Coverage-Based, Prioritized Testing Using Neural Network Clustering

Gökçe, Nida; Eminov, Mübariz; Belli, Fevzi

doi:10.1007/11902140_110

Cited by 11 publications

(15 citation statements)

References 8 publications

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“…18. Priority values assigned to the CESs generated from the INC3 module are also given in Table A.19 in the Appendix.…”

Section: Experimental Results and The Discussionmentioning

confidence: 99%

“…One of the benefits of the proposed approach is that it can be deployed in any stage of software development to reflect the preferences of the tester wherever he/she needs to select a subset among all possible/available test cases in favor of the total testing cost. Although the authors' previous works [13,[18][19][20][21][22][23] also defined similar attributes for events and assigned values to them in order to determine the degree of preference by means of cluster analysis [24], the approach presented in this article is different in that it classifies the events using two techniques adapted from soft computing, namely neural networks and fuzzy logic [22,23]. Details and the theoretical basis of the aforementioned clustering-based prioritization approaches are given in the authors' previous works [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Gökçe¹,

Belli²,

Eminli³

et al. 2015

Turk J Elec Eng & Comp Sci

Self Cite

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Abstract:Model-based testing is related to the particular relevant features of the software under test (SUT) and its environment. Real-life systems often require a large number of tests, which cannot exhaustively be run due to time and cost constraints. Thus, it is necessary to prioritize the test cases in accordance with their importance as the tester perceives it, usually given by several attributes of relevant events entailed. Based on event-oriented graph models, this paper proposes an approach to ranking test cases in accordance with their preference degrees. For forming preference groups, events are clustered using an unsupervised neural network and fuzzy c-means clustering algorithm. The suggested approach is model-based, so it does not necessitate the availability of the source code of the SUT. It differs from existing approaches also in that it needs no prior information about the tests carried out before. Thus, it can be used to reflect the tester's preferences not only for regression testing as is common in the literature but also for ranking test cases in any stage of software development. For the purpose of experimental evaluation, we compare the suggested prioritization approach with six well-known prioritization methods.

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“…18. Priority values assigned to the CESs generated from the INC3 module are also given in Table A.19 in the Appendix.…”

Section: Experimental Results and The Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Gökçe¹,

Belli²,

Eminli³

et al. 2015

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…The principle underlying the RSC is to first employ the SC algorithm to generate the test set satisfying the Prime Path Coverage (denoted as P ). Then, from P , the test cases that cover the maximal number of priority edges that must be present in the test cases are utilized to build test set T 18 if e / ∈ any t ∈ T then 19 T is invalid 20 end 21 end incrementally.…”

Section: ) Rsc Algorithmmentioning

confidence: 99%

Employment of multiple algorithms for optimal path-based test selection strategy

Bureš

Ahmed

2019

Information and Software Technology

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Executing various sequences of system functions in a system under test represents one of the primary techniques in software testing. The natural way to create effective, consistent and efficient test sequences is to model the system under test and employ an algorithm to generate the tests that satisfy a defined test coverage criterion. Several criteria of test set optimality can be defined. In addition, to optimize the test set from an economic viewpoint, the priorities of the various parts of the system model under test must be defined. Using this prioritization, the test cases exercise the high priority parts of the system under test more intensely than those with low priority. Evidence from the literature and our observations confirm that finding a universal algorithm that produces an optimal test set for all test coverage and test set optimality criteria is a challenging task. Moreover, for different individual problem instances, different algorithms provide optimal results. In this paper, we present a path-based strategy to perform optimal test selection. The strategy first employs a set of current algorithms to generate test sets; then, it assesses the optimality of each test set by the selected criteria, and finally, chooses the optimal test set. The experimental results confirm the validity and usefulness of this strategy. For individual instances of 50 system under test models, different algorithms provided optimal results; these results varied by the required test coverage level, the size of the priority parts of the model, and the selected test set optimality criteria.

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“…Regarding the work by Dwarakanath and Jankiti [15], they focus on prime-path coverage, which is out of the scope of the paper. The approach by Gke et al [17] (also presented in Belli et al [18]) is aimed at minimization of the test set for complete coverage, which is also out of the scope of the paper. Moreover, prioritization by individual parts of the SUT as discussed in this paper is not the subject of the study.…”

Section: Experimental Evaluationmentioning

confidence: 99%

“…In addition, alternative strategies and algorithms for prioritizing the path-based test cases have been studied and developed. For instance, approaches based on neural network clustering [17], fuzzy clustering [18] and the firefly optimization algorithm [19] have been implemented. In contrast to algorithms based on an SUT model and a set of test requirements only, in these techniques, information about the SUT internal structure is also used as an input to the prioritization.…”

Section: Related Workmentioning

confidence: 99%

Prioritized Process Test: An Alternative to Current Process Testing Strategies

Bureš

Ahmed

Zamli

2019

Int. J. Soft. Eng. Knowl. Eng.

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Testing processes and workflows in information and Internet of Things systems is a major part of the typical software testing effort. Consistent and efficient path-based test cases are desired to support these tests. Because certain parts of software system workflows have a higher business priority than others, this fact has to be involved in the generation of test cases. In this paper, we propose a Prioritized Process Test (PPT), which is a model-based test case generation algorithm that represents an alternative to currently established algorithms that use directed graphs and test requirements to model the system under test. The PPT accepts a directed multigraph as a model to express priorities, and edge weights are used instead of test requirements. To determine the testcoverage level of test cases, a test-depth-level concept is used. We compared the presented PPT with five alternatives (i.e., the Process Cycle Test, a naive reduction of test set created by the Process Cycle Test, Brute Force algorithm, Set-covering Based Solution and Matching-based Prefix Graph Solution) for edge coverage and edge-pair coverage. To assess the optimality of the path-based test cases produced by these strategies, we used fourteen metrics based on the properties of these test cases and 59 models that were created for three real-world systems. For all edge coverage, the PPT produced more optimal test cases than the alternatives in terms of the majority of the metrics. For edge-pair coverage, the PPT strategy yielded similar results to those of the alternatives. Thus, the PPT strategy is an applicable alternative, as it reflects both the required test * coverage level and the business priority in parallel.

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Coverage-Based, Prioritized Testing Using Neural Network Clustering

Cited by 11 publications

References 8 publications

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

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

Employment of multiple algorithms for optimal path-based test selection strategy

Prioritized Process Test: An Alternative to Current Process Testing Strategies

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