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

He Jiang, Xiaochen Li, Z. Yang, J. Xuan
{"title":"What Causes My Test Alarm? Automatic Cause Analysis for Test Alarms in System and Integration Testing","authors":"He Jiang, Xiaochen Li, Z. Yang, J. Xuan","doi":"10.1109/ICSE.2017.71","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":6505,"journal":{"name":"2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)","volume":"47 1","pages":"712-723"},"PeriodicalIF":0.0000,"publicationDate":"2017-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"53","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICSE.2017.71","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 53

Abstract

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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
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。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Adaptive Unpacking of Android Apps Symbolic Model Extraction for Web Application Verification On Cross-Stack Configuration Errors Syntactic and Semantic Differencing for Combinatorial Models of Test Designs Fuzzy Fine-Grained Code-History Analysis
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1