The essence of the VivaCore code analysis library

The essence of the VivaCore code
analysis library
Authors: Andrey Karpov, Evgeniy Ryzhkov

Date: 09.01.2008

Abstract
The article tells developers about VivaCore library, preconditions of its creation, its possibilities,
structure and scope of use. This article was written simultaneously with the development of VivaCore
library and that's why some of the details of the final realization may differ from the features described
here. But this won't prevent the developers from getting acquainted with the general work principles of
the library, mechanisms of analysis and processing of C and C++ source code.

Introduction
VivaCore is an open source library for work with C and C++ code. The library is intended for realizing
code refactoring systems, systems of static and dynamic analysis, systems of transformation and
optimization, systems of language extensions code, subsystems for syntax highlighting, systems for
building documentation on the code and other similar tools on its basis.

The idea of the library appeared when our team was creating the Viva64 static code analyzer [1]. The
Viva64 tool is intended for diagnosing errors in C/C++ programs related to the peculiarities of code
migration on 64-bit Windows systems.

During the process of Viva64 development our team faced the absence of open libraries convenient for
realization of such projects. OpenC++ (OpenCxx) library [2] was chosen as the basis and we were
satisfied with our choice. But during the development of the static analyzer our team made a lot of
corrections and improvements in OpenC++ library. And now when the development of the first versions
of Viva64 is over we would like to offer thir-party developers our remade variant of OpenC++ library
which we called VivaCore. We think that the changes we've made can help a lot to the developers who
are going to begin the development of software products in the sphere of analysis or processing of
C/C++ code.

The license of VivaCore library allows you to use, copy, distribute and modify it in binary form or as an
original code both for commercial and non-commercial use free, without any payments to the library's
authors. One should only mention the authors of the original libraries (OpenC++ and VivaCore).

You can download VivaCore here - http://www.viva64.com/vivacore-download.php

The difference between VivaCore library and OpenC++ library
The main difference between VivaCore and OpenC++ is that VivaCore is a living project and it continues
to gain functionality. Unfortunately, OpenC++ library hasn't been developed for a long time. The latest
change in the library was in 2004. And the latest change related to support of new keywords was in

2003. This correction was an unlucky try to add wchar_t data type, which caused adding about five
errors of different types.

It is evident why we introduce our own library and didn't make changes in OpenC++. We don't have
enough free resources for that. A lot of modifications were carried out and it is a very difficult task to
introduce all the changes into OpenC++. Many changes are of a specific type and may not fit the general
ideology of OpenC++ library and that's why they will need additional adaptation. All this makes the task
of OpenC++ library update very resource-intensive, and from our viewpoint unreasonable.

Let's list the key features implemented in the VivaCore library in comparison with OpenC++.

1. The classical C language is supported. A different set of tokens is used and it gives an
opportunity to name the variables as 'class' or declare a function in the following old C style:

PureC_Foo(ptr)
char *ptr;
{
...
}
2. Great work was carried out to support the peculiarities of C++ syntax used in the VisualStudio
2005/2008 environment. For example, the library processes keywords __noop, __if_exists,
__ptr32, __pragma, __interface, etc.
3. Some new keywords and other constructions included into new language standards are
supported. In particular we have added support for the register keyword and evocation of
templated function with the use of the template word: object.template foo<int>();.
4. Computation of literal constants has been implemented.
5. The library is adopted and optimized for the work on 64-bit systems with the help of Viva64
code analyzer.
6. A lot of errors and defects are corrected. An example is support for string literals separated by
space (const wchar_t *str = L"begin" L"end") or divided into two strings by a slash:
const char *name = "Viva
Core";
Another example is correct processing of expressions like "bool r = a < 1 || b > (int) 2;" which
are treated as templates by OpenC++.
And so on.
7. The mechanism of primary preprocessing of the original text is created. This helps to implement
some specific code modifications.

