Stability Aware Software Refactoring Using Hybrid Search Based Techniques

Software refactoring is a crucial and necessary activity for the quality enhancement during maintenance and evolution of a software system. Stability is a key quality aspect that needs to be monitored especially for the process of refactoring. Due to the nature of the larger possible options for refactoring, search based software engineering practices provide a best possible way in improving the code quality through refactoring. A combination of automated refactoring techniques and metaheuristic search methods, along with the aid of software metrics, can improve the structure of the code. In this work, four quality attributes, namely abstraction, coupling, inheritance, along with stability is taken as fitness functions that drive the search process. The calculation of the fitness function follows a weighted aggregate method of the software metrics. A hybrid Gravitational Search Algorithm and Artificial Bee Colony algorithm (GSA-ABC) is applied which provides improved results in the search process of refactoring options. The results show improvement in terms of average quality gain of the four measured quality factors for the refactored software.