Identifying metamorphic relations: A data mutation directed approach

Chang‐ai Sun, Hui Jin, SiYi Wu, An Fu, ZuoYi Wang, Wing Kwong Chan
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Abstract

Summary Metamorphic testing (MT) is an effective technique to alleviate the test oracle problem. The principle of MT is to detect failures by checking whether some necessary properties, commonly known as metamorphic relations (MRs), of software under test (SUT) hold among multiple executions of source and follow‐up test cases. Since both the generation of follow‐up test cases and test result verification depend on MRs, the identification of MRs plays a key role in MT, which is an important yet difficult task requiring deep domain knowledge of the SUT. Accordingly, techniques that can direct a tester to identify MRs effectively are desirable. In this paper, we propose MT, a data mutation directed approach to identifying MRs. MT guides a tester to identify MRs by providing a set of data mutation operators and template‐style mapping rules, which not only alleviates the difficulties faced in the process of MR identification but also improves the identification effectiveness. We have further developed a tool to implement the proposed approach and conducted an empirical study to evaluate the MR identification effectiveness of MT and the performance of MRs identified by MT with respect to fault detection capability and statement coverage. The empirical results show that MT is able to identify MRs for numeric programs effectively, and the identified MRs have high fault detection capability and statement coverage. The work presented in this paper advances the field of MT by providing a simple yet practical approach to the MR identification problem.
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识别变质关系:数据突变导向的方法
变形测试(MT)是缓解测试oracle问题的一种有效技术。MT的原理是通过检查被测软件(SUT)的一些必要属性(通常称为变质关系(MRs))是否在源测试用例和后续测试用例的多次执行中成立来检测故障。由于后续测试用例的生成和测试结果的验证都依赖于MRs,因此MRs的识别在机器翻译中起着关键作用,这是一项重要而困难的任务,需要深入的SUT领域知识。因此,能够指导测试人员有效地识别MRs的技术是需要的。在本文中,我们提出了一种基于数据突变的MR识别方法,MT通过提供一组数据突变算子和模板式映射规则来指导测试者识别MR,这不仅减轻了MR识别过程中面临的困难,而且提高了识别效率。我们进一步开发了一种工具来实现所提出的方法,并进行了一项实证研究,以评估机器翻译的MR识别有效性以及机器翻译识别的MR在故障检测能力和语句覆盖率方面的性能。实证结果表明,机器翻译能够有效地识别出数值程序中的MRs,并且识别出的MRs具有较高的故障检测能力和语句覆盖率。本文提出的工作通过提供一种简单而实用的方法来解决MR识别问题,从而推动了MT领域的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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