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Test case optimization using grey wolf algorithm
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NETL
NSTL
Springer Nature
Testing is an important part of any software development process. Increased effectiveness of software testing and reduction in cost can be achieved by reordering called Test Case Optimization (TCO). This paper proposes a new algorithm for multi-objective test case optimization (TCP) using the Grey Wolf Optimization (GWO) algorithm and current approach is based on optimizing maximum fault coverage with minimum runtime of test cases. The GWO is an optimization method inspired by nature, based on the mechanism of how grey wolf hunts. To achieve the research purpose, a comprehensive literature review of the GWO algorithm, software testing, and related optimization techniques was conducted. Based on that findings, this paper represents a new algorithm, named GWOJTCO, which combines GWO with the traveling salesman problem concept to optimize test cases in software regression testing. The proposed algorithm has been evaluated and analyzed over seventeen open-source problems along with one benchmark flex dataset. The experimentation is aimed at reduction in size and time of the resultant test suite. Also, 72% average percentage correctness have been achieved. Further, It was found that the average % fault detection of each program was nearly 85% or more going upto 100% fault coverage. Henceforth, GWO_TCO reduces the number of test cases required for software testing while maintaining a high level of early fault detection in a relatively short time, making it practical for real-world software testing scenarios. The studies' findings demonstrated that, in terms of the number of defects found and the effectiveness of the testing as a whole, the proposed algorithm performed better than almost all of the conventional approaches, providing valuable insights for software developers and testers to improve their testing processes and reduce testing time and costs.
Software testingTest case optimizationMeta-heuristicsOptimizationTest case selectionTest case prioritizationRegression techniquesGWO
Srishti Kumari、Shweta Jindal、Arun Sharma
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Computer Science and Engineering - Artificial Intelligence, Indira Gandhi Delhi Technical University for Women, New Delhi 110006, Delhi, India
Information Technology, Indira Gandhi Delhi Technical University for Women, New Delhi 110006, Delhi, India
NETL
NSTL
Springer Nature
