Prioritising test cases to improve the software fault detection using MCDM methods

Maryam Mohammadi Sarpiri, Keyvan Mohebbi, Ali Jamshidi
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Abstract

To decrease the cost of software testing, we can run a subset of test cases, but this may result in residual faults. To keep the efficiency of testing, the most important test cases should be selected through a prioritisation approach. Such prioritisation requires the assessment of different criteria, so it can be formulated as a multi-criteria decision-making (MCDM) problem. This research proposes an approach to select the proper subset of test cases using the MCDM methods. Three MCDM methods, namely, fuzzy SAW, fuzzy VIKOR, and fuzzy TOPSIS are applied to prioritise the test cases concerning various criteria. To select a subset of test cases, a threshold is determined for different pairs of the most important criteria. The proposed approach is applied to an actual e-government software system with two variants. The experimental evaluations indicate the efficiency of this approach with respect to both the failure rate and the average percentage of fault detection metrics.
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对测试用例进行优先级排序,以改进使用MCDM方法的软件故障检测
为了减少软件测试的成本,我们可以运行测试用例的子集,但是这可能会导致残留的错误。为了保持测试的效率,应该通过优先级方法来选择最重要的测试用例。这种优先排序需要评估不同的标准,因此可以将其表述为多标准决策(MCDM)问题。本研究提出了一种使用MCDM方法选择测试用例的适当子集的方法。应用模糊SAW、模糊VIKOR和模糊TOPSIS三种MCDM方法对不同标准的测试用例进行优先级排序。为了选择测试用例的子集,需要为最重要的标准的不同对确定一个阈值。将该方法应用于一个具有两个变体的实际电子政务软件系统。实验结果表明,该方法在故障检测指标的失败率和平均百分比方面都是有效的。
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来源期刊
CiteScore
1.40
自引率
0.00%
发文量
23
期刊介绍: IJICA proposes and fosters discussion on all new computing paradigms and corresponding applications to solve real-world problems. It will cover all aspects related to evolutionary computation, quantum-inspired computing, swarm-based computing, neuro-computing, DNA computing and fuzzy computing, as well as other new computing paradigms
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