Poster: Identification of Methods with Low Fault Risk

TL;DR

This paper introduces an inverse defect prediction method to identify low-fault-risk code regions, helping testers prioritize testing efforts and improve fault detection efficiency in software projects.

Contribution

The paper presents a novel inverse defect prediction approach that effectively identifies low-risk methods, including in cross-project scenarios, aiding testing prioritization.

Findings

31.6% of methods have low fault risk

Low-risk methods contain only 5.8% of total faults

Approach works in cross-project prediction scenarios

Abstract

Test resources are usually limited and therefore it is often not possible to completely test an application before a release. Therefore, testers need to focus their activities on the relevant code regions. In this paper, we introduce an inverse defect prediction approach to identify methods that contain hardly any faults. We applied our approach to six Java open-source projects and show that on average 31.6% of the methods of a project have a low fault risk; they contain in total, on average, only 5.8% of all faults. Furthermore, the results suggest that, unlike defect prediction, our approach can also be applied in cross-project prediction scenarios. Therefore, inverse defect prediction can help prioritize untested code areas and guide testers to increase the fault detection probability.