Q-learning is an attractive option for GUI testing, allowing for sophisticated test generation strategies that learn and exploit effective GUI interactions. However, learning comprehensive models requires long test sessions. This issue is exacerbated by the needs of both testers, who might want to run multiple testing sessions to fine-tune the test strategy to their applications under test, and researchers, who might want to experiment with multiple alternative approaches. To address these concerns, this paper presents $GT^{PQL}$, a testing tool that supports GUI testing with a parallel deployment Q-learning, and that can be flexibly configured and extended with multiple state-space abstractions and Q-leaning variants.
Mobilio, M., Clerissi, D., Denaro, G., Mariani, L. (2023). GUI Testing to the Power of Parallel Q-Learning. In Proceedings - International Conference on Software Engineering (pp.55-59). IEEE Computer Society [10.1109/ICSE-Companion58688.2023.00024].
GUI Testing to the Power of Parallel Q-Learning
Mobilio M.;Clerissi D.;Denaro G.;Mariani L.
2023
Abstract
Q-learning is an attractive option for GUI testing, allowing for sophisticated test generation strategies that learn and exploit effective GUI interactions. However, learning comprehensive models requires long test sessions. This issue is exacerbated by the needs of both testers, who might want to run multiple testing sessions to fine-tune the test strategy to their applications under test, and researchers, who might want to experiment with multiple alternative approaches. To address these concerns, this paper presents $GT^{PQL}$, a testing tool that supports GUI testing with a parallel deployment Q-learning, and that can be flexibly configured and extended with multiple state-space abstractions and Q-leaning variants.
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