Component-Based Quality Prediction via Component Reliability Using Optimal Fuzzy Classifier and Evolutionary Algorithm

Sheoran, Kavita; Sangwan, Om Prakash

doi:10.1007/978-981-10-8848-3_43

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Moreover, other techniques include NSGA-II [37], Bacteriological Algorithm [38], Bee Colony Optimization, and Hill Climbing [34]. Search-based techniques are increasingly adopted to solve such optimization [39][40][41]. There are many search-based techniques, that are successfully applied in the area of TCS&P. However, there are newer and more efficient search-based approaches available in the literature which is easier to use and give efficient results on other optimization problems.…”

Section: Multi-objective Test Case Selection and Prioritization (Tcsa...mentioning

confidence: 99%

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Singhal¹,

Jatana²,

Subahi³

et al. 2023

Computers, Materials &Amp; Continua

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Software needs modifications and requires revisions regularly. Owing to these revisions, retesting software becomes essential to ensure that the enhancements made, have not affected its bug-free functioning. The time and cost incurred in this process, need to be reduced by the method of test case selection and prioritization. It is observed that many nature-inspired techniques are applied in this area. African Buffalo Optimization is one such approach, applied to regression test selection and prioritization. In this paper, the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection and prioritization. The proposed algorithm converges in polynomial time (O(n 2 )). In this paper, the empirical evaluation of applying African Buffalo Optimization for test case prioritization is done on sample data set with multiple iterations. An astounding 62.5% drop in size and a 48.57% drop in the runtime of the original test suite were recorded. The obtained results are compared with Ant Colony Optimization. The comparative analysis indicates that African Buffalo Optimization and Ant Colony Optimization exhibit similar fault detection capabilities (80%), and a reduction in the overall execution time and size of the resultant test suite. The results and analysis, hence, advocate and encourages the use of African Buffalo Optimization in the area of test case selection and prioritization.

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Section: Multi-objective Test Case Selection and Prioritization (Tcsa...mentioning

confidence: 99%

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Singhal¹,

Jatana²,

Subahi³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

Component-Based Quality Prediction via Component Reliability Using Optimal Fuzzy Classifier and Evolutionary Algorithm

Cited by 1 publication

References 6 publications

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

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