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What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing

机译:是什么导致我的测试警报?系统和集成测试中测试警报的自动原因分析

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Driven by new software development processes and testing in clouds, system and integration testing nowadays tends to produce enormous number of alarms. Such test alarms lay an almost unbearable burden on software testing engineers who have to manually analyze the causes of these alarms. The causes are critical because they decide which stakeholders are responsible to fix the bugs detected during the testing. In this paper, we present a novel approach that aims to relieve the burden by automating the procedure. Our approach, called Cause Analysis Model, exploits information retrieval techniques to efficiently infer test alarm causes based on test logs. We have developed a prototype and evaluated our tool on two industrial datasets with more than 14,000 test alarms. Experiments on the two datasets show that our tool achieves an accuracy of 58.3% and 65.8%, respectively, which outperforms the baseline algorithms by up to 13.3%. Our algorithm is also extremely efficient, spending about 0.1s per cause analysis. Due to the attractive experimental results, our industrial partner, a leading information and communication technology company in the world, has deployed the tool and it achieves an average accuracy of 72% after two months of running, nearly three times more accurate than a previous strategy based on regular expressions.
机译:在新的软件开发流程和在云中进行测试的驱动下,当今的系统和集成测试往往会产生大量警报。这样的测试警报给必须手动分析这些警报原因的软件测试工程师带来了几乎无法承受的负担。原因很关键,因为它们决定由哪个涉众负责修复在测试过程中检测到的错误。在本文中,我们提出了一种新颖的方法,旨在通过自动化程序来减轻负担。我们的方法称为原因分析模型,它利用信息检索技术根据测试日志有效地推断出测试警报原因。我们已经开发了原型,并在具有14,000多个测试警报的两个工业数据集上评估了我们的工具。在这两个数据集上进行的实验表明,我们的工具分别达到了58.3 \%和65.8 \%的精度,其性能比基准算法高出13.3%。我们的算法也非常有效,每个原因分析花费大约0.1s。由于具有诱人的实验结果,我们的工业合作伙伴(全球领先的信息和通信技术公司)已部署了该工具,并且在运行两个月后,其平均精度达到了72%,是以前的三倍。基于正则表达式的策略。

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