An Industrial Study of Natural Language Processing Based Test Case Prioritization

Yilin Yang, Xinhai Huang, Xuefei Hao, Zicong Liu, Zhenyu Chen
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引用次数: 12

Abstract

In mobile application development, the frequentsoftware release limits the testing time resource. In order todetect bugs in early phases, researchers proposed various testcase prioritization (TCP) techniques in past decades. In practice, considering that some test case is described or contains text, theresearchers also employed Natural Language Processing (NLP)to assist the TCP techniques. This paper conducted an extensiveempirical study to analyze the performance of three NLP basedTCP technologies, which is based on 15059 test cases from 30industrial projects. The result shows that all of these threestrategies can help to improve the efficiency of software testing, and the Risk strategy achieved the best performance across thesubject programs.
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基于自然语言处理的测试用例优先级的工业研究
在移动应用开发中,频繁的软件发布限制了测试时间资源。为了在早期阶段检测错误,研究人员在过去的几十年里提出了各种测试用例优先级(TCP)技术。在实践中,考虑到某些测试用例被描述或包含文本,研究人员还使用自然语言处理(NLP)来辅助TCP技术。本文基于30个工业项目的15059个测试用例,对三种基于NLP的tcp技术的性能进行了广泛的实证研究。结果表明,这三种策略都可以帮助提高软件测试的效率,并且风险策略在整个主题程序中取得了最好的性能。
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