Aggregate-strength interaction test suite prioritization

Huang, Rubing; Chen, Jinfu; Towey, Dave; Chan, Alvin T. S.; Lu, Yilong

doi:10.1016/j.jss.2014.09.002

Cited by 18 publications

(14 citation statements)

References 30 publications

(28 reference statements)

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“…A "SuiteBuilder" has been developed for collecting raw test suites and assign the priority for effective selection of features in the process of test case selection [24]. An aggregate-strength prioritization technique based on uncover test cases for higher interaction with various parameters of the module has been proposed for finding the dissimilarity of combinatorial test cases and heuristic algorithms are applied to prioritized the test cases [25].…”

Section: Related Workmentioning

confidence: 99%

Selection of Test Case Features Using Fuzzy Entropy Measure and Random Forest

Murugan¹,

Kulanthaivel²,

Ulagamuthalvi³

2019

ISI

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Faults are extracted from the source code, from the pre-processed metrics and the related fault data. The generated faults are represented as a data set and are categorised into code, design and other features. Selection of features and identifying the importance of the attributes from the given set of test cases is one of the important tasks in the software testing phase. A Fuzzy based evaluation of features using the Entropy measure and Hurwicz criterion has been carried on the code and design metrics for different test cases. The datasets have been further analysed using the Random Forest Approach for identifying the feature that has the higher priority. The results obtained using the Fuzzy Entropy measure and the Random Forest approach exhibits a similarity of 95 % in identifying the feature importance. The results show that the feature NUM_OPERANDS is having the highest impact from the given list of features by applying Fuzzy Entropy measure and the model built using Random Forest approach.

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Section: Related Workmentioning

confidence: 99%

Selection of Test Case Features Using Fuzzy Entropy Measure and Random Forest

Murugan¹,

Kulanthaivel²,

Ulagamuthalvi³

2019

ISI

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“…In general, similarity-based TCP is mainly divided into two types, which are distribution based and ART (Adaptive Random Test) inspired. TCP for the first case is derived from clustered test cases through dissimilarities between test cases [28,29]. ART is used as an improvised methodology of random testing, and in almost all cases, it outperforms all the cases expect when the size of code is less [30].…”

Section: Related Studies Of Test Case Prioritization (Tcp)mentioning

confidence: 99%

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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“…Huang et al did the same work [12]. The technique based on adaptive random strategy [10] and the technique for aggregate-strength [11] were proposed in recent years.…”

Section: Related Workmentioning

confidence: 99%

Combinatorial Test Case Prioritization based on Incremental Combinatorial Coverage Strength

Wang¹

2018

IJPE

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A combinatorial test case prioritization technique based on incremental combinatorial coverage strength is proposed in this paper. Such an efficient technique prioritizes test cases in an existing high-strength combinatorial test suite, to form an ordered test case sequence. The output prioritized combinatorial test suite could cover combinations of parametric values, where their strengths are mixed from 2 or more, quicker than non-prioritized combinatorial test suite. Theoretical analysis shows that the proposed technique consumes less cost than existing combinatorial test case prioritization technique for the same type of scenarios. Experimental results also show that compared to initial combinatorial test suites without prioritization, prioritized combinatorial test suites yield a higher speed of covering combinations of parametric values. Compared to combinatorial test suites generated by an existing incremental adaptive combinatorial testing strategy, prioritized combinatorial test suites 1) require less test cases to achieve final combinatorial coverage, and 2) yield a higher speed of covering high-strength combinations of parametric values.

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Aggregate-strength interaction test suite prioritization

Cited by 18 publications

References 30 publications

Selection of Test Case Features Using Fuzzy Entropy Measure and Random Forest

Selection of Test Case Features Using Fuzzy Entropy Measure and Random Forest

Novel Fuzzy Clustering Methods for Test Case Prioritization in Software Projects

Combinatorial Test Case Prioritization based on Incremental Combinatorial Coverage Strength

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