Test Case Minimization Applying Firefly Algorithm
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TL;DR: Findings from this study shows that this test case minimization method performed on UML statechart has successful covered more types of test coverage and is capable of minimizing the number of test cases.
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Abstract: The objective of this study is to propose a test case minimization method performed on UML statechart to produce test cases that are optimal while considering higher coverage criteria. Current test case generation techniques consume a large amount of time and cost with less testing coverage, while generating large number or test cases, what results in difficulties in detecting the faults and errors in the tested systems. Many approaches on test case minimization use genetic algorithms, model checking, or graph search algorithms to reduce the number of generated test cases, also the using of UML diagrams to test the system requirements and design before implementing it in the coding phase. However, these studies lack concentration in achieving higher coverage criteria and minimization in the generated test cases in the same time. The proposed test case minimization method has the following steps: provide weight to the paths, calculate path coverage for each path, transform an immediate graph into an adjacency matrix, which later is used to apply firefly algorithm and generate optimal test cases. A review on of the previous similar research in this domain has been presented and analysed to identify the issues and gaps in this domain. The steps use to perform the test case minimization have been presented together with some example and formula used. Findings from this study shows that this test case minimization has successful covered more types of test coverage which are all state, all transition, all transition pairs and all-one-loop paths. At the same time, it is capable of minimizing the number of test cases.
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Citations
Test case prioritization techniques in software regression testing: An overview
TL;DR: A detailed survey of the TCP methodologies is provided, which includes 2010 to the most recent studies and definitely improves the quality of software and brings the best product within a limited time and cost.
Improved novel bat algorithm for test case prioritization and minimization
TL;DR: An enhanced discrete novel bat algorithm for the test case prioritization and a fix-up mechanism for the discrete combinatorial problem, which conducts the perturbation in the population using the asexual reproduction algorithm are proposed.
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MC/DC guided Test Sequence Prioritization using Firefly Algorithm
Swadhin Kumar Barisal,Arpita Dutta,Sangharatna Godboley,Sangharatna Godboley,Bibhudatta Sahoo,Durga Prasad Mohapatra +5 more
TL;DR: This work uses Firefly algorithm with an improvised objective function to generate optimal test paths guided by “Modified Condition/Decision Coverage” (MC/DC) criteria in the form of a guided matrix.
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Test suite minimization using particle swarm optimization
TL;DR: In this paper , the authors proposed a novel techniques for test suite minimization using nature inspired metaheuristic particle swarm optimization algorithm for removing the redundant test cases from the suite and compared their technique with four benchmark reduction techniques G WSC, G, HGS and GRE based on the size of reduced set and execution cost.
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