An Innovative Approach To Software Modularization Based On The Artificial Fish Swarm Algorithm

It gets more and more expensive to maintain the complete software system as time goes on since the software architecture grows more complicated and the software code is more difficult to understand. This issue may be solved by taking the essential parts out of the source code and organizing them into the appropriate subsystems. Hierarchy-based partitioning is more complex and less successful in solving issues with huge software modules. In order to do this, this work suggests an innovative artificial fish swarm method as a meta-heuristic to solve the software module clustering problems (SMCPs) and presents a mutation operator and a local search strategy to handle the premature situation. The performance of the suggested method is assessed on a range of real-world software systems by comparison to the most recent meta-heuristic methods. The outcomes of the experiments demonstrate that the suggested methodology works better than those of other methods.