In the nearest future we are planning to implement the following features in the VivaCore library:

its own preprocessor (on the basis of The Wave C++ preprocessor library), instead of an external one
(for example, Visual Studio preprocessor). This will allow avoiding some errors in positioning by code
line numbers when showing the compilation errors, and also provide better control over the process of
code parsing and analysis;

support for coding of complex types, which occupy more than 127 symbols in the coded form;

a simple application demonstrating the basic features of VivaCore library.

We should mention that despite all the listed differences between OpenC++ and VivaCore libraries, they
have a lot in common and that's why the documentation on OpenC++ doesn't lost its relevance. Our
team will try to pay attention to the documenting of VivaCore library. But as far as this documentation
will cover the differences and new abilities realized in VivaCore library, it will be useful to get acquainted
with the OpenC++ documentation in any case.

The scope of use of VivaCore library
VivaCore library may be of interest for companies and organizations that are creating or planning to
create tools for source code processing. Of course it is impossible to list all the acceptable spheres and
methods of use, but we'll list some directions in order to show VivaCore from different viewpoints. In
brackets we put the products related to every solution class. One shouldn't think that they are realized
on the basis of VivaCore - it is just examples of solutions. So, with the help of VivaCore one can develop
solutions for:

1. code refactoring (VisualAssist, DevExpress Refactoring, JetBrains Resharper);
2. static analysis of general and specialized purposes (Viva64, lint, Gimpel Software PC-Lint,
Microsoft FxCop, Parasoft C++test);
3. dynamic code analysis (Compuware BoundsChecker, AutomatedQA AQTime);
4. C/C++ languages extensions, including support for metaprogramming (OpenTS);
5. automated code testing (Parasoft C++test)
6. code transformation, for example, for optimization;
7. syntax highlighting (Whole Tomato Software VisualAssist, any up-to-date development
environment);
8. code documentation building (Synopsis, Doxygen);
9. high-precision detection of changes in the original code or analysis of changes evolution;
10. search of duplicated code on the level of grammatical language constructions;
11. metrics calculation (C and C++ Code Counter - CCCC);
12. support for coding standards (Gimpel Software PC-Lint);
13. tools simplifying the code migration on other program and hardware platforms (Viva64);
14. automatic code generation;
15. code visualizers, systems of building dependency graphs (Source-Navigator);
16. code formatting (Ochre SourceStyler).

For more information on code parsing technology please see the fundamental book on compilers [3].
We also recommend you to get acquainted with principles of program analysis [4].

One shouldn't mix up VivaCore and professional multifunctional parsers for C/C++ code. If a user needs
a front-end code parser with full support for the modern C++ language standard, which allows creating
his own compiler for a specific platform, he should pay attention to GCC or other expensive commercial
solutions. For example, such solutions are offered by Semantic Designs [5].

But if a company develops a tool requiring classical analysis of C/C++ code, then the rational solution is
the use of a convenient and open source library, that is of VivaCore.

Basic terms
Before we begin to speak about VivaCore library in details, let's recollect some terms which will be used
during the description.

Preprocessing - a mechanism browsing the incoming ".c/.cpp" file, executing preprocessor's directions
in it, including the content of other files pointed in #include directions into it etc. As a result, a file
appears which doesn't contain preprocessor's directions, all the used macros are open, instead of
#include directions the content of the corresponding files is placed. The file containing preprocessing
results usually has the ".i" suffix. The result of preprocessing is called a translation unit.

Syntax analysis (parsing) - the process of analysis of the incoming sequence of symbols to parse the
grammatical structure. Usually syntax analysis is divided into two levels: lexical analysis and grammatical
analysis.

Lexical analysis - the procedure of incoming sequence of symbols processing to get the sequence of
symbols called lexemes (or "tokens") at the output. Each lexeme can be presented as a structure
containing the lexeme type and, if necessary, the corresponding meaning.

Grammatical analysis (grammatical parsing) - the process of comparison of the linear sequence of
lexemes (words, tokens) of the language with its formal grammar. The result is usually the derivation
tree or the abstract syntax tree.

