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| 干扰惰性序列的连续决策模型模糊测试 |
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吴泊逾1, 王凯锐1,2,3, 王亚文1,2,3, 王俊杰1,2,3
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1.中国科学院大学, 北京 100049;2.智能博弈重点实验室 (中国科学院 软件研究所), 北京 100190;3.中国科学院 软件研究所, 北京 100190
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| 摘要: |
| 人工智能技术的应用已经从分类、翻译、问答等相对静态的任务延伸到自动驾驶、机器人控制、博弈等需要和环境进行一系列“交互-行动”才能完成的相对动态的任务. 执行这类任务的模型核心是连续决策算法, 由于面临更高的环境和交互的不确定性, 而且这些任务往往是安全攸关的系统, 其测试技术面临极大的挑战. 现有的智能算法模型测试技术主要集中在单一模型的可靠性、复杂任务多样性测试场景生成、仿真测试等方向, 对连续决策模型的“交互-行动”决策序列没有关注, 导致无法适应, 或者成本效益低下. 提出一个干预惰性“交互-行动”决策序列执行的模糊测试方法IIFuzzing, 在模糊测试框架中, 通过学习“交互-行动”决策序列模式, 预测不会触发失效事故的惰性“交互-行动”决策序列, 并中止这类序列的测试执行, 以提高测试效能. 在4种常见的测试配置中进行实验评估, 结果表明, 与最新的针对连续决策模型的模糊测试相比, IIFuzzing可以在相同时间内多探测16.7%–54.5%的失效事故, 并且事故的多样性也优于基线方法. |
| 关键词: 连续决策模型 马尔可夫决策过程 模糊测试 |
| DOI：10.13328/j.cnki.jos.007320 |
| 分类号:TP311 |
| 基金项目:国家自然科学基金(62232016, 62072442); 中国科学院青年创新促进会; 中国科学院软件研究所基础研究项目(ISCAS-JCZD-202304); 中国科学院软件研究所创新基金重大重点项目(ISCAS-ZD-202302); 中国科学院软件研究所2024年度“创新团队” (2024-66) |
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| Fuzz Testing for Sequential Decision-making Model with Intervening Inert Sequences |
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WU Bo-Yu1, WANG Kai-Rui1,2,3, WANG Ya-Wen1,2,3, WANG Jun-Jie1,2,3
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1.University of Chinese Academy of Sciences, Beijing 100049, China;2.State Key Laboratory of Intelligent Game (Institute of Software, Chinese Academy of Sciences), Beijing 100190, China;3.Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
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| Abstract: |
| The application of artificial intelligence technology has extended from relatively static tasks such as classification, translation, and question answering to relatively dynamic tasks that require a series of “interaction-action” with the environment to be completed, like autonomous driving, robotic control, and games. The core of the model for executing such tasks is the sequential decision-making (SDM) algorithm. As it faces higher uncertainties of the environment and interaction and these tasks are often safety-critical systems, the testing techniques are confronted with great challenges. The existing testing technologies for intelligent algorithm models mainly focus on the reliability of a single model, the generation of diverse test scenarios for complex tasks, simulation testing, etc., while no attention is paid to the “interaction-action” decision sequence of the SDM model, leading to unadaptability or low cost-effectiveness. In this study, a fuzz testing method named IIFuzzing for intervening in the execution of inert “interaction-action” decision sequences is proposed. In the fuzz testing framework, by learning the “interaction-action” decision sequence pattern, the inert “interaction-action” decision sequences that will not trigger failure accidents are predicted and the testing execution of such sequences is terminated to improve the testing efficiency. The experimental evaluations are conducted in four common test configurations, and the results show that compared with the latest fuzz testing for SDM models, IIFuzzing can detect 16.7%–54.5% more failure accidents within the same time, and the diversity of accidents is also better than that of the baseline approach. |
| Key words: sequential decision-making (SDM) model Markov decision process (MDP) fuzz testing |
