Input Domain Reduction through Irrelevant Variable Removal and Its Effect on Local, Global, and Hybrid Search-Based Structural Test Data Generation
Input Domain Reduction through Irrelevant Variable Removal and Its Effect on Local, Global, and Hybrid Search-Based Structural Test Data Generation
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DOI：
10.1109/tse.2011.18
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发表时间：
2012-03
影响因子：
7.4
通讯作者：
Phil McMinn;M. Harman;Kiran Lakhotia;Y. Hassoun;J. Wegener
中科院分区：
文献类型：
--
作者：
Phil McMinn;M. Harman;Kiran Lakhotia;Y. Hassoun;J. Wegener
基于搜索的测试数据生成将测试目标重新定义为适应度函数，使得测试输入可以通过选择的基于搜索的优化算法自动生成。优化算法在潜在输入的空间中搜索，在适应度函数的指导下，寻找那些“符合目的”的输入。潜在输入的搜索空间可能非常大，即使对于被测试的非常小的系统也是如此。当然，它的大小是影响任何基于搜索的方法的性能的关键决定因素。然而，尽管在基于搜索的软件测试方面有大量的工作，但文献中很少涉及搜索空间减少对性能的影响。本文提出了一种基于程序切片的静态依赖分析方法，用于支持搜索空间的缩减。本文对这种方法在开源和工业生产代码中的应用进行了理论和实证分析。研究结果表明，输入域缩减对局部搜索、全局搜索和混合搜索的性能有显著影响，而纯随机搜索不受影响。
Search-Based Test Data Generation reformulates testing goals as fitness functions so that test input generation can be automated by some chosen search-based optimization algorithm. The optimization algorithm searches the space of potential inputs, seeking those that are “fit for purpose,” guided by the fitness function. The search space of potential inputs can be very large, even for very small systems under test. Its size is, of course, a key determining factor affecting the performance of any search-based approach. However, despite the large volume of work on Search-Based Software Testing, the literature contains little that concerns the performance impact of search space reduction. This paper proposes a static dependence analysis derived from program slicing that can be used to support search space reduction. The paper presents both a theoretical and empirical analysis of the application of this approach to open source and industrial production code. The results provide evidence to support the claim that input domain reduction has a significant effect on the performance of local, global, and hybrid search, while a purely random search is unaffected.