Effectively Combining Risk Evaluation Metrics for Precise Fault Localization

Adekunle Ajibode, Ting Shu (), Laghari Gulsher and Zuohua Ding
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Adekunle Ajibode: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
Ting Shu: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China
Laghari Gulsher: Institute of Mathematics and Computer Science, University of Sindh, Jamshoro 76080, Sindh, Pakistan
Zuohua Ding: School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China

Mathematics, 2022, vol. 10, issue 21, 1-24

Abstract: Spectrum-based fault localization (SBFL) is an automated fault localization technique that uses risk evaluation metrics to compute the suspiciousness scores from program spectra. Thus, risk evaluation metrics determine the technique’s performance. However, the existing experimental studies still show no optimal metric for different program structures and error types. It is possible to further optimize SBFL’s performance by combining different metrics. Therefore, this paper effectively explores the combination of risk evaluation metrics for precise fault localization. Based on extensive experiments using 92 faults from SIR and 357 faults from Defects4J repositories, we highlight what and which risk evaluation metrics to combine to maximize the efficiency and accuracy of fault localization. The experimental results show that combining risk evaluation metrics with high negative correlation values can improve fault localization effectiveness. Similarly, even though the combination of positively correlated effective risk evaluation metrics can outperform most negatively correlated non-effective ones, it still cannot improve the fault localization effectiveness. Furthermore, low-correlated risk evaluation metrics should also be considered for fault localization. The study concluded that getting highly negatively correlated risk evaluation metrics is almost impossible. The combination of such risk evaluation metrics would improve fault localization accuracy.

Keywords: spectrum-based fault localization; risk evaluation metrics; metric combination; software testing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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