论文标题
自动驾驶汽车软件中基于成本效益的基于仿真的测试选择
Cost-effective Simulation-based Test Selection in Self-driving Cars Software
论文作者
论文摘要
模拟环境对于连续开发复杂的网络物理系统（例如自动驾驶汽车（SDC））至关重要。对SDC的基于仿真测试的先前结果表明，许多自动生成的测试并不能强烈促进SDC故障的识别，因此并不有助于提高SDC的质量。由于运行这种“非信息”测试通常会导致浪费计算资源和SDC的测试成本的急剧增加，因此测试人员应避免使用它们。但是，在运行它们之前识别“非信息性”测试仍然是一个悬而未决的挑战。因此，本文提出了SDCSCISSOR，该框架利用机器学习（ML）来识别SDC测试，这些测试不太可能检测到测试中的SDC软件中的故障，从而使测试人员能够跳过其执行并大大提高基于SDCS软件的基于SINUTAUTION测试的成本效益。我们在两个以22'652测试为特征的大型数据集上使用六个ML模型的评估表明，SDC-Scissor的分类F1得分最高96％。此外，我们的结果表明，SDC-Scissor在确定每个时间单元的更多失败测试方面优于随机基线。 网页和视频：https：//github.com/christianbirchler/sdc-scissor
Simulation environments are essential for the continuous development of complex cyber-physical systems such as self-driving cars (SDCs). Previous results on simulation-based testing for SDCs have shown that many automatically generated tests do not strongly contribute to identification of SDC faults, hence do not contribute towards increasing the quality of SDCs. Because running such "uninformative" tests generally leads to a waste of computational resources and a drastic increase in the testing cost of SDCs, testers should avoid them. However, identifying "uninformative" tests before running them remains an open challenge. Hence, this paper proposes SDCScissor, a framework that leverages Machine Learning (ML) to identify SDC tests that are unlikely to detect faults in the SDC software under test, thus enabling testers to skip their execution and drastically increase the cost-effectiveness of simulation-based testing of SDCs software. Our evaluation concerning the usage of six ML models on two large datasets characterized by 22'652 tests showed that SDC-Scissor achieved a classification F1-score up to 96%. Moreover, our results show that SDC-Scissor outperformed a randomized baseline in identifying more failing tests per time unit. Webpage & Video: https://github.com/ChristianBirchler/sdc-scissor
