Abstract
In recent years, with the rapid development of artificial intelligence, reinforcement learning has made significant progress in various fields. However, there are still some challenges when applying reinforcement learning to solve problems in software engineering. The generation of class integration test orders is a key challenge in object-oriented program integration testing. Previous research mainly focused on static couplings and neglected dynamic couplings, leading to inaccurate cost measurement of class integration test orders. In this paper, we propose a reinforcement learning method to generate class integration test orders considering dynamic couplings. Firstly, the concept of dynamic couplings generated by polymorphism is introduced, and a strategy for measuring the stubbing complexity of simulating dynamic dependencies is proposed. Then, we combine this new stubbing complexity with a reinforcement learning method to generate class integration test orders and achieve the optimal result with minimal overall stubbing complexity. Comprehensive experiments show that our proposed approach outperforms other methods in measuring the cost of generating class integration test orders.
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This work is supported by “the Fundamental Research Funds for the Central Universities” under grant No. 2022XSCX18.
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Ding, Y., Zhang, Y., Yuan, G., Li, Y., Jiang, S., Dai, W. (2024). A Reinforcement Learning Method for Generating Class Integration Test Orders Considering Dynamic Couplings. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_8
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