How is mutational analysis done?
Best insight from top research papers
Mutational analysis is conducted by systematically seeding small artificial faults into a program to evaluate a test suite's effectiveness in detecting these faults. This process, known as mutation testing, aims to enhance or create test suites by using mutants as test goals. The traditional approach faces scalability challenges, especially in large codebases like Google's, due to the immense computational effort required. To address this, a scalable approach has been proposed, involving incremental mutation testing on changed code during code reviews, mutant filtering to remove irrelevant mutants, and selection based on historical performance of mutation operators. This approach has been empirically validated in a code-review-based setting, demonstrating significantly fewer mutants and improved mutant quality and actionability.
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| Papers (4) | Insight |
|---|---|
| Mutational analysis is done incrementally on changed code during code review, filtering and selecting mutants based on historical performance of mutation operators to improve quality and actionability. | |
17 Citations | Mutational analysis is done incrementally on changed code during code review, filtering and selecting mutants based on historical performance to improve quality and actionability. |
Open access•Posted Content | Mutation analysis is done incrementally on changed code during code review, filtering and selecting mutants based on historical performance of mutation operators to improve mutant quality and actionability. |
| Mutational analysis is done by selecting mutants based on Fourier analysis of Boolean functions to identify effective transitions on the output, improving mutation analysis efficiency without compromising performance. |
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