The impact of training data selection on the software defect prediction performance and data complexity

Abstract

Directly learning a defect prediction model from cross-project datasets results in a model with poor performance. Hence, training data selection becomes a feasible solution to this problem. Limited comparative studies investigating the effect of training data selection on the prediction performance have presented contradictory results. Those studies also did not analyze why a training data selection method underperforms. This study aims to investigate the impact of training data selection on the defect prediction model and data complexity measures. The method is based on an empirical comparison between prediction performance and data complexity measure before and after selection. This study compared 13 training data selection methods on 61 projects using six classification algorithms and measured the data complexity using six complexity measures focusing on overlap class, noise level, and class imbalanced ratio. Experimental results indicate that the best method for each dataset varies depending on the dataset and classifiers. The training data selection most affects noise rate and class imbalance. We concluded that critically selecting the training data method could improve the performance of the prediction model. We recommend dealing with noise and unbalanced classes when designing training data methods.

Cross-project software defect prediction through multiple learning

“…However, software testing activities are higly expensive [1]. Numerious studies found that most software errors are caused by only some few parts of the software modules [2], [3]. To reduce the number of resources required for testing the software project, prior to testing activities, software testing team can use software defect prediction (SDP) tools to predict defect-prone modules in software projects [4].…”