Abstract Syntax Tree (AST) - a finite, marked, oriented tree in which the inner nodes correspond to the
programming language operators, and the leaves to the corresponding operands. So, the leaves are
empty operators and they are just variables and constants. The AST differs from the Derivation Tree (DT)
in that way that there are no nodes for those syntax rules which don't influence the program's
semantics. The classical example of such absence is the grouping brackets, as in the AST the grouping of
operands is defined by the tree structure explicitly.

Metaprogramming - development of programs which create other programs as the result of their work
or change and complement themselves during execution. In the VivaCore library metaprogramming is
the possibility of extending the syntax and functionality of C/C++ language with the purpose of creating
one's own programming language. Metaprograms created on this programming language may be then
translated into C/C++ code with the use of VivaCore and compiled by an external compiler.

Tree traversal - traversal of all the nodes and leaves of the syntax tree to gather information of different
kinds, analyze and modify the tree.

The general structure of VivaCore library
The general functional structure of VivaCore library is shown on figure 1. At this moment the library is
intended for the full integration with the user's application and is represented as a set of original source
code.

Figure 1. General functional structure of VivaCore library.

We suggest you examine the library's functional blocks in the order in which they process the incoming
original source code as it is shown on figure 2. We'll see what actions a functional block performes, what
information it allows to get and how it can be modified for specific purposes.

Figure 2. The sequence of code processing.

1) Input subsystem
VivaCore library may correctly process only the original C/C++ code processed by the preprocessor.
Further, the possibility of using the preprocessor from The Wave C++ preprocessor library is taken into
consideration, but it won't be implemented in the first version of the VivaCore library. To get a
preprocessed file one may use a compiler (for example, Microsoft Visual C++) and get a processed file
which usually has the "i" extension.

In particular cases unprocessed C/C++ files may be input, but in this case one should work with VivaCore
not further than on the level of splitting the file into lexemes. This will be enough for calculating metrics
or for other purposes. But one shouldn't try to build and analyze the Parse Tree, because the result,
most probably, will be unsuitable for processing.

Having the preprocessed code, the user may transfer it to the input subsystem as a file or memory
buffer. The purpose of the input subsystem is to arrange the data in the inner structures of VivaCore
library. The input system also takes configuration data, which states which libraries should be treated as
system and which libraries should be treated as user's ones.

See in the code: VivaConfiguration, ReadFile.

2) Preprocessor subsystem.
We would like to point out that this subsystem doesn't provide code preprocessing in its classical
meaning. As it has been mentioned before, it is the preprocessed code that should be input into
VivaCore library. The subsystem under consideration designed for the following tasks:

• Splitting the source code into strings and classifying them into two logical groups. The first group
is the system code (the code of the compiler's libraries and so on). The second group includes
the user's code which is of interest for analysis. As a result, while developing a static analyzer,
the user has an opportunity to decide whether he will analyze the system libraries code.
• Specialized modification of the program text in memory. An example is the removal of
constructions of a concrete development environment and of constructions not related to
C/C++ languages from the code. For example, Viva64 analyzer removes such key constructions
as SA_Success or SA_FormatString which is present in Visual Studio's header files.

See in the code: VivaPreprocessor, CreateStringInfo, IsInterestingLine, GetLineNumberByPtr,
PreprocessingItem, SkipUninterestingCode.

3) Lexer
So, we've come to those levels of data processing which are of practical interest for developers. Having
parsed the code into lexemes, the user has an opportunity to calculate metrics, implement a specific
algorithm of syntax highlighting in different applications.

VivaCore lexical analyzer parses the source code into a set of objects of Token type (see the Token.h file)
which contain information about the lexeme type, its location in the program text and its length. The
lexeme types are enumerated in the tokennames.h file. Examples of lexeme types:

CLASS - "class"-keyword

WCHAR - "wchar_t"-keyword

The user can extend the set of lexemes if necessary. It may be needed when it is necessary to support
the specific syntax of a specific language imlementation or when developing one's own language
extension.

