Abstract
Software refactoring is one of the most significant practices in software maintenance as the quality of software design tends to deteriorate during software evolution. But, refactoring software is a very challenging task as it requires a holistic view of the entire software system. To this end, recent studies introduced search-based algorithms to facilitate software refactoring. However, they still have the following major limitations: 1) the searched solutions may violate the design principles as their fitness functions do not directly reflect the degree of software’s compliance with design principles; 2) most approaches start the searching process from a completely random initial population, which may lead to unoptimal solutions. In this article, we aim to develop effective search-based refactoring approach to recommend better refactoring activities for developers which can improve the degree of software’s compliance with design principles as well as the software design quality. We propose <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DEPICTER</monospace> , a design-principle guided and heuristic-rule constrained software refactoring recommendation approach. In particular, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DEPICTER</monospace> uses non-dominated sorting genetic algorithm (NSGA)-II genetic algorithm and employs design-principle metrics as fitness functions. Besides, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DEPICTER</monospace> leverages heuristic rules to improve the quality of initial population for subsequent generic evolution. Our evaluations, based on four widely used systems, show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DEPICTER</monospace> is effective for guiding the development of better refactoring models in practice.
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