The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Reinforcement learning for automatic test case prioritization and selection in continuous integration
Open accessAbstract
Testing in Continuous Integration (CI) involves test case prioritization, selection, and execution at each cycle. Selecting the most promising test cases to detect bugs is hard if there are uncertainties on the impact of committed code changes or, if traceability links between code and tests are not available. This paper introduces Retecs, a new method for automatically learning test case selection and prioritization in CI with the goal to minimize the round-trip time between code commits and developer feedback on failed test cases. The Retecs method uses reinforcement learning to select and prioritize test cases according to their duration, previous last execution and failure history. In a constantly changing environment, where new test cases are created and obsolete test cases are deleted, the Retecs method learns to prioritize error-prone test cases higher under guidance of a reward function and by observing previous CI cycles. By applying Retecs on data extracted from three industrial case studies, we show for the first time that reinforcement learning enables fruitful automatic adaptive test case selection and prioritization in CI and regression testing.
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
Scikit-learn: Machine Learning in Python
(2011) · Journal of Machine Learning Research · doi:10.5555/1953048.2078195 · Cited by 8,539
Introduction to Reinforcement Learning
Sutton, R. S., & Barto, A. G. (1998) · The MIT Press eBooks · Cited by 6,855
Apprenticeship learning via inverse reinforcement learning
Abbeel, P., & Ng, A. Y. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015430 · Cited by 2,769
Self-improving reactive agents based on reinforcement learning, planning and teaching
Lin, L.-J. (1992) · Machine Learning · doi:10.1007/bf00992699 · Cited by 1,564
Prioritizing test cases for regression testing
Rothermel, G., Untch, R. H., Chu, C., & Harrold, M. J. (2001) · IEEE Transactions on Software Engineering · doi:10.1109/32.962562 · Cited by 1,338
Solving large scale linear prediction problems using stochastic gradient descent algorithms
Zhang, T. (2004) · Twenty-first international conference on Machine learning - ICML '04 · doi:10.1145/1015330.1015332 · Cited by 1,106
Riedmiller, M. (2005) · Lecture Notes in Computer Science · doi:10.1007/11564096_32 · Cited by 757
Cited by
Machine learning applied to software testing: A systematic mapping study
Durelli, V. H. S., Durelli, R. S., Borges, S. S., Endo, A. T., Eler, M. M., Dias, D. R. C., & Guimaraes, M. P. (2019) · IEEE Transactions on Reliability · doi:10.1109/tr.2019.2892517 · Cited by 207
Ali, S., Hafeez, Y., Hussain, S., & Yang, S. (2020) · Software Quality Journal · doi:10.1007/s11219-019-09463-4 · Cited by 43
Metadata
- Authors
- Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M.
- Venue
- Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis
- Publisher
- ACM
- Published
- 2017-07-10
- Pages
- 12-22
- OpenAlex
- W2734711024
- Cited by
- 283
- References
- 28
- FWCI
- 13.71
- Type
- conference paper
- Language
- en
- Open access
- green
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- Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017). Reinforcement learning for automatic test case prioritization and selection in continuous integration. Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis. https://doi.org/10.1145/3092703.3092709
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