用于具有不稳定测试执行的网络物理系统的增量调试
Delta Debugging for Cyber-Physical Systems with Flaky Test Executions
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中文总结 AI 辅助
针对网络物理系统模拟器不稳定致故障难调试问题，提出结合统计分析、重复执行和环境感知约简的三种增量调试算法，经案例系统评估，可减少调试时间、提高故障重现性，为调试CPS提供实用基础。
中文摘要 AI 辅助
基于仿真的测试广泛用于验证网络物理系统（CPS），但现代CPS模拟器常表现出非确定性（不稳定）行为，使故障难重现和调试。虽增量调试对确定性系统有效，但在随机环境中其假设不成立。本文提出三种增量调试算法，结合统计故障分析、重复执行和环境感知约简，为随机CPS隔离最小故障诱导测试输入。在工业电梯调度系统和自主移动机器人两个案例系统上评估，结果表明所提方法大幅减少调试时间，保留原始故障行为，还常提高故障重现性，为调试CPS提供实用基础。
英文摘要
Simulation-based testing is widely used to validate Cyber-Physical Systems (CPSs), yet modern CPS simulators frequently exhibit non-deterministic (flaky) behavior, making failures difficult to reproduce and debug. Although delta debugging has proven effective for deterministic systems, its underlying assumptions do not hold in stochastic environments. This paper presents three delta debugging algorithms that combine statistical failure analysis, repeated executions, and environment-aware reduction to isolate minimal failure-inducing test inputs for stochastic CPSs. We evaluate the proposed techniques on two complementary case study systems: an industrial elevator dispatching system employing stochastic optimization and an autonomous mobile robot exhibiting simulator-induced non-determinism. The results show that the proposed approaches substantially reduce debugging time while preserving the original failure behavior. More importantly, we observe that minimizing failure-inducing test inputs frequently increases failure reproducibility compared with the original executions. By eliminating execution segments that introduce incidental stochastic effects, the reduced test inputs isolate the causal conditions of the failure and consistently reproduce it with higher probability. These findings suggest that delta debugging not only simplifies failure analysis but also mitigates execution flakiness, providing a practical foundation for debugging CPSs.