When adding lexemes it is necessary to define them in the tokennames.h file and add them into the
"table" or "tableC" tables in Lex.cc file. The former table is intended for processing of C++ files and the
latter is for C files. This is natural, as the set of lexemes in C and C++ languages is different. For example,
there is no CLASS lexeme in C language, because the word "class" is not a keyword in C and can indicate
a name of a variable.

When you add new lexemes you should not forget to modify the isTypeSpecifier,
optIntegralTypeOrClassSpec and other functions. We recommend you perform search for a keyword
which has similar meaning and modify all the fragments of VivaCore code in which the keyword occurs.

The set of lexemes can be represented both as a simple array and as a file. Lexemes are kept in the
tokens_array in the Lex class. You can either get the entire array or separate lexemes using the
GetToken, LookAhead, CanLookAhead functions.

The user can get lexemes in unstructured text form or in the following formatted form using the
DumpEx function:

258 LC_ID 5
258 lc_id 5
91 [ 1
262 6 1
93 ] 1
59 ; 1
303 struct 6
123 { 1
282 char 4
42 * 1
258 locale 6
The user can also export the lexemes in XML file format.

See in the code: Token, Lex, TokenContainer.

4) Parser
The parser is intended for building the derivation tree that may be further subjected to analysis and
transformation. Pay attention that the parser of VivaCore library builds not an abstract syntax tree but
just the derivation tree. This allows user to implement support for metaprogram constructions, which
may be added by the user into C or C++ language. If the user will urgently need to work just with the
abstract syntax tree, we hope it won't be difficult to improve the analyzer so that it could traverse the
whole derivation tree and remove nodes and leaves that are not used in abstract syntax.

Building of the tree in VivaCore library is performed in functions of the Parser class. Nodes and leaves of
the tree are objects whose classes were inherited from the NonLeaf and Leaf base classes. A partial
hierarchy of classes used to present the tree is shown on figure 3.

Figure 3. A part of hierarchy of classes used for building the derivation tree.

As it is seen from the figure, Ptree class is a base for all the others and serves as a single interface to
work with other classes. The Ptree class contains a set of virtual functions which should be implemented
functions
in descendant classes. For example, the "virtual bool IsLeaf() const = 0;" function is implemented in the
NonLeaf and Leaf classes. Actually the classes implement only this function and they are designed to
make the hierarchy of classes more logical and nice.
rarchy

As far as the work with the tree occupies a significant part of the library, Ptree has a large set of
functions to work with the tree nodes. These functions are presented as analogs of list proressing
functions in the Lisp language. Here are some of them: Car, Cdr, Cadr, Cddr, LastNth, Length, Eq.
he

To get the general idea of the parser's workflow let's examine a derivation tree which will be built from
the following code:

int MyFoo(const float value)

{

if (value < 1.0)

return sizeof(unsigned long *);

return value * 4.0f < 10.0f ? 0 : 1;

}

Unfortunately, it is impossible to show the entire derivation tree, that's why we'll show it in parts on
figures 4.1-4.4.

Figure 4.1. Color indication of the semantic tree nodes.

Figure 4.2. Presentation of the function header.

Figure 4.3. Presentation of the function body.

Figure 4.4. Presentation of the function body.

We should mention one more important component of the analyzer's work. This is the gathering of
information about types of various objects (functions, variables etc), that is performed in the Encoding
class. Information about a type is represented in the form of a specifically coded string. You may
form
examine the format of this string in the Encoding.cc file. The library also contains a special class,

TypeInfo. This class allows getting and modifying types information. For example, it is easy to identify
the type of the processed element using functions like IsFunction, IsPointerType, IsBuiltInType.

The addition of new types of nodes and leaves is a difficult task and cannot be observed in this
overview. The rational solution is to choose one of the classes, for example, the PtreeExprStatement
class and examine all the places in the code where instances of this class are created, processed, and so
on.

The tree that we finally get may be saved in a ".c /.cpp" file, but it is pointless. It will be reasonable after
changing the derivation tree that can take place on the following steps. By saving the tree as a program
source code at the moment we'll have the same code that we had on the input. However, this might be
useful for testing changes introduced into lexer and parser.

