一句话：报告用探索驱动的强化学习/进化策略为飞控系统生成多样故障测试用例的工业经验。
关键软件系统需要严格测试以发现失效场景，但人工测试难以高效覆盖。本文报告在真实研发飞控系统上开展工业测试的经验，采用启发式测试方法：以进化策略为基础，并加入内在动机以驱动探索，从而生成多样化的测试用例，揭示系统不同潜在故障。作者进一步分析进化发现的测试用例以定位系统薄弱点。结果表明，该方法能高效、低成本地提供高质量测试用例，有助于提升航空电子系统的可靠性与鲁棒性。
Critical software systems require stringent testing to identify possible failure cases, which can be difficult to find using manual testing. In this study, we report our industrial experience in testing a realistic R&D flight control system using a heuristic based testing method. Our approach utilizes evolutionary strategies augmented with intrinsic motivation to yield a diverse range of test cases, each revealing different potential failure scenarios within the system. This diversity allows for a more comprehensive identification and understanding of the system’s vulnerabilities. We analyze the test cases found by evolution to identify the system’s weaknesses. The results of our study show that our approach can be used to improve the reliability and robustness of avionics systems by providing high-quality test cases in an efficient and cost-effective manner.
@inproceedings{paperbot8534,
title = {Exploration-Driven Reinforcement Learning for Avionic System Fault Detection (Experience Paper)},
author = {Paul-Antoine Le Tolguenec and Emmanuel Rachelson and Yann Besse and Florent Teichteil-Koenigsbuch and Nicolas Schneider and Hélène Waeselynck and Dennis Wilson},
booktitle = {ISSTA},
year = {2024},
doi = {10.1145/3650212.3680331}
}