A Tuned Version of Ant Colony Optimization Algorithm (TACO) for Uniform Strength T-way Test Suite Generator: An Execution’s Time Comparison

Software testing is one of important phase in software development. The capabilities of t-way testing to cater bugs due to interactions while reducing the test suite size compare to exhaustive testing has been proven in past decades. However, the execution’s time of the t-way strategy also should be...

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Veröffentlicht in:Journal of physics. Conference series Jg. 1962; H. 1; S. 12037 - 12045
Hauptverfasser: Ahmad, Mohd Zamri Zahir, Othman, Rozmie Razif, Ali, Mohd Shaiful Aziz Rashid, Ramli, Nuraminah, Nasrudin, Mohd Wafi, Halim, Ahmad Ashraf Abdul
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.07.2021
Schlagworte:
Algorithms
Ant Colony Optimization
Combinatorial testing
Fuzzy logic
Heuristic methods
Optimization algorithms
Software development
Software testing
T-way testing
ISSN:1742-6588, 1742-6596
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Abstract Software testing is one of important phase in software development. The capabilities of t-way testing to cater bugs due to interactions while reducing the test suite size compare to exhaustive testing has been proven in past decades. However, the execution’s time of the t-way strategy also should be given attention as it could increase the productivity of the testing phase. Thus, this paper proposed a tune version of ant colony optimization algorithm (TACO). TACO is metaheuristic strategy where it adopts ant colony optimization in generating test suites. As further improvement, TACO also integrated with fuzzy logic to dynamically select amount of ant in the algorithm. TACO able to supports uniform strength t-way testing. Experiment result shows that TACO produce a remarkable result of test suite size and execution’s time compared to other strategy for uniform strength t-way testing.
AbstractList Software testing is one of important phase in software development. The capabilities of t-way testing to cater bugs due to interactions while reducing the test suite size compare to exhaustive testing has been proven in past decades. However, the execution’s time of the t-way strategy also should be given attention as it could increase the productivity of the testing phase. Thus, this paper proposed a tune version of ant colony optimization algorithm (TACO). TACO is metaheuristic strategy where it adopts ant colony optimization in generating test suites. As further improvement, TACO also integrated with fuzzy logic to dynamically select amount of ant in the algorithm. TACO able to supports uniform strength t-way testing. Experiment result shows that TACO produce a remarkable result of test suite size and execution’s time compared to other strategy for uniform strength t-way testing.
Author Ahmad, Mohd Zamri Zahir
Ramli, Nuraminah
Ali, Mohd Shaiful Aziz Rashid
Nasrudin, Mohd Wafi
Halim, Ahmad Ashraf Abdul
Othman, Rozmie Razif
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  surname: Halim
  fullname: Halim, Ahmad Ashraf Abdul
  organization: Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis , Perlis, Malaysia
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Cites_doi 10.1088/1757-899X/767/1/012017
10.1145/1883612.1883618
10.1016/j.asoc.2016.03.021
10.1109/ICSTW.2013.40
10.3844/jcssp.2011.1375.1385
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SubjectTerms Algorithms
Ant Colony Optimization
Combinatorial testing
Fuzzy logic
Heuristic methods
Optimization algorithms
Software development
Software testing
T-way testing
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Title A Tuned Version of Ant Colony Optimization Algorithm (TACO) for Uniform Strength T-way Test Suite Generator: An Execution’s Time Comparison
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