The possibility to save the tree for further processing in any user-implemented format is more
interesting. An example is the following textual presentation of the program code, described earlier:

PtreeDeclaration:[

0

NonLeaf:[

LeafINT:int

]

PtreeDeclarator:[

Leaf:MyFoo

Leaf:(

NonLeaf:[

NonLeaf:[

NonLeaf:[

LeafCONST:const

NonLeaf:[

LeafFLOAT:float

]

]

PtreeDeclarator:[

Leaf:value

]

]

]

Leaf:)

]

[{

NonLeaf:[

PtreeIfStatement:[

LeafReserved:if

Leaf:(

PtreeInfixExpr:[

LeafName:value

Leaf:<

Leaf:1.0

]

Leaf:)

PtreeReturnStatement:[

LeafReserved:return

PtreeSizeofExpr:[

Leaf:sizeof

Leaf:(

NonLeaf:[

NonLeaf:[

LeafUNSIGNED:unsigned

LeafLONG:long

]

PtreeDeclarator:[

Leaf:*

]

]

Leaf:)

]

Leaf:;

]

]

PtreeReturnStatement:[

LeafReserved:return

PtreeCondExpr:[

PtreeInfixExpr:[

PtreeInfixExpr:[

LeafName:value

Leaf:*

Leaf:4.0f

]

Leaf:<

Leaf:10.0f

]

Leaf:?

Leaf:0

Leaf::

Leaf:1

]

Leaf:;

]

]

Leaf:}

}]

]

This format is shown only as an example. In practice you are likely to save more information and in a
more convenient format - for example, the XML format.

See in the code: Parser, Ptree, Leaf, NonLeaf, Encoding, TypeInfo, Typeof, PtreeUtil.

5) The tree traversal
The step of the tree traversal which is implemented in the Walker, ClassWalker, ClassBodyWalker
classes is the most interesting for those who develop static code analyzers (see the [6] book for detailed

introduction in this sphere) or systems for building documentation on source code. The tree traversal
may be performed several times. This allows creating systems for source code modification in several
traversals or providing analysis considering the information gathered in previous tree traversals.

The Walker class is designed for traversing basic C/C++ constructions.

The ClassWalker class is inherited from the Walker class and adds functionality concerning the specific
features of classes, presented in the C++ language.

When it is necessary to traverse the class body, objects of the ClassBodyWalker class are created and
used for a short time.

If no changes are introduced into the VivaCore library all the tree elements will be simply traversed. The
tree won't be changed.

If a user implements functionality that will modify tree nodes the library may rebuild the tree. As an
example, let's examine the code translating unary operations:

Ptree* ClassWalker::TranslateUnary(Ptree* exp)

{

using namespace PtreeUtil;

Ptree* unaryop = exp->Car();

Ptree* right = PtreeUtil::Second(exp);

Ptree* right2 = Translate(right);

if(right == right2)

return exp;

else

return

new (GC_QuickAlloc)

PtreeUnaryExpr(unaryop, PtreeUtil::List(right2));

}

Please note that if the tree is changed while translating the expression staying on the right of the unary
operation, the node of the unary operation will be changed (rebuilt) as well.

To make it clearer, let's examine this example in details.

The processing of the node that corresponds to a unary operation over some expression and that has
PtreeUnaryExpr type begins. The first element in the list that is extracted with the help of exp->Car()
operation, is the unary operation itself. The second element extracted with the help of
PtreeUtil::Second(exp) is the expression with which the unary operation is performed.

The translation of the expression is performed and the result is assigned to the right2 variable. If this
address differs from the current one it means that the expression was changed. In this case a new object
of the PtreeUnaryExpr type is created. This object will be returned by the TranslateUnary function.
Otherwise nothing was changed and the function returns the object, which was on the input.

If the user needs to gather information while traversing the tree or modify the tree, it is natural that the
corresponding new classes should be inherited from the ClassWalker and ClassBodyWalker classes.

