Software module clustering is an important and challenging problem in software engineering. It is widely believed that a well-modularized software system is easier to develop and maintain. Typically, a good module structure is regarded as one that has a high degree of cohesion and a low degree of coupling. Automated techniques are used for suggesting software clustering, delimiting boundaries between modules that maximize cohesion while minimizing coupling. Implications of software modularization are considered on many software engineering concerns. Badly modularized software is widely regarded as a source of problems for comprehension, increasing the time for ongoing maintenance and testing. Cohesion and coupling are used to assess module structure. Software module clustering is the problem of automatically organizing software units into modules to improve program structure. There has been a great deal of recent interest in search-based formulations of this problem in which module boundaries are identified by automated search, guided by a fitness function that captures the twin objectives of high cohesion and low coupling in a single-objective fitness function. The system uses multi objective model for module clustering process. The multi-objective approach produces significantly better solutions than the single-objective approach. The proposed system is designed to improve the module clustering process. Multi objective functions with dynamic criteria selection model is used in the system. Module size and communication bandwidth features are used in the objective functions. Feature location is also used in the multi objective functions.
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