and WRK [48]. A kernel configuration specifies the CPU architecture, the device drivers, filesystems, and other elements that will be included in a kernel build. Through conditional compilation directives, the processed source code of one configuration can differ markedly from another. By processing multiple configurations as a single workspace CScout can present the source code as the union of the corresponding source code elements, and therefore ensure that the refactorings won't break any of the configurations processed.
The processing time required appears to be acceptable for integrating CScout in an IDE for small (e.g. up to 10 KLOC) projects. Memory requirements also appear to be tolerable for up to medium sized workspaces (e.g. up to 100 KLOC) for a typical developer workstation. Large workspaces will require a high-end modern workstation or a server equipped with multi-gigabyte memory and a 64-bit CPU. The time required to write-back the refactored files is negligible. For instance, saving the 96 files of Apache httpd (60 KLOC) with all identifiers replaced with a unique random name required in our configuration 331 ms-about 1% of the total processing time. However, if the user opts to check rename refactorings for clashes with other identifiers, then a complete reprocessing of the source code is required; this takes about the same time as the original processing.
Up to now the most useful application of CScout has been the cleanup of source code, performed by removing unused objects, macros, and included header files, and by reducing the visibility scope of defined objects. This is an easy target, since all it entails is letting an editor automatically jump to each affected file location by going through CScout's standardized warning report.
To test CScout's identifier analysis we added an option in the refactoring engine to rename all the writable identifiers into new, mechanically derived, random identifier names. We applied this transformation to the apache HTTP server source code; the resulting version compiled and appeared to work without a problem. This source code transformation can be applied on proprietary code to derive an architecture-neutral software distribution format: a (minimally) obfuscated version of the source code, which, like compiled code, unauthorized third parties cannot easily comprehend and modify, but, unlike compiled code, can be configured and compiled on each end-user platform to match its processor architecture and operating system.

7  Lessons Learned

The main lessons learned from CScout's development are the value of end-to-end whole-workspace analysis of C source code, and the many practical difficulties of dealing with real-world C software. Researchers can apply these lessons by adopting a similar depth of analysis, such as the analysis already done in the LLVM compiler infrastructure project [31]. Alternatively, researchers at the forefront of tool technology, can save a lot of effort and pain by steering their energy toward more tractable languages, like Java. Furthermore, commercial tool builders should plan and budget for the difficulties we outline.
The operation of program analysis and transformation tools can be characterized as sound when the analysis will not generate any false positive results, and as complete when there are not missing elements in the results of the analysis. The analysis performed by CScout over identifier equivalence classes is in the general case sound, because it follows precisely the language's semantic rules. The incompleteness of the produced results stems from three different complications; addressing those with heuristics would result in an analysis that would no longer be sound. Predictably, these complications in our scheme arise from preprocessing features.
Unifying undefined macros   In the absence of a shared #undef directive two undefined macros with the same name can only be unified into a single identifier through a heuristic rule that considers them to be referring to the same entity. This is typically a correct assumption, because testing through undefined macros is used for configuring software through a carefully managed namespace, with identifiers such as HAS_FGETPOS and HAS_GETPPID.
Coverage of macro applications   Dealing with function-like macros whose application does not cover all possible cases needed for semantically correct refactoring can be problematic. Consider the first case in Section 2.1. If the code does not apply get_block_len on at least one element of type disk_block and one of type mem_block CScout has no way to know that all three instances of len are semantically equivalent and should be renamed in concert.
Handling conditional compilation   In practice, this issue has caused the greatest number of problems. Conditional compilation results in code parts that are not always processed. Some of them may be mutually exclusive, defining e.g. different operating system-dependent versions of the same function. The problem can be handled with multiple passes over the code, or by ignoring conditional compilation commands. This process may need to be guided by hand, because conditionally compiled code sections are often specific to a particular compilation environment. When processing the Free BSD kernel we used both approaches: a special predefined kernel configuration target named LINT to maximize the amount of conditionally compiled code that the configuration and processing would cover, and a separate pass for each of the three supported processor architectures. Yet, even this approach did not adequately cover the complete source code, as evidenced by an aborted attempt to remove header files that appeared to be unused.
Another problem we encountered when applying CScout in realistic situations concerned language extensions. The first version of CScout supported the 1989 version of ANSI C [1] and a number of C99 [25] extensions. In practice we found that CScout could not be applied on real-world source code without supporting many compiler-specific language extensions. Even programs that were written in a portable manner included platform-specific header files, which used many compiler extensions, and could therefore not be processed by a tool that did not support them. This was a significant problem for a number of reasons.
Finally, we have yet to find a practical way to handle meta programming approaches where a project-internal domain specific language ( DSL) is used to produce C code. In such cases, changes to the C source code may need to be propagated to the DSL code, or even to the DSL compiler. Integrating the support into CScout, as we have done for yacc, solves the problem for one specific case, but this approach cannot scale in a realistic manner. Currently, identifiers residing in an automatically generated C file can be easily tagged as "read-only", but this will restrict the number of identifiers that can be renamed.

