A genetic algorithm-based approach for automated refactoring of component-based software

TL;DR: This approach is the first to focus on component-based refactoring, whereas the state-of-the-art approaches focus only on object-oriented refactors, and outperforms random search and local search in terms of efficiency and accuracy.

Abstract: Context: During its lifecycle, a software system undergoes repeated modifications to quickly fulfill new requirements, but its underlying design is not properly adjusted after each update. This leads to the emergence of bad smells. Refactoring provides a de facto behavior-preserving approach to eliminate these anomalies. However, manually determining and performing useful refactorings is a formidable challenge, as stated in the literature. Therefore, framing object-oriented automated refactoring as a search-based technique has been proposed. However, the literature shows that search-based refactoring of component-based software has not yet received proper attention.Objective: This paper presents a genetic algorithm-based approach for the automated refactoring of component-based software. This approach consists of detecting component-relevant bad smells and eliminating these bad smells by searching for the best sequence of refactorings using a genetic algorithm.Method: Our approach consists of four steps. The first step includes studying the literature related to component-relevant bad smells and formulating bad smell detection rules. The second step involves proposing a catalog of component-relevant refactorings. The third step consists of constructing a source code model by extracting facts from the source code of a component-based software. The final step seeks to identify the best sequence of refactorings to apply to reduce the presence of bad smells in the source code model using a genetic algorithm. The latter uses bad smell detection rules as a fitness function and the catalog of refactorings as a means to explore the search space.Results: As a case study, we conducted experiments on an unbiased set of four real-world component-based applications. The results indicate that our approach is able to efficiently reduce the total number of bad smells by more than one half, which is an acceptable value compared to the recent literature. Moreover, we determined that our approach is also accurate in refactoring only components suffering from bad smells while leaving the remaining components untouched whenever possible. Furthermore, a statistical analysis shows that our genetic algorithm outperforms random search and local search in terms of efficiency and accuracy on almost all the systems investigated in this work.Conclusion: This paper presents a search-based approach for the automated refactoring of component-based software. To the best of our knowledge, our approach is the first to focus on component-based refactoring, whereas the state-of-the-art approaches focus only on object-oriented refactoring.