A Hybrid Approach of Clustering and Time-Aware Based Novel Test Case Prioritization Technique

Chaurasia, Geetanjali; Agarwal, Sonali

doi:10.14257/ijdta.2016.9.4.02

Cited by 3 publications

(4 citation statements)

References 28 publications

(35 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%

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%

Novel Fuzzy Clustering Methods for Test Case Prioritization in Software Projects

et al. 2019

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“…[134], [46], [48]-[51] 2 Clustering [10], [12], [42], [43], [53], [57], [60], [61], [69], [71], [79], [85], [21], [89], [90], [93], [101]- [103], [111], [114], [117], [118], [25], [126], [129], [130], [132], [133], [27], [31]- [35] 35…”

Section: Discussionmentioning

confidence: 99%

“…As for lab programs, some institution may have established lab with a good team could proceed with the own study program. Also similar with the issues in industrial programs, the confidential information of the programs may reduce the availability of program to be utilized in other works [14], [57], [89], [90]. As the distribution of size of study programs used, the information illustrated in Figure 11.…”

Section: Figure 10 Percentage Distribution Of Study Programsmentioning

confidence: 99%

“…The second largest utilized technique reported in collated studies is clustering techniques with 32% contributed by these notable works [34], [51], [53]- [57]. Clustering technique look like classification which aim to grouping the inputs but they difference in term of the needs of training and testing dataset.…”

Section: Figure 6 Percentages Distribution Of ML Techniquesmentioning

confidence: 99%

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Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

et al. 2021

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Software quality can be assured by passing the process of software testing. However, software testing process involve many phases which lead to more resources and time consumption. To reduce these downsides, one of the approaches is to adopt test case prioritization (TCP) where numerous works has indicated that TCP do improve the overall software testing performance. TCP does have several kinds of techniques which have their own strengths and weaknesses. As for this review paper, the main objective of this paper is to examine deeper on machine learning (ML) techniques based on research questions created. The research method for this paper was designed in parallel with the research questions. Consequently, 110 primary studies were selected where, 58 were journal articles, 50 were conference papers and 2 considered as others articles. For overall result, it can be said that ML techniques in TCP has trending in recent years yet some improvements are certainly welcomed. There are multiple ML techniques available, in which each technique has specified potential values, advantages, and limitation. It is notable that ML techniques has been considerably discussed in TCP approach for software testing.

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Test Case Prioritization Using Clustering Approach for Object Oriented Software

Yadav

Dutta

2019

International Journal of Information System Modeling and Design

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In the software maintenance activity, regression testing is performed for validing modified source code. Regression testing ensures that the modified code would not affect the earlier tested program. Due to a constraint of resources and time, regression testing is a time-consuming process and it is a very expensive activity. During the regression testing, a set of the test case and the existing test cases are reused. To minimize the cost of regression testing, the researchers proposed a test case prioritization based on clustering techniques. In recent years, research on regression testing has made significant progress for object-oriented software. The empirical results show the importance of K-mean clustering algorithm used to achieve an effective result. They found from experimental results that their proposed approach achieves the highest faults detected value than others.

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A Hybrid Approach of Clustering and Time-Aware Based Novel Test Case Prioritization Technique

Abstract: Abstract

Cited by 3 publications

References 28 publications

Novel Fuzzy Clustering Methods for Test Case Prioritization in Software Projects

Novel Fuzzy Clustering Methods for Test Case Prioritization in Software Projects

Trend Application of Machine Learning in Test Case Prioritization: A Review on Techniques

Test Case Prioritization Using Clustering Approach for Object Oriented Software

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