A Learning-to-Rank Based Approach for Improving Regression Test Case Prioritization

Abstract

Many prior studies with attempt to improve regression testing adopt test case prioritization (TCP). TCP generally arranges the execution of regression test cases according to specific rules with the goal of revealing faults as early as possible. It is noted that different TCP algorithms adopt different metrics to evaluate test cases' priority so that they may be effect at revealing faults early in different faulty programs. Adopting a single metric may not generally work well. In this decade, learning-to-rank (LTR) strategies have been adopted to address some software engineering problems. This study also uses a pairwise LTR strategy XGBoost to combine several existing metrics so as to improve TCP effectiveness. More specifically, we regard the metrics adopted by TCP techniques to evaluate test cases' priority as the features of the training data and adopt XGBoost to learn the weights of the combined metrics. Additionally, in order to avoid overfitting, we use a fuzzy inference system to generate additional features for data augmentation. The experimental results show that our approach achieves more excellent effectiveness than the existing TCP techniques with respect to the selected subject programs.