Performance Analysis of Maximal Risk Evaluation Formulas for Spectrum-Based Fault Localization

• Published in: Applied sciences 2020-01, Vol.10 (1), p.398
• Main Authors: Wu, Tingting, Dong, Yunwei, Lau, Man Fai, Ng, Sebastian, Chen, Tsong Yueh, Jiang, Mingyue
• Format: Article
• Language: English
• Subjects: debugging; maximal risk evaluation formulas; spectrum-based fault localization; testing
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10010398

The effectiveness analysis of risk evaluation formulas has become a significant research area in spectrum-based fault localization (SBFL). The risk evaluation formula is designed and widely used to evaluate the likelihood of a program spectrum to be faulty. There are numerous empirical and theoretical studies to investigate and compare the performance between sixty risk evaluation formulas. According to previous research, these sixty risk evaluation formulas together form a partially ordered set. Among them, nine formulas are maximal. These nine formulas can further be grouped into five maximal risk evaluation formula groups so that formulas in the same group have the same performance. Moreover, previous research showed that we cannot theoretically compare formulas across these five maximal formula groups. However, experimental data “suggests” that a maximal formula in one group could outperform another one (from a different group) more frequently, though not always. This inspired us to further investigate the performance between any two maximal formulas in different maximal formula groups. Our approach involves two major steps. First, we propose a new condition to compare between two different maximal formulas. Based on this new condition, we present five different scenarios under which a formula performs better than another. This is different from the condition suggested by the previous theoretical study. We performed an empirical study to compare different maximal formulas using our condition. Our results showed that among two maximal risk evaluation formulas, it is feasible to identify one that can outperform the others more frequently.