A Brief Review on Multi-objective Software Refactoring and a New Method for Its Recommendation

Abstract

Software refactoring is a commonly accepted means of improving the software quality without affecting its observable behaviour. It has gained significant attention from both academia and software industry. Therefore, numerous approaches have been proposed to automate refactoring that consider software quality maximization as their prime objective. However, this objective is not enough to generate good and efficient refactoring sequences as refactoring also involves several other uncertainties related to smell severity, history of applied refactoring activities and class severity. To address these concerns, we propose a multi-objective optimization technique to generate refactoring solutions that maximize the (1) software quality, (2) use of smell severity and (3) consistency with class importance. To this end, we provide a brief review on multi-objective search-based software refactoring and use a multi-objective spotted hyena optimizer (MOSHO) to obtain the best compromise between these three objectives. The proposed approach is evaluated on five open source datasets and its performance is compared with five different well-known state-of-the-art meta-heuristic and non-meta-heuristic approaches. The experimental results exhibit that the refactoring solutions provided by MOSHO are significantly better than other algorithms when class importance and code smell severity scores are used.