8  Conclusions

We have plans to extend CScout in a number of directions. One challenging and worthwhile avenue is support for the C++ language and object-oriented refactorings.
The web front-end is beginning to show its age. It should probably be redesigned to use of AJAX technologies, communicating with the CScout engine through XML requests. This interface would also allow the implementation of a more sophisticated testing framework. Queries can be made considerably more flexible by allowing the user to specify them in an embedded scripting language, like Lua [24]. Such a change would probably also require the provision of an asynchronous mechanism for aborting expensive queries. An alternative approach would be to provide a built-in SQL interface, perhaps through virtual tables of an embedded database, like SQLite [37].
Currently, many URLs of the web front end are fragile, breaking across CScout invocations or when the web-front end source code changes. These URLs can be made more robust by expressing them at a higher level of abstraction. Logging of CScout's HTTP requests can provide research data on its actual use.
Source code browsing can also be improved. The source code views can be enhanced through the use of configurable syntax coloring and easier navigation to various elements. An interface can be provided for showing identifiers shared between two files. Refactoring opportunities can be pointed out by identifying bad smells in the code. These can be located through the judicious provision of some key metric-based queries, and through the automatic detection of duplicated code [32].
CScout could support file names as first class citizens. This should allow the renaming of a file name, correcting all references to it in include directives. Furthermore, the web front-end should hyperlink file names appearing in include directives. On the same subject, CScout could provide a header refactoring option to support the style guideline that requires each included file to be self-sufficient (compile on its own) by including all the requisite header files [55,p. 42].
CScout's support for DSLs can be improved along a number of lines. For one, csmake should also support yacc invocations. More generally, it would probably be worthwhile to provide CScout with an option to perform best-effort identifier substitutions in files it can't parse. These substitutions would be performed simply by matching whole words; developers will enable this option when they are reasonable confident that there are no spurious matches of the identifiers they rename in DSL files. In the future, ubiquitous accurate file and offset tagging of the automatically created source code, in a way similar to the #line directives currently emitted by generators such as lex and yacc, may offer a more robust solution.
The application of CPU and memory resources toward the analysis of large program families written in C is an effective approach that yields readily exploitable refactoring opportunities in legacy code. CScout has already been successfully applied on a wide range of projects for performing modest, though not insignificant, refactoring operations. Our approach can be readily extended to cover other preprocessed languages like C++. Open issues from a research perspective are the automatic identification and implementation of more complex refactoring operations, increasing the accuracy of dependency graphs by reasoning about function pointers [35], the production of source code views for given macro values, and the efficient maximization of code coverage.

Acknowledgements and Tool Availability

We would like to thank the anonymous reviewers for their many excellent suggestions to improve this paper and CScout. The following people have helped over the years the development of CScout with advice, comments, and feature requests: Walter Briscoe, Wilko Bulte, Munish Chopra, Georgios Gousios, Poul-Henning Kamp, Kris Kennaway, Alexander Leidinger, Sandor Markon, Marcel Moolenaar, Richard A. O'Keefe, Igmar Palsenberg, Wes Peters, Dave Prosser, Jeroen Ruigrok van der Werven, Remco van Engelen, and Peter Wemm. The tool, its documentation, and representative examples are available at CScout currently runs under the Free BSD, Linux, Mac OS X, Microsoft Windows, and Solaris operating systems under several 32 and 64-bit architectures. The freely-downloadable version of CScout can be used on open-source code; the supported commercial version is licensed for use on proprietary code, and includes the obfuscation and SQL back-ends.


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