Calibrated Mutation Testing
Calibrated Mutation Testing
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DOI：
10.1109/icstw.2011.57
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发表时间：
2011-03
期刊：
影响因子：
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通讯作者：
Jaechang Nam;David Schuler;A. Zeller
中科院分区：
文献类型：
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作者：
Jaechang Nam;David Schuler;A. Zeller
在突变测试中，人为的缺陷被插入到程序中，以测量测试集的质量并提供改进的方法。这些缺陷是使用预定义的变异操作符生成的，这是受到程序员容易犯错误的启发。由于不同的程序员和项目之间的错误类型不同，突变测试可能会通过从过去的缺陷中学习来改进-与过去的缺陷类似的突变样本是否有助于开发比随机选择的突变样本更好的测试？在本文中，我们提出了第一种方法，使用软件存储库挖掘技术校准突变测试的缺陷历史的项目。此外，我们提供了一个校准的突变测试的Jaxen项目的实施和评估。然而，第一个结果表明，校准突变测试不能优于随机选择策略。
During mutation testing, artificial defects are inserted into a program, in order to measure the quality of a test suite and to provide means for improvement. These defects are generated using predefined mutation operators-inspired by faults that programmers tend to make. As the type of faults varies between different programmers and projects, mutation testing might be improved by learning from past defects-Does a sample of mutations similar to past defects help to develop better tests than a randomly chosen sample of mutations? In this paper, we present the first approach that uses software repository mining techniques to calibrate mutation testing to the defect history of a project. Furthermore, we provide an implementation and evaluation of calibrated mutation testing for the Jaxen project. However, first results indicate that calibrated mutation testing cannot outperform random selection strategies.