A Novel Mutation Operator for Search-Based Test Case Selection
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48796-5_6
Reference35 articles.
1. Almulla, H., Gay, G.: Learning how to search: generating effective test cases through adaptive fitness function selection. Empir. Softw. Eng. 27(2), 1–62 (2022)
2. Arcuri, A., Briand, L.: A practical guide for using statistical tests to assess randomized algorithms in software engineering. In: 2011 33rd International Conference on Software Engineering (ICSE), pp. 1–10. IEEE (2011)
3. Arrieta, A.: Is the revisited hypervolume an appropriate quality indicator to evaluate multi-objective test case selection algorithms? In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1317–1326 (2022)
4. Arrieta, A.: Multi-objective metamorphic follow-up test case selection for deep learning systems. In: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1327–1335 (2022)
5. Arrieta, A., Agirre, J.A., Sagardui, G.: Seeding strategies for multi-objective test case selection: an application on simulation-based testing. In: Proceedings of the 2020 Genetic and Evolutionary Computation Conference, pp. 1222–1231 (2020)
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