The aim is to allow the calling of native functions by other programming languages, passing complex data types to those functions, keeping memory from being inappropriately freed, inheriting object classes across languages, etc. The programmer writes an interface file containing a list of C/C++ functions to be made visible to an interpreter. SWIG will compile the interface file and generate code in regular C/C++ and the target programming language. SWIG will generate conversion code for functions with simple arguments; conversion code for complex types of arguments must be written by the programmer. The SWIG tool creates source code that provides the glue between C/C++ and the target language. Depending on the language, this glue comes in two forms:
a shared library that an extant interpreter can link to as some form of extension module, or
a shared library that can be linked to other programs compiled in the target language.
SWIG is not used for calling interpreted functions by native code; this must be done by the programmer manually.
Example
SWIG wraps simple C declarations by creating an interface that closely matches the way in which the declarations would be used in a C program. For example, consider the following interface file: %module example %inline %
define STATUS 50
define VERSION "1.1"
In this file, there are two functions and, a global variable, and two constants and. When SWIG creates an extension module, these declarations are accessible as scripting language functions, variables, and constants respectively. In Python: >>> example.sin 0.141120008 >>> example.strcmp -1 >>> print example.cvar.Foo 42 >>> print example.STATUS 50 >>> print example.VERSION 1.1
Purpose
There are two main reasons to embed a scripting engine in an existing C/C++ program:
The program can then be customized far faster, via a scripting language instead of C/C++. The scripting engine may even be exposed to the end user, so that they can automate common tasks by writing scripts.
Even if the final product is not to contain the scripting engine, it may nevertheless be very useful for writing test scripts.
There are several reasons to create dynamic libraries that can be loaded into extant interpreters, including:
Provide access to a C/C++ library which has no equivalent in the scripting language.
Write the whole program in the scripting language first, and after profiling, rewrite performance critical code in C or C++.
SWIG was a successful participant of Google Summer of Code in 2008, 2009, 2012. In 2008, SWIG got four slots. Haoyu Bai spent his summers on SWIG's Python 3.0 Backend, Jan Jezabek worked on Support for generating COM wrappers, Cheryl Foil spent her time on Comment 'Translator' for SWIG, and Maciej Drwal worked on a C backend. In 2009, SWIG again participated in Google Summer of Code. This time four students participated. Baozeng Ding worked on a Scilab module. Matevz Jekovec spent time on C++0x features. Ashish Sharma spent his summer on an Objective-C module, Miklos Vajna spent his time on PHP directors. In 2012, SWIG participated in Google Summer of Code. This time four out of five students successfully completed the project. Leif Middelschulte worked on a C target language module. Swati Sharma enhanced the Objective-C module. Neha Narang added the new module on JavaScript. Dmitry Kabak worked on source code documentation and Doxygen comments.
Alternatives
For Python, similar functionality is offered by SIP and Boost's Boost.python library.
