arXiv：cs.LG· Ocan Sankur (DEVINE), Thierry J\'eron (DEVINE), Nicolas Markey (DEVINE), David Mentr\'e (MERCE-France), Reiya Noguchi·· 4 小时前AI 评分26
基于需求的测试：用博弈论增强强化学习
Requirement-Based Testing: Enhancing Reinforcement Learning with Game Theory
AI 导读
研究者提出一种从自动机形式功能需求出发、面向反应式实现的在线黑盒测试用例自动合成方法，用 Monte Carlo Tree Search 高效选择有潜力的输入。他们把自动机需求视为实现与测试者之间的博弈，据此设计启发式来偏置搜索。实验表明该启发式加速了 Monte Carlo Tree Search 的收敛，从而提升测试性能。
正文
Abstract: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.
| Subjects: | Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG) |
| Cite as: | arXiv:2407.18994 [cs.AI] |
| (or arXiv:2407.18994v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2407.18994 arXiv-issued DOI via DataCite |
|
| Journal reference: | FMCAD 2026 - Formal Methods in Computer-Aided Design, Sep 2026, Graz, France |
Submission history
From: Ocan Sankur [view email] [via CCSD proxy]
[v1]
Fri, 26 Jul 2024 07:59:59 UTC (230 KB)
[v2]
Wed, 7 Oct 2026 13:17:41 UTC (247 KB)
来源：arXiv：cs.LG · arxiv.org