Fault localization via efficient probabilistic modeling of program semantics

Zeng, Muhan; Wu, Yiqian; Ye, Zhentao; Xiong, Yingfei; Zhang, Xin; Zhang, Lu

doi:10.1145/3510003.3510073

Cited by 19 publications

(1 citation statement)

References 29 publications

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“…To enhance the impact of failed test cases on fault localization, Xie et al (2022) used a universal data augmentation method that generates synthesized failing test cases from reduced feature space for improving fault localization. Zeng et al (2022) introduced a probabilistic approach to model program semantics and utilize information from static analysis and dynamic execution traces for fault localization, which balance could be reached between effectiveness and scalability. Wang et al (2022) investigated the performance of Mutation-based fault localization with First-Order-Mutants and Higher-Order-Mutants on single-fault localization and multiple-fault localization.…”

Section: Related Workmentioning

confidence: 99%

The improved dynamic slicing for spectrum-based fault localization

Cao

Wang

Deng

et al. 2022

PeerJ Computer Science

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Background Spectrum-based Fault localization have proven to be useful in the process of software testing and debugging. However, how to improve the effectiveness of software fault localization has always been a research hot spot in the field of software engineering. Dynamic slicing can extract program dependencies under certain conditions. Thus, this technology is expected to benefit for locating fault. Methods We propose an improved dynamic slicing for spectrum-based fault localization under a general framework. We first obtain the dynamic slice of program execution. Secondly, we construct a mixed slice spectrum matrix from the dynamic slice of each test case and the corresponding test results. Finally, we compute the suspiciousness value of each statement in the mixed-slice spectram matrix. Results To verify the performance of our method, we conduct an empirical study on 15 widely used open-source programs. Experimental results show that our approach achieves significant improvement than the compared techniques. Conclusions Our approach can reduce approximately 1% to 17.79% of the average cost of code examined significantly.

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Section: Related Workmentioning

confidence: 99%

The improved dynamic slicing for spectrum-based fault localization

Cao

Wang

Deng

et al. 2022

PeerJ Computer Science

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A Systematic Exploration of Mutation‐Based Fault Localization Formulae

Wang,

Wei,

Chen

et al. 2024

Software Testing Verif & Rel

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Fault localization (FL) aims to automatically find the location of bugs in a software program. In the family of FL approaches, spectrum‐based fault localization (SBFL) is the most widely used and has been extensively studied, which computes the suspicious scores of a code element to be buggy via test coverage information. Mutation‐based fault localization (MBFL) further analyses the relationship between mutants and test results. Although MBFL involves more information, it also faces the following limitations. (1) To precisely evaluate suspicious sources, SBFL approaches proposed dozens of formulae based on coverage, while only a few of them have been adopted by MBFL. (2) The current MBFL approaches are based on the assumption that the capability in localizing bugs of each mutant is the same, so they assign the same weight to the mutants that change the test outputs and consider the mutant from the same code element equivalent. In this paper, we intend to enrich the MBFL family by the following two approaches. First, we collect 25 typical SBFL formulae and transform them into MBFL versions. Second, we propose new assumptions that the mutants should have different weights in computing suspicious sources and propose BLMu, a novel MBFL approach that treats mutants differently. We also propose novel metrics for MBFL by considering the high cost of mutation analysis. We perform large‐scale experiments by evaluating all the MBFL approaches against 395 real‐world Java bugs of the Defects4J benchmark. Our evaluation results reveal that BLMu demonstrates a substantial improvement over both MUSE and Metallaxis, the most popular MBFL approaches, at both the statement level and the method level. Specifically, in terms of Top‐1, BLMu improves by 106% at the statement level and 69% at the method level. When compared with other types of FL approaches, MBFL outperforms typical SBFL approaches while still far behind the state‐of‐the‐art learning‐based FL approaches.

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