Optimizing Software Clustering using Hybrid Bee Colony Approach

Abstract

Maintenance of software is the most expensive and complicated phase of the software development lifecycle. It becomes more cumbersome if the architecture of the software system is not available. Search-based optimization is found to be a technique very efficient in recovering the architecture of such a system. In this paper, we propose a technique which is based on a combination of artificial honey bee swarm intelligent algorithm and genetic algorithm to recover this architecture. In this way, it will be very helpful to software maintainers for efficient and effective software maintenance. In order to evaluate the success of this approach, it has been applied to a few real-world module clustering problems. The results we obtained support our claim that this approach produces architecture significantly better than the existing approaches.

Keywords: Artificial bee colony algorithm, Genetic algorithm, Software clustering, Software Modularization.