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Regression test suite minimization using integer linear programming model

机译:使用整数线性规划模型最小化回归测试套件

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Software testers always face the dilemma of whether to retest the software with all the test cases or select a few of them on the basis of their fault detection ability. This paper introduces a novel approach to minimizing the test suite as an integer linear programming problem with optimal results. The minimization method uses the cohesion values of the program parts affected by the changes made to the program. The hypothesis is that the program parts with low cohesion values are more prone to errors. This assumption is validated on the mutation fault detection ability of the test cases. The experimental study carried out on 30 programs evaluates the effectiveness and usefulness of the proposed framework. The experimental results show that the minimized test suite can efficiently reveal the errors and ensure acceptable software quality. Copyright (C) 2017 John Wiley & Sons, Ltd.
机译:软件测试人员始终面临着用所有测试用例重新测试软件还是根据其故障检测能力选择其中几个的难题。本文介绍了一种将测试套件最小化为具有最佳结果的整数线性规划问题的新颖方法。最小化方法使用受程序更改影响的程序部分的内聚值。假设是,具有低内聚值的程序部分更容易出错。该假设在测试案例的突变故障检测能力上得到了验证。在30个程序上进行的实验研究评估了所提出框架的有效性和实用性。实验结果表明,最小化的测试套件可以有效地揭示错误并确保可接受的软件质量。版权所有(C)2017 John Wiley&Sons,Ltd.

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