Model-based test case prioritization using cluster analysis: a soft-computing approach

Gökçe, Nida; Belli, Fevzi; Eminli, Mübariz; Dinçer, Bekir Taner

doi:10.3906/elk-1209-109

Cited by 13 publications

(14 citation statements)

References 66 publications

(119 reference statements)

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“…The uniqueness of the proposed clustering methodology is that the identical properties among the test-cases and the faulty functions are identified separately and then combined. The proposed methodology performs better than the other clustering algorithms suggested in the recent state-of-the-art techniques by Gokce et al [38], Mohammed et al [44], Chaurasia et al [45], and Mohammed and Do [46]. Grouping accuracy is based on: (i) the number of clusters to select and (ii) the number of test cases accommodated in each cluster.…”

Section: Discussionmentioning

confidence: 96%

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Novel Fuzzy Clustering Methods for Test Case Prioritization in Software Projects

et al. 2019

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Systematic Regression Testing is essential for maintaining software quality, but the cost of regression testing is high. Test case prioritization (TCP) is a widely used approach to reduce this cost. Many researchers have proposed regression test case prioritization techniques, and clustering is one of the popular methods for prioritization. The task of selecting appropriate test cases and identifying faulty functions involves ambiguities and uncertainties. To alleviate the issue, in this paper, two fuzzy-based clustering techniques are proposed for TCP using newly derived similarity coefficient and dominancy measure. Proposed techniques adopt grouping technology for clustering and the Weighted Arithmetic Sum Product Assessment (WASPAS) method for ranking. Initially, test cases are clustered using similarity//dominancy measures, which are later prioritized using the WASPAS method under both inter- and intra-perspectives. The proposed algorithms are evaluated using real-time data obtained from Software-artifact Infrastructure Repository (SIR). On evaluation, it is inferred that the proposed algorithms increase the likelihood of selecting more relevant test cases when compared to the recent state-of-the-art techniques. Finally, the strengths of the proposed algorithms are discussed in comparison with state-of-the-art techniques.

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Section: Discussionmentioning

confidence: 96%

“…Event-oriented graph models and the importance of events lead to the clustering of events using unsupervised neural network and fuzzy c-means clustering in finding the frequently occurring event groups, thus providing high ranking to their respective test cases [38].…”

Section: Model-based Black-box Techniquesmentioning

confidence: 99%

Novel Fuzzy Clustering Methods for Test Case Prioritization in Software Projects

et al. 2019

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“…Also, Gökçe et al (2015), carried out a search for test case prioritization in model-based testing. They have proposed a new approach that is based on eventoriented graph models.…”

Section: Related Workmentioning

confidence: 99%

Combining Model-Based Testing and Failure Modes and Effects Analysis for Test Case Prioritization: A Software Testing Approach

Meriem¹,

Abdelaziz²

2019

Journal of Computer Science

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Due to The complexity of modern software projects and the increasing size of software systems, it becomes difficult to manually perform tests with limited resources. Also, manual testing cannot assure that the software is tested using all possible combinations of inputs. Therefore, automate software testing activities have become primordial in the Software Development Life Cycle (SDLC). Model-based testing is a prominent validation technique in software testing that uses models of the system under test to automatically generate test cases; this generation leads to a large number of test cases; which cannot exhaustively be executed, due to time and cost constraints. Moreover, the test-cases execution order has an influence on the rate at which faults can be detected. Therefore, it is essential to prioritize test cases in order to execute the most important with limited time and cost. On the other hand, The Failure Mode and Effects Analysis (FMEA) considered as one of the formal techniques to do risk based testing. It offers a structured methodology to identifying the system failure modes, analyzing their effects and setting up controls to risk reducing and improve the quality of systems or products. By applying such a technique to Model-based testing projects, we can benefit from FMEA analysis and the automating in same project. Through this paper, we introduced a new model based testing approach for prioritizing and ranking test cases according to the requirements and failure modes. In the suggested approach we used the Failure Mode and Effect Analysis methodology in model based testing way to automatically generate a set of pair {test case, priority number} to prioritize test cases. It differs from existing approaches in that it generates two types of test cases; requirement-based test cases and failure mode-based test cases. For the purpose of experimental evaluation and analysis, we will compare the suggested approach with some well-known prioritization methods and we will present a case study to illustrate the potential application of the proposed approach in a future work.

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“…Most used approaches are coverage based, fault-based, model-based, history-based, modification based, similarity-based, genetic-based, etc. [13], [15], [16]. Khatibsyarbini et al [16] show that the number of publications related to the test case prioritization is still growing in recent years.…”

Section: Introductionmentioning

confidence: 99%

The Application Of Machine Learning In Test Case Prioritization - A Review

Meçe

Paci

Binjaku

2020

EJECE

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Regression testing is very important but also a very costly and time-consuming activity that ensures the developers that changes in the application will not bring new errors. Retest all, selection of test cases and prioritization of test cases (TCP) approaches are used to enhance the efficiency and effectiveness in regression testing. While test case selection techniques decrease testing time and cost, it can exclude some critical test cases that can detect the faults. On the other hand, test case prioritization considers all test cases and execute them until resources are exhausted or all test cases are executed, while always focusing on the most important ones. Over the years, machine learning has found wide usage in solving different problems in software engineering. Software development and maintenance problems can be defined as learning problems and machine learning techniques have shown to be very effective in solving these problems. In the range of application of machine learning, machine learning techniques have also found usage in solving the test case prioritization problem. In this paper, we investigate the application of machine learning techniques in test case prioritization. We survey some of the most recent studies made in this field and provide information like techniques of machine learning used in TCP process, metrics used to measure the effectiveness of the proposed methods, data used to define the priority of test cases and some advantages or limitations of application of machine learning in TCP.

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Model-based test case prioritization using cluster analysis: a soft-computing approach

Cited by 13 publications

References 66 publications

Novel Fuzzy Clustering Methods for Test Case Prioritization in Software Projects

Novel Fuzzy Clustering Methods for Test Case Prioritization in Software Projects

Combining Model-Based Testing and Failure Modes and Effects Analysis for Test Case Prioritization: A Software Testing Approach

The Application Of Machine Learning In Test Case Prioritization - A Review

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