Application of AO* Algorithm in Recognizing the Optimum Refactoring sequence for examining the effect on Maintainability: An Empirical Study

摘要

Bad smells are an indication of deeper problems in source code that need to be identified in order to decrease the accumulation effect in the SDLC which implies that at each stage smells may transform into bugs, faults or even failure of the working software resulting in loss of efforts. Refactoring, on the other hand, helps in removal of smells without affecting the external attributes of the software. Nowadays, researchers are focusing on the detection of optimum refactoring sequences well in advance so that software maintenance cost can be reduced and subsequently the efforts may minimize. In this paper, authors have identified a total of eleven bad smells present in the critically affected class selected on the basis of prioritization technique. After that, an attempt have been made to find optimum refactoring technique sequence using AO* algorithm which will eliminate identified bad smells and thereby helping the team to complete project within budget and time constraints. The obtained results showed that there is a considerable amount of improvement in maintainability value after applying optimum refactoring sequence on every class. This approach will help researchers and practitioners to use heuristic algorithms in finding the sequences in the early phase and hence maintain the source code under surveillance.