A Test Suite Reduction Method Based on Novel Quantum Ant Colony Algorithm

Regression testing is an important activity to ensure the quality of software. In order to improve the efficiency of regression testing, in this paper, the author proposes to reorder test suite based on ant colony algorithm in regression testing, and compare the result with other common sort results. Through experiment, it shows that the method can get the optimal sequence of test cases under the time limit and it has been proven a superior method in both effectiveness and efficiency for test cases prioritization.

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Ant colony algorithm to generate t-way test suite with constraints

Journal of Physics Conference Series ◽

10.1088/1742-6596/1529/4/042103 ◽

2020 ◽

Vol 1529 ◽

pp. 042103

Author(s):

Nuraminah Ramli ◽

Rozmie Razif Othman ◽

Zahereel Ishwar Abdul Khalib ◽

Mohd Zamri Zahir Ahmad ◽

S S M Fauzi

Keyword(s):

Ant Colony Algorithm ◽

Ant Colony ◽

Test Suite

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Test case set reduction method based on gre and ant colony algorithm

2021 IEEE International Conference on Consumer Electronics and Computer Engineering (ICCECE) ◽

10.1109/iccece51280.2021.9342065 ◽

2021 ◽

Author(s):

Nanxin Gan ◽

Hongman Wang ◽

Yanyang Zhao ◽

Fangchun Yang

Keyword(s):

Ant Colony Algorithm ◽

Reduction Method ◽

Ant Colony ◽

Test Case

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Optimum Test Suite using Fault-Type Coverage Based Ant Colony Optimization Algorithm

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2022010106 ◽

2022 ◽

Vol 13 (1) ◽

pp. 0-0

Keyword(s):

Ant Colony Optimization ◽

Ant Colony ◽

Test Suite ◽

Computational Time ◽

Test Case ◽

Test Cases ◽

Product Lines ◽

Fault Type ◽

Testing Cost ◽

Test Suite Reduction

Software Product Lines(SPLs) covers a mixture of features for testing Software Application Program(SPA). Testing cost reduction is a major metric of software testing. In combinatorial testing(CT), maximization of fault type coverage and test suite reduction plays a key role to reduce the testing cost of SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome for test suite optimization problem due to mutation operation and required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm is offered for test suite reduction in CT. FTCBACO algorithm starts with test cases in test suite and assign separate ant to each test case. Ants elect best test cases by updating of pheromone trails and selection of higher probability trails. Best test case path of ant with least time are taken as optimal solution for performing CT. Hence, FTCBACO Technique enriches reduction rate of test suite and minimizes computational time of reducing test cases efficiently for CT.

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