基于变异分析和覆盖准则的回归测试用例集缩减

发布时间：2018-03-23 17:34

  本文选题：软件测试　切入点：测试用例集缩减　出处：《西北工业大学学报》2017年03期

【摘要】：软件测试是在软件开发过程中,用以确认和验证软件质量的主要方法。然而测试用例冗余是软件测试面临的一个重要难题。在回归测试中,人们会根据新的测试需求不断补充大量的测试用例,这也会造成测试用例冗余的出现。虽然现在已有很多工具通过重用测试用例集来降低回归测试的成本,但回归测试依然可能是极其耗时的过程。为此,人们提出了各种方法,对已生成的测试用例集进行缩减。虽然一些现有的数据缩减方法可以减少冗余数据,但往往会削弱排除错误的能力。文章通过引入变异分析和覆盖准则来建立回归测试用例集缩减实验的数学模型,并采用多目标进化优化方法来进行求解优化模型。最后采用Siemens suit基准数据集及工业space大程序进行验证,并使用3种进化优化算法进行测试用例集缩减。事实上,对于SIR小程序,NSGA-Ⅱ算法表现最优;对于space大程序,则是MOEA/D-PBI优于NSGA-Ⅱ。实验结果表明,在保证缺陷检测能力不下降的同时,该方法可以有效地缩减测试用例集。
[Abstract]:Software testing is the main method to confirm and verify the quality of software in the process of software development. However, redundancy of test cases is an important problem in software testing. People continue to add a large number of test cases to new test requirements, which can also lead to redundancy in test cases, although there are many tools that reduce the cost of regression testing by reusing test case sets. But regression testing can still be an extremely time-consuming process. For this reason, a variety of methods have been proposed to reduce the set of test cases that have been generated, although some existing data reduction methods can reduce redundant data. However, it often weakens the ability to eliminate errors. In this paper, the mathematical model of regression test set reduction experiment is established by introducing variation analysis and coverage criterion. Finally, the optimization model is solved by using multi-objective evolutionary optimization method. Finally, Siemens suit datum data set and industrial space program are used to verify, and three evolutionary optimization algorithms are used to reduce test case set. In fact, For SIR Mini Programs, NSGA- 鈪，

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