UI Test case prioritization on an industrial setting: A search for the best criteria

Magalhães, Cláudio; Mota, Alexandre; Momente, Luis

doi:10.1007/s11219-021-09549-y

Cited by 3 publications

(2 citation statements)

References 26 publications

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“…Finally, the reliability and superiority of the proposed method in improving the abnormal detection ability of portable multidimensional control software testing are demonstrated by simulation test analysis. [9,10], as shown in Figure 1.…”

Section: Literature Reviewmentioning

confidence: 90%

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

Qiu

2022

Security and Communication Networks

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In order to improve the anomaly detection ability of portable multidimensional control software test data, a software test data anomaly detection method based on K-means clustering is proposed. The abnormal data distribution structure model of portable multidimensional control software testing is constructed. The fuzzy semantic feature reconstruction method is adopted to identify the fuzzy parameters of portable multidimensional control software and extract the feature quantity of associated information. According to the evolution distribution of associated features, the joint combination feature analysis method is adopted to realize the fuzzy clustering center detection of abnormal data of portable multidimensional control software test, and the fusion of abnormal feature distribution is carried out to complete the joint multidimensional feature detection. The K-means clustering method is used for effective data combination control in portable multidimensional control software to extract and detect abnormal features of test data in portable multidimensional control software. Experimental results show that the accuracy of anomaly detection of software test data by the proposed method is always greater than 0.8. Conclusion. Using this method to detect abnormal data of portable multidimensional control software test has higher accuracy and better detection performance.

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Section: Literature Reviewmentioning

confidence: 90%

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

Qiu

2022

Security and Communication Networks

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“…As the result of this review, there were several notable findings which could give a guide for future work. The discovered notable findings were: [26], [52], [55], [56], [58], [76], [77], [80]- [83], [28], [84], [87], [94], [95], [100], [104]- [106], [108], [109], [44], [110], [112], [113], [115], [116], [119]- [121], [124], [125], [45],…”

Section: Discussionmentioning

confidence: 99%

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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HOTCP: Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints

Wagh,

Puri,

Waykar

et al. 2024

J. Info. Know. Mgmt.

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As a result of late detection and resource limitations during any software evaluation, there have been several software-related breakdowns or malfunctions. Many people have begun focussing on the test cases or alternatively the priority of validation suites after identifying the difficulties in the regression testing process of any product. The test case prioritisation technique is presented as a solution to this problem. It increases the fault detection rate. Earlier research studies have been implemented many techniques, but the rate of fault detection is not up to the mark. To overcome this drawback, we proposed HOTCP (Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints) model, which includes two steps: first is test case generation and the next one is test case prioritisation. The test case is generated from the released software. Consequently, test case prioritisation will be done by the optimisation strategy, in which the multi-objective function will be defined based on the constraints like statement coverage, branch coverage, contribution index and fault exposing potential. For this optimisation process, a new algorithm is proposed termed as CCCOA (Customised Coot and Chimp Optimisation Algorithm). The COOT optimisation and Chimp optimisation algorithms are combined in this algorithm. The system produces prioritised test cases, and the performance of the proposed method is validated in comparison to the traditional methods in terms of several metrics.

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UI Test case prioritization on an industrial setting: A search for the best criteria

Cited by 3 publications

References 26 publications

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

Application of Software Data Analysis Model Based on K-Means Clustering Algorithm

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

HOTCP: Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints

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