Evolutionary Computation-Based Techniques Over Multiple Data Sets: An Empirical Assessment

Abstract

In the realm of software testing various organizations wish to predict the faults in their software systems prior to their deployment. This improves the delivered quality and also reduces the maintenance effort. A multitude of software metrics and statistical models have been developed to solve this problem and one such method is called defect prediction. Defect prediction is the process of identifying the defects in the software program prior to its deployment. In recent times, a class of learners called evolutionary computation (EC) techniques has emerged. These EC techniques apply the Darwinian principle of ‘survival of the fittest’. This study performs an empirical assessment of the performance of various EC techniques in the prediction of software defects over multiple data sets. An empirical assessment compares and assesses the performance capability of 16 EC techniques for evaluating the relationship between object-oriented metrics and defect prediction. The developed models are validated using 7 data sets obtained from open source software systems developed by the Software Foundation. On investigating their predictive capabilities and comparative performance, it was found that a majority of EC techniques proved to be highly effective. DTG (a hybridized algorithm) was observed to be the best performing technique. The work done in the current study shows that EC techniques are very effective and can be highly beneficial to testers in the realm of defect prediction in the future.