Improving Fault-Localization Accuracy by Referencing Debugging History to Alleviate Structure Bias in Code Suspiciousness

• Published in: IEEE transactions on reliability Vol. 69; no. 3; pp. 1021 - 1049
• Main Authors: Zhang, Long, Li, Zijie, Feng, Yang, Zhang, Zhenyu, Chan, Wing Kwong, Zhang, Jian, Zhou, Yuming
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytical models; Bias; Debugging; Fault location; History; Java; Localization; Performance enhancement; Program debugging; Software; software testing; spectrum-based fault localization; Testing
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2020.2982975

Spectrum-based fault localization (SBFL) techniques can automatically localize software faults. They employ the program spectrum, such as code coverage profile with test verdicts, to rank the program entities based on their code suspiciousness. In the past decades, researchers have proposed many approaches to optimize these techniques; however, the program structure, which can influence their performance, is not taken into consideration in developing and improving these techniques. In this article, we identify and analyze the effect of the program structure on the application of SBFL techniques. We observe that some specific program structures may introduce structure bias to code suspiciousness and negatively influence the output of SBFL techniques. To mitigate these effects and improve the performance of fault localization, we propose Delta4Ts, a structure-aware technique. Delta4Ts references debugging history to alleviate the impact of structure bias in the calculation of code suspiciousness. It reasons from the observable suspicious value towards the desired suspicious value and the impact of structure bias. To evaluate Delta4Ts under practical constraints, we conduct a controlled experiment using nine widely-studied SBFL formulae on 12 C programs and 6 Java programs. The experiment results show that Delta4Ts can significantly improve the accuracy of the studied SBFL formulae by an average of 34.8% on 12 C programs and 30.6% on 6 Java programs, and improve more on subject programs associated with more history versions or having larger code sizes.