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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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Zenodo
2026
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| Acceso en línea: | https://doi.org/10.5281/zenodo.19253962 |
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| _version_ | 1866901209615433728 |
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| author | Liu, Xiaoming |
| author_facet | Liu, Xiaoming |
| contents | <div>Mutation score (MS) is a single scalar value that summarizes the effectiveness of a test suite in detecting artificially seeded bugs. While widely adopted, MS loses critical subcategory-level information about how different mutation operator types contribute to the overall score. This paper proposes operator-level R´enyi entropy fingerprints as a fine-grained information-theoretic characterization of mutation testing landscapes. We conduct experiments on 5 controlled Python projects and 4 real-world Python modules.</div> <div> Our key findings are:</div> <div>(1) projects with identical MS can exhibit significantly different fingerprints, revealing that MS alone obscures important structural differences in mutation distributions;</div> <div>(2) the diversity gap metric, defined as the difference between Hartley and min-entropy, predicts operator-level vulnerability, identifying which operator categories are inadequately covered by test suites;</div> <div>(3) Literal Constant Replacement (LCR) dom inates mutation landscapes, accounting for 61% of all mutants in the dateutil-relativedelta module, indicating that test suites primarily need validation of boundary and literal values.</div> <div>The R´enyi entropy frame work provides a structured diagnostic perspective that complements traditional mutation score reporting.</div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19253962 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Rényi Entropy Fingerprints of Mutant Subcategories: (Information-Theoretic Framework for Mutation Testing Analysis) Liu, Xiaoming mutation testing, R´enyi entropy, entropy fingerprint, diversity gap, mutation operators, information theory <div>Mutation score (MS) is a single scalar value that summarizes the effectiveness of a test suite in detecting artificially seeded bugs. While widely adopted, MS loses critical subcategory-level information about how different mutation operator types contribute to the overall score. This paper proposes operator-level R´enyi entropy fingerprints as a fine-grained information-theoretic characterization of mutation testing landscapes. We conduct experiments on 5 controlled Python projects and 4 real-world Python modules.</div> <div> Our key findings are:</div> <div>(1) projects with identical MS can exhibit significantly different fingerprints, revealing that MS alone obscures important structural differences in mutation distributions;</div> <div>(2) the diversity gap metric, defined as the difference between Hartley and min-entropy, predicts operator-level vulnerability, identifying which operator categories are inadequately covered by test suites;</div> <div>(3) Literal Constant Replacement (LCR) dom inates mutation landscapes, accounting for 61% of all mutants in the dateutil-relativedelta module, indicating that test suites primarily need validation of boundary and literal values.</div> <div>The R´enyi entropy frame work provides a structured diagnostic perspective that complements traditional mutation score reporting.</div> |
| title | Rényi Entropy Fingerprints of Mutant Subcategories: (Information-Theoretic Framework for Mutation Testing Analysis) |
| topic | mutation testing, R´enyi entropy, entropy fingerprint, diversity gap, mutation operators, information theory |
| url | https://doi.org/10.5281/zenodo.19253962 |