Software Testing Verification and Reliability· 2013Q2
Metallaxis‐FL: mutation‐based fault localization
- 355citations
- Q2SCImago
- 2013year
Short summary
Metallaxis-FL, a novel fault localization approach, identifies faulty code by linking program mutants to their locations, showing mutants killed by failing tests are strong indicators of faults.
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Key points
- Metallaxis-FL uses program mutants, linking them to faulty code locations.
- Mutants killed predominantly by failing tests are strong indicators of fault locations.
- Metallaxis-FL is significantly more effective than statement-based fault localization methods.
- Mutation-based testing improves fault localization over block- or branch-based test suites.
AI-generated from the title and abstract; the full text is not read.
Abstract
Summary Fault localization methods seek to identify faulty program statements based on the information provided by the failing and passing test executions. Spectrum‐based methods are among the most popular ones and assist programmers by assigning suspiciousness values on program statements according to their probability of being faulty. This paper proposes Metallaxis, a fault localization approach based on mutation analysis. The innovative part of Metallaxis is that it uses mutants and links them with the faulty program places. Thus, mutants that are killed mostly by failing tests provide a good indication about the location of a fault. Experimentation using Metallaxis suggests that it is significantly more effective than statement‐based approaches. This is true even in the case where mutation cost‐reduction techniques, such as mutant sampling, are facilitated. Additionally, results from a controlled experiment show that the use of mutation as a testing technique provides benefits to the fault localization process. Therefore, fault localization is significantly improved by using mutation‐based tests instead of block‐based or branch‐based test suites. Finally, evidence in support of the methods’ scalability is also given. Copyright © 2013 John Wiley & Sons, Ltd.
The authors' abstract, as published at the source. Software Testing Verification and Reliability, 2013 · DOI ↗
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Field: Software
SoftwareComputer Science