USING THE CBUA MODEL WITHOUT THE PRESENCE OF UNCOVERED MUTANTS
DOI:
https://doi.org/10.17721/3041-2323.2024.341-346Keywords:
mutation testing, predictive mutation testingAbstract
Mutation testing is an effective tool for evaluating test quality, but it requires significant computational resources. Since there is a lack of frameworks capable of supporting both mutation analysis and fuzzing simultaneously, this limits research in this area. The CBUA mutation testing prediction method, which utilizes the Halstead complexity metric, improves prediction accuracy,
especially in cases where all mutants are covered by tests.
References
Aghamohammadi, A., & Mirian-Hosseinabadi, S.-H. (2020). The threat to the validity of predictive mutation testing: The impact of uncovered mutants. arXiv.
Gopinath, R., Görz, P., & Groce, A. (2022). Mutation analysis: Answering the fuzzing challenge. arXiv.
Vikram, V., Laybourn, I., Li, A. et al. (2023). Guiding greybox fuzzing with mutation testing. ACM, Seattle, WA, USA.
Zhang, P., Li, Y., Ma, W. et al. (2020). CBUA: A probabilistic, predictive, and practical approach for evaluating test suite effectiveness. IEEE Transactions on Software Engineering, 46(1), 1–1.
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