基于机器学习的自动驾驶汽车软件仿真测试选择
Machine Learning-based Test Selection for Simulation-based Testing of Self-driving Cars Software
摘要：
仿真平台促进了自动驾驶汽车（SDC）等新兴网络物理系统（CPS）的开发，因为它们比现场操作测试用例更有效，危险性更低。尽管如此，在模拟环境中彻底测试SDC仍然具有挑战性，因为SDC必须在大量长时间运行的测试用例中进行测试。过去关于软件测试优化的结果表明，并不是所有的测试用例都对建立测试对象的质量和可靠性的信心有同样的贡献，并且可以跳过“安全和无信息”测试用例的执行以减少测试工作。然而，这个问题只是部分解决的背景下，SDC仿真平台。在本文中，我们调查测试选择策略，以增加基于模拟的测试的成本效益的背景下，SDC。我们提出了一种称为SDC-Scissor（SDC cost-effeCtIve testSt SelectOR）的方法，该方法利用机器学习（ML）策略来识别和跳过不太可能在执行SDC之前检测到故障的测试用例。我们的评估表明，SDC-Scissor优于基线。使用Logistic模型，我们在选择导致错误的测试时达到了70%的准确率，65%的精确率和80%的召回率，并提高了测试的成本效益。具体来说，SDC-Scissor避免了50%不必要的测试，并且优于两个基线策略。作为对现有工作的补充，我们还将SDC-Scissor集成到汽车领域的工业组织中，以演示如何在工业环境中使用它。
英文摘要：
Simulation platforms facilitate the development of emerging Cyber-Physical Systems (CPS) like self-driving cars (SDC) because they are more efficient and less dangerous than field operational test cases. Despite this, thoroughly testing SDCs in simulated environments remains challenging because SDCs must be tested in a sheer amount of long-running test cases. Past results on software testing optimization have shown that not all the test cases contribute equally to establishing confidence in test subjects' quality and reliability, and the execution of "safe and uninformative" test cases can be skipped to reduce testing effort. However, this problem is only partially addressed in the context of SDC simulation platforms. In this paper, we investigate test selection strategies to increase the cost-effectiveness of simulation-based testing in the context of SDCs. We propose an approach called SDC-Scissor (SDC coSt-effeCtIve teSt SelectOR) that leverages Machine Learning (ML) strategies to identify and skip test cases that are unlikely to detect faults in SDCs before executing them. Our evaluation shows that SDC-Scissor outperforms the baselines. With the Logistic model, we achieve an accuracy of 70%, a precision of 65%, and a recall of 80% in selecting tests leading to a fault and improved testing cost-effectiveness. Specifically, SDC-Scissor avoided the execution of 50% of unnecessary tests as well as outperformed two baseline strategies. Complementary to existing work, we also integrated SDC-Scissor into the context of an industrial organization in the automotive domain to demonstrate how it can be used in industrial settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
