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• W2952636017 abstract "Meta-heuristic search algorithms such as genetic algorithms have been applied successfully to generate unit tests, but typically take long to produce reasonable results, achieve sub-optimal code coverage, and have large variance due to their stochastic nature. Parallel genetic algorithms have been shown to be an effective improvement over sequential algorithms in many domains, but have seen little exploration in the context of unit test generation to date. In this paper, we describe a parallelised version of the many-objective sorting algorithm (MOSA) for test generation. Through the use of island models, where individuals can migrate between independently evolving populations, this algorithm not only reduces the necessary search time, but produces overall better results. Experiments with an implementation of parallel MOSA on the EvoSuite test generation tool using a large corpus of complex open source Java classes confirm that the parallelised MOSA algorithm achieves on average 84% code coverage, compared to 79% achieved by a standard sequential version." @default.
• W2952636017 created "2019-06-27" @default.
• W2952636017 creator A5028926123 @default.
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• W2952636017 date "2019-04-01" @default.
• W2952636017 modified "2023-09-27" @default.
• W2952636017 title "Parallel Many-Objective Search for Unit Tests" @default.
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• W2952636017 doi "https://doi.org/10.1109/icst.2019.00014" @default.
• W2952636017 hasPublicationYear "2019" @default.
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