Empirical comparison and evaluation of Artificial Immune Systems in inter-release software fault prediction

Abstract

Artificial immune systems are bio-inspired machine learning algorithms based on the mammalian immune paradigms. One of the possible uses of these methods is Software Fault Prediction, which consists of classifying the modules of an application as being fault-prone or not, thus allowing a developer to better target the modules during the test phase leading to a high-quality software with lower cost. Despite the high number of works in the field, only five studies included Artificial Immune Systems in their approaches and exclusively focused on the intra-project fault prediction scheme. In this study, our objective is to appraise 8 immunological systems on the rarely treated inter-project software defect prediction scenario over three different benchmarks, hence, we selected 41 datasets corresponding to 11 java projects from the PROMISE data repository. According to the Friedman and Nemenyi Post-hoc test results, none of the performance of the studied algorithms was better than Immunos-1 and Immunos-99 in terms of the Recall measure. Furthermore, the outcomes of the Wilcoxon test suggest that the researches addressing the intra-projects defect prediction problems should also evaluate their models on inter-release scenarios.