Let's examine an example taken from the Viva64 static analyzer where specialized analysis takes place
while passing the "throw" operator.

Ptree* VivaWalker::TranslateThrow(Ptree *p) {

Ptree *result = ClassWalker::TranslateThrow(p);

Ptree* oprnd = PtreeUtil::Second(result);

if (oprnd != NULL) { //if oprnd==NULL then this is "throw;".

if (!CreateWiseType(oprnd)) {

return result;

}

if (IsErrorActive(115) &&

!ApplyRuleN10(oprnd->m_wiseType.m_simpleType))

{

AddError(VivaErrors::V115(), p, 115);

}

}

return result;

}

In the beginning the ClassWalker::TranslateThrow(p) method call performs standard node translation.
After that the necessary analysis is performed. Simple and elegant.

Speaking about the tree traversal, we should also mention a very important class, the Environment
class, which gathers information on types of various objects in different scopes.

Here is an example demonstrating usage of the Environment class represented by the env object, for
getting the type of the declTypeInfo object:

TypeInfo declTypeInfo;

if (env->Lookup(decl, declTypeInfo)) {

...

}

See in the code: AbstractTranslatingWalker, Walker, ClassWalker, ClassBodyWalker, Class, Environment,
Bind, Class, TemplateClass.

6) Support for metaprogramming
Metaprogramming is based on the approach to code generation when the program code is not written
manually but is generated by a generating program on the basis of another, simpler program. This
approach becomes reasonable if various additional rules are produced during development (high-level
paradigms, fulfillment of the requirements of external libraries, stereotype ways of realization of
particular functions). At this point, a part of the code loses its substantial sense and becomes just a
mechanical fulfillment of the rules. When this part becomes considerable, a thought may appear to
define only the substantial part manually and let all the rest be added automatically. This is the purpose
of the generator.

Sometimes this generator is necessary for translation of an invented language into C/C++ operators.
VivaCore has a mechanism for convenient creation of C/C++ extensions on the basis of metaobjects. It is
possible to change or build new syntax trees in order to save them into C/C++ code.

You may get acquainted with the metaprogramming paradigm and ways of using metaobjects in detail
in the documentation on OpenC++ library.

See in the code: Metaclass.

7) Saving the results
As it has already been said, you can save the necessary information on any step of the original source
code processing inside VivaCore library. We have also mentioned that the derived and changed
derivation tree may be saved in the form of source code or in any other format. So, we won't repeat it
once more. It is also clear that one may gather the necessary information, for example during static
analysis or calculation of metrics in different ways and that's why it's senseless to enumerate the means
of implementation.

Let's say only some words about the use of XML format that was mentioned more than once in this
article. XML is a textual format intended for keeping of structured data, for exchange of information
between programs and different subsystems of information processing. XML is a simpler subset of SGML
language.

We use XML for exporting various information hoping that it will make it easier for third-party
developers to use VivaCore in their program developments in other programming languages. For
example, it will be very convenient for C# programs. And what is not less important, XML as a data
format make it simpler to structure the information and present it in a form familiar to a programmer.

Conclusion
We understand that after reading this article the number of new questions which may appear, is greater
than the number of answers received. But the good news is that our Viva64.com team is always ready to
communicate, to discuss appearing questions and give recommendations on the use of VivaCore. Write
us!

References
1. Evgeniy Ryzhkov. Viva64: what is it and for whom is it meant? http://www.viva64.com/art-1-2-
903037923.html
2. OpenC++ library. http://www.viva64.com/go.php?url=16
3. Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman. Compilers: Principles, Techniques,
and Tools (2nd Edition). Addison Wesley, 2006, 1000 pages.
4. Flemming Nielson, Hanne R. Nielson, Chris Hankin. Principles of Program Analysis. Springer,
2004, 452 pages.
5. Semantic Designs site. http://www.viva64.com/go.php?url=19
6. Patrick Cousot. Static Analysis. Springer, 2001, 450 pages.

The essence of the VivaCore code analysis library

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