没有覆盖的故障聚类

Mojdeh Golagha, Constantin Lehnhoff, A. Pretschner, H. Ilmberger
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引用次数: 12

摘要

在汽车工业中开发和集成软件是一项复杂的任务,需要进行大量的测试。测试和调试中的一个重要成本因素是分析失败测试所需的时间。在回归测试的上下文中,通常,由于一些潜在的错误,大量的测试失败。因此,根据潜在错误对失败测试进行聚类可以帮助减少所需的分析时间。在本文中,我们提出了一种聚类技术来分组失败的硬件在环测试基于非基于代码的特征,从三个不同的来源检索。为了有效地减少分析工作量,聚类工具为每个聚类选择一个代表性测试。而不是分析所有失败的测试,测试人员只检查有代表性的测试来发现潜在的错误。我们在一家大型汽车公司中使用86次回归测试、8743次失败测试和1531次故障来评估我们的解决方案的有效性和效率。结果表明,使用聚类工具,测试人员仅通过检查代表性测试,就能减少60%以上的分析时间,发现80%以上的故障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Failure clustering without coverage
Developing and integrating software in the automotive industry is a complex task and requires extensive testing. An important cost factor in testing and debugging is the time required to analyze failing tests. In the context of regression testing, usually, large numbers of tests fail due to a few underlying faults. Clustering failing tests with respect to their underlying faults can, therefore, help in reducing the required analysis time. In this paper, we propose a clustering technique to group failing hardware-in-the-loop tests based on non-code-based features, retrieved from three different sources. To effectively reduce the analysis effort, the clustering tool selects a representative test for each cluster. Instead of analyzing all failing tests, testers only inspect the representative tests to find the underlying faults. We evaluated the effectiveness and efficiency of our solution in a major automotive company using 86 regression test runs, 8743 failing tests, and 1531 faults. The results show that utilizing our clustering tool, testers can reduce the analysis time more than 60% and find more than 80% of the faults only by inspecting the representative tests.
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