rmmoc: refactoring method based on multi-objective algorithms and new criteria

Some factors can change the software and affect the quality, such as the new users’ requirements and the need for compatibility with modern techniques. these factors impose a high cost on technical software maintenance. one of the techniques for software quality improvement and maintenance cost reduction is refactoring. the advantage of this method is software behavior preservation. because the cost of refactoring manually is high, a technique called the hybrid optimization problem has been proposed. the main challenge in refactoring is to propose a technique with high accuracy and less runtime. hence, in the present work, a refactoring method based on the multi-objective algorithms called rmmoc is proposed to tradeoff between quality and runtime. this method uses a helpful search-based method called umocell to increase refactoring quality. this method inspires both population-based and local-based search algorithms. another novelty in this paper is using new metrics for program quality assessment that help increase accuracy, decrease refactoring runtime, and find the best solutions. because software metrics play a significant role in search-based refactoring approaches, this paper introduces two effective criteria called mpc and refactoring number reduction in addition to previously presented metrics. the experiments’ results show that the proposed method’s performance is remarkable and that using new metrics is effective.