Comparative study of machine learning test case prioritization for continuous integration testing
Author:
Publisher
Springer Science and Business Media LLC
Subject
Safety, Risk, Reliability and Quality,Software
Link
https://link.springer.com/content/pdf/10.1007/s11219-023-09646-0.pdf
Reference35 articles.
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3. Busjaeger, B., & Xie, T. (2016). Learning for test prioritization: An industrial case study. In Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, FSE 2016, Association for Computing Machinery, New York, NY, USA (pp. 975–980). https://doi.org/10.1145/2950290.2983954
4. Calvo, B., & Santafé Rodrigo, G. (2016). scmamp: Statistical comparison of multiple algorithms in multiple problems. The R Journal, 8, 248–256.
5. Chen, J., Lou, Y., Zhang, L., Zhou, J., Wang, X., Hao, D., & Zhang, L. (2018). Optimizing test prioritization via test distribution analysis. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, ESEC/FSE 2018, Association for Computing Machinery, New York, NY, USA (pp. 656–667). https://doi.org/10.1145/3236024.3236053
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