一句话：提出结合多目标进化搜索与决策树分类的可学习进化测试算法，能更高效地为视觉控制系统生成关键测试场景并刻画关键区域。
基于视觉的控制系统是自动驾驶等自主车辆系统的关键使能技术，但其输入空间复杂且多维，给测试带来挑战。本文提出一种可学习的进化测试算法，将多目标种群搜索与决策树分类模型相结合：分类模型引导搜索更快生成导致失败的关键测试场景，而搜索算法反过来精化分类模型，使其准确刻画测试输入空间中可能包含多数关键场景的关键区域。在工业汽车系统上的评估表明，该算法优于基线进化搜索算法，生成的关键测试场景数量多出78%，并能准确刻画被测系统的关键区域，识别可能导致系统失败的条件。
Vision-based control systems are key enablers of many autonomous vehicular systems, including self-driving cars. Testing such systems is complicated by complex and multidimensional input spaces. We propose an automated testing algorithm that builds on learnable evolutionary algorithms. These algorithms rely on machine learning or a combination of machine learning and Darwinian genetic operators to guide the generation of new solutions (test scenarios in our context). Our approach combines multiobjective population-based search algorithms and decision tree classification models to achieve the following goals: First, classification models guide the search-based generation of tests faster towards critical test scenarios (i.e., test scenarios leading to failures). Second, search algorithms refine classification models so that the models can accurately characterize critical regions (i.e., the regions of a test input space that are likely to contain most critical test scenarios). Our evaluation performed on an industrial automotive automotive system shows that: (1) Our algorithm outperforms a baseline evolutionary search algorithm and generates 78% more distinct, critical test scenarios compared to the baseline algorithm. (2) Our algorithm accurately characterizes critical regions of the system under test, thus identifying the conditions that are likely to lead to system failures.
@inproceedings{paperbot4532,
title = {Testing vision-based control systems using learnable evolutionary algorithms},
author = {Raja Ben Abdessalem and Shiva Nejati and Lionel C. Briand and Thomas Stifter},
booktitle = {ICSE},
year = {2018},
doi = {10.1145/3180155.3180160}
}