什么导致我的测试警报?系统与集成测试中测试告警的自动原因分析

He Jiang, Xiaochen Li, Z. Yang, J. Xuan
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引用次数: 53

摘要

在新的软件开发过程和云测试的驱动下,系统和集成测试现在往往会产生大量的警报。这样的测试警报给软件测试工程师带来了难以承受的负担,他们不得不手动分析这些警报的原因。原因是至关重要的,因为它们决定了哪些涉众负责修复测试期间检测到的错误。在本文中,我们提出了一种新的方法,旨在通过自动化过程来减轻负担。我们的方法,称为原因分析模型,利用信息检索技术有效地推断基于测试日志的测试报警原因。我们已经开发了一个原型,并在两个工业数据集上评估了我们的工具,其中包含超过14,000个测试警报。在两个数据集上的实验表明,我们的工具分别达到了58.3%和65.8%的准确率,比基线算法高出13.3%。我们的算法也非常高效,每次原因分析花费大约0.1s。由于具有吸引力的实验结果,我们的工业合作伙伴,一家世界领先的信息和通信技术公司,已经部署了该工具,经过两个月的运行,它达到了72%的平均准确率,比以前基于正则表达式的策略准确率提高了近三倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing
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.
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