Predicting Fault Proneness of Classes Trough a Multiobjective Particle Swarm Optimization Algorithm

Software testing is a fundamental software engineering activity for quality assurance that is also traditionally very expensive. To reduce efforts of testing strategies, some design metrics have been used to predict the fault-proneness of a software class or module. Recent works have explored the use of machine learning (ML) techniques for fault prediction. However most used ML techniques can not deal with unbalanced data and their results usually have a difficult interpretation. Because of this, this paper introduces a multi-objective particle swarm optimization (MOPSO) algorithm for fault prediction. It allows the creation of classifiers composed by rules with specific properties by exploring Pareto dominance concepts. These rules are more intuitive and easier to understand because they can be interpreted independently one of each other. Furthermore, an experiment using the approach is presented and the results are compared to the other techniques explored in the area.