一句话：提出基于真实RL故障分类的变异测试流水线μPRL，可有效区分强弱测试生成器。
强化学习智能体日益用于复杂序列任务，但生产前测试充分性难以保障。变异测试假设注入故障能模拟真实故障，是一种有前景的方案。本文首先通过代码仓库挖掘建立真实RL故障分类，随后据此推导变异算子，并实现变异测试流水线μPRL。实验结果表明，μPRL能有效区分强测试生成器与弱测试生成器，从而为开发者判断所生成测试场景是否充分提供有用反馈。
Reinforcement Learning (RL) is increasingly adopted to train agents that can deal with complex sequential tasks, such as driving an autonomous vehicle or controlling a humanoid robot. Correspondingly, novel approaches are needed to ensure that RL agents have been tested adequately before going to production. Among them, mutation testing is quite promising, especially under the assumption that the injected faults (mutations) mimic the real ones. In this paper, we first describe a taxonomy of real RL faults obtained by repository mining. Then, we present the mutation operators derived from such real faults and implemented in the tool$\mu \text{PRL}$. Finally, we discuss the experimental results, showing that$\mu \text{PRL}$is effective at discriminating strong from weak test generators, hence providing useful feedback to developers about the adequacy of the generated test scenarios.
DOI 原文 ·
@inproceedings{paperbot8858,
title = {$\mu \text{PRL}$: A Mutation Testing Pipeline for Deep Reinforcement Learning Based on Real Faults},
author = {Deepak-George Thomas and Matteo Biagiola and Nargiz Humbatova and Mohammad Wardat and Gunel Jahangirova and Hridesh Rajan and Paolo Tonella},
booktitle = {ICSE},
year = {2025},
doi = {10.1109/icse55347.2025.00036}
}