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Object-Oriented Test Case Generation Using Teaching Learning-Based Optimization (TLBO) Algorithm
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DOI：10.1109/ACCESS.2022.3214841.png)
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Researchers are currently seeking effective methods for automated software testing to reduce time, avoid test case redundancy, and create comprehensive test cases to cover (paths, benches, conditions, and statements). Generating a minimum number of test cases and covering all code paths is challenging in automated test case generation. Therefore, the use of optimization algorithms has become a popular trend for generating test cases to achieve many goals. In this study, we used a teaching-learning-based optimization algorithm to generate the minimum number of test cases. We compared our results with those of other state-of-the-art methods based on the path coverage for ten Java programs. The motive for using this algorithm is to optimize the number of test cases that cover all code paths in the unit test. The results emphasize that the proposed algorithm generates the minimum number of test cases and covers all paths in the code at a full-coverage rate.
Keyword:
Software algorithms
Software
Genetic algorithms
Particle swarm optimization
Software testing
Codes
Object oriented modeling
Test suite generation
unit testing
object-oriented test case generation
coverage-based optimization
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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Performance analysis of six meta-heuristic algorithms over automated test suite generation for path coverage-based optimization.png)
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SOFT COMPUTINGIF2.5
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