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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2407.18994 |
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| _version_ | 1866909272006197248 |
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| author | Sankur, Ocan Jéron, Thierry Markey, Nicolas Mentré, David Noguchi, Reiya |
| author_facet | Sankur, Ocan Jéron, Thierry Markey, Nicolas Mentré, David Noguchi, Reiya |
| contents | We consider the automatic online synthesis of black-box test cases from functional requirements specified as automata for reactive implementations. The goal of the tester is to reach some given state, so as to satisfy a coverage criterion, while monitoring the violation of the requirements. We develop an approach based on Monte Carlo Tree Search, which is a classical technique in reinforcement learning for efficiently selecting promising inputs. Seeing the automata requirements as a game between the implementation and the tester, we develop a heuristic by biasing the search towards inputs that are promising in this game. We experimentally show that our heuristic accelerates the convergence of the Monte Carlo Tree Search algorithm, thus improving the performance of testing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_18994 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Online Test Synthesis From Requirements: Enhancing Reinforcement Learning with Game Theory Sankur, Ocan Jéron, Thierry Markey, Nicolas Mentré, David Noguchi, Reiya Artificial Intelligence Computer Science and Game Theory Machine Learning We consider the automatic online synthesis of black-box test cases from functional requirements specified as automata for reactive implementations. The goal of the tester is to reach some given state, so as to satisfy a coverage criterion, while monitoring the violation of the requirements. We develop an approach based on Monte Carlo Tree Search, which is a classical technique in reinforcement learning for efficiently selecting promising inputs. Seeing the automata requirements as a game between the implementation and the tester, we develop a heuristic by biasing the search towards inputs that are promising in this game. We experimentally show that our heuristic accelerates the convergence of the Monte Carlo Tree Search algorithm, thus improving the performance of testing. |
| title | Online Test Synthesis From Requirements: Enhancing Reinforcement Learning with Game Theory |
| topic | Artificial Intelligence Computer Science and Game Theory Machine Learning |
| url | https://arxiv.org/abs/2407.18994 |