一句话：提出好奇心驱动的黑盒模糊测试方法 CureFuzz，高效发现序列决策模型中多样化的崩溃触发场景。
序列决策过程（SDP）广泛存在于自动驾驶、机器人控制与交通管理等复杂现实任务中，基于深度学习的求解方案虽日趋成熟，却容易学到不安全行为，在安全攸关场景下风险巨大。然而，如何构建测试框架以识别多样化的崩溃触发场景仍是一个开放难题。本文提出 CureFuzz，一种面向序列决策模型（SDM）的好奇心驱动黑盒模糊测试方法：其好奇心机制使模糊测试器能够有效探索新颖且多样的场景，从而提高崩溃触发场景的发现率；同时引入多目标种子选择技术，在探索新颖场景与生成崩溃触发场景之间取得平衡，优化整体模糊测试过程。作者在多种 SDM 上进行了评估，实验结果表明 CureFuzz 在发现的故障总数与崩溃触发场景类型数上均大幅超越当前最先进方法；此外还证明其发现的崩溃触发场景可用于修复 SDM，说明 CureFuzz 是测试并优化 SDM 性能的有效工具。
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent advances in Deep Learning (DL) have led to mature solutions for solving these complex problems, SDMs remain vulnerable to learning unsafe behaviors, posing significant risks in safety-critical applications. However, developing a testing framework for SDMs that can identify a diverse set of crash-triggering scenarios remains an open challenge. To address this, we propose CureFuzz, a novel curiosity-driven black-box fuzz testing approach for SDMs. CureFuzz proposes a curiosity mechanism that allows a fuzzer to effectively explore novel and diverse scenarios, leading to improved detection of crash-triggering scenarios. Additionally, we introduce a multi-objective seed selection technique to balance the exploration of novel scenarios and the generation of crash-triggering scenarios, thereby optimizing the fuzzing process. We evaluate CureFuzz on various SDMs and experimental results demonstrate that CureFuzz outperforms the state-of-the-art method by a substantial margin in the total number of faults and distinct types of crash-triggering scenarios. We also demonstrate that the crash-triggering scenarios found by CureFuzz can repair SDMs, highlighting CureFuzz as a valuable tool for testing SDMs and optimizing their performance.
@inproceedings{paperbot11360,
title = {Curiosity-Driven Testing for Sequential Decision-Making Process},
author = {Junda He and Zhou Yang and Jieke Shi and Chengran Yang and Kisub Kim and Bowen Xu and Xin Zhou and David Lo},
booktitle = {ICSE},
year = {2024},
doi = {10.1145/3597503.3639149}
}