An Empirical Study on Higher-Order Mutation-Based Fault Localization

Author:

Wang Haifeng1,Li Zheng1,Liu Yong1,Chen Xiang23

Affiliation:

1. College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, P. R. China

2. School of Information Science and Technology, Nantong University, Nantong 226019, P. R. China

3. State Key Laboratory of Information Security, Chinese Academy of Sciences, Beijing 100093, P. R. China

Abstract

Fault localization is one of the most expensive activities in software debugging. Mutation-based fault localization (MBFL) is a commonly studied technique that applied mutation analysis to find the location of faults in the programs. Previous studies showed that MBFL adopted First-Order-Mutants (FOMs) that could achieve promising results in single-fault localization, but it did not perform well in multiple-fault localization. Recently, Higher-Order-Mutants (HOMs) were proposed for modeling complex faults but whether HOMs can help in fault localization is still unknown. In this paper, we investigate the performance of MBFL with FOMs and HOMs on single- and multiple-fault localization. Moreover, to study the characteristics of HOMs, we divide HOMs into three groups (i.e. Accurate HOMs, Partially accurate HOMs, and Inaccurate HOMs) by considering different mutation locations. Based on the empirical results on 186 versions of six real-world programs, we find that (1) In single-fault localization, FOMs can achieve better performance than HOMs. (2) However, in multiple-fault localization, HOMs (2-HOMs) localize more faults than FOMs. (3) Furthermore, different types of HOMs have different fault localization effectiveness, where Accurate HOMs outperform the other two HOMs categories. Therefore, the researchers should propose methods to find HOMs more useful for fault localization.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Artificial Intelligence,Computer Graphics and Computer-Aided Design,Computer Networks and Communications,Software

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1. Optimizing Mutation-Based Fault Localization Through Contribution-Based Test Case Reduction;International Journal of Software Engineering and Knowledge Engineering;2024-07-05

2. Test Case Level Predictive Mutation Testing Combining PIE and Natural Language Features;2023 30th Asia-Pacific Software Engineering Conference (APSEC);2023-12-04

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5. Multiple fault localization based on ant colony algorithm via genetic operation;Journal of King Saud University - Computer and Information Sciences;2023-09

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