Software Refactoring Prediction Using SVM and Optimization Algorithms

Abstract

Test suite code coverage is often used as an indicator for test suite capability in detecting faults. However, earlier studies that have explored the correlation between code coverage and test suite effectiveness have not addressed this correlation evolutionally. Moreover, some of these works have only addressed small sized systems, or systems from the same domain, which makes the result generalization process unclear for other domain systems. Software refactoring promotes a positive consequence in terms of software maintainability and understandability. It aims to enhance the software quality by modifying the internal structure of systems without affecting their external behavior. However, identifying the refactoring needs and which level should be executed is still a big challenge to software developers. In this paper, the authors explore the effectiveness of employing a support vector machine along with two optimization algorithms to predict software refactoring at the class level. In particular, the SVM was trained in genetic and whale algorithms. A well-known dataset belonging to open-source software systems (i.e., ANTLR4, JUnit, MapDB, and McMMO) was used in this study. All experiments achieved a promising accuracy rate range of between 84% for the SVM–Junit system and 93% for McMMO − GA + Whale + SVM. It was clear that added value was gained from merging the SVM with two optimization algorithms. All experiments achieved a promising F-measure range between the SVM–Antlr4 system’s result of 86% and that of the McMMO − GA + Whale + SVM system at 96%. Moreover, the results of the proposed approach were compared with the results from four well known ML algorithms (NB-Naïve, IBK-Instance, RT-Random Tree, and RF-Random Forest). The results from the proposed approach outperformed the prediction performances of the studied MLs.