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research-article

An effective fault localization approach based on PageRank and mutation analysis

Authors:

Cheng ZhangAuthors Info & Claims

Volume 204, Issue C

https://doi.org/10.1016/j.jss.2023.111799

Published: 01 October 2023 Publication History

Abstract

Mutation-based fault localization (MBFL) is a popular method based on mutation testing. MBFL applies a variety of operators to generate mutants and calculates the statement’s suspiciousness by counting the execution results of the test cases on the mutants. However, the tie problem of MBFL creates obstacles to accurate fault localization. The tie problem refers to that many statements have the same suspiciousness. To solve the tie problem, we propose a fault localization approach based on the PageRank algorithm and mutation analysis (PRMA). We first apply the PageRank algorithm to calculate the faultiness scores of the statements. Then, we weight the suspicious value of the statements with faultiness scores to solve the tie problem. Finally, the weighted suspicious values are sorted in descending order to generate a list, which is provided to developers for fault localization. To evaluate our approach, we conduct experiments on the real fault benchmark Defects4J and the artificial fault dataset Siemens. We compare PRMA with the traditional MBFL techniques (Metallaxis and MUSE) and recently proposed MBFL methods (MCBFL-hybrid-avg, SMFL and SMBFL). The experimental results show that our approach outperforms above comparison methods and improves the effect of fault localization in both quantity and accuracy.

Highlights

•

PRMA combines PageRank and mutation analysis for fault localization.

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Solving the tie problem by weighting the mutating suspicious value with the PageRank score.

•

Evaluating on the real and artificial faults from Defects4J and Siemens, respectively.

•

PRMA outperforms traditional and recently proposed MBFL methods.

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Information & Contributors

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Published In

cover image Journal of Systems and Software

Journal of Systems and Software Volume 204, Issue C

Oct 2023

486 pages

ISSN:0164-1212

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Elsevier Inc.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 October 2023

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