The effect of locality based learning on software defect prediction

The Effect of Locality Based Learning on Software Defect Prediction Bryan Lemon Software defect prediction poses many problems during classification. A common solution uses to improve software defect prediction is to train on similar, or local, data to the testing data. Proior work [8,47] shows that locallity improves the effect of classifiers, and this approach has been commonly applied to the field of software defect prediction. In this thesis, we compair the performance of many classifiers, both locality based and non-locality based classifiers. We propose a novel classifier called Clump, with the goals of improving classification while providing an explination as to how the decisions were reached. We also explore the effects of standard clustering and relevency filtering algorithms. Through experimentation, we show that locality does not improve the performance when applied to software defect prediction. The performance of the algorithms is impacted more by the datasets used than by the algorithmic choices made. More research is needed to explore locality based learning and the impact of the datasets chosen.