Abstract
In the progression of computer-based structures and products, software has turned out to be a vital element. Software testing is an utmost effort of an intense segment in the software development life cycle. In the same manner, everyone like to minimalize the struggle and distinguish the most of the number of errors. Automatic test case production helps to minimize the price and time effort. Worldwide a single or most significant method of automatic test case production is a crucial issue in software testing and a warm problem in the study of software testing. The paper proposed a genetic algorithm (GA) to improve the test case and the submission of the artificial intelligence (AI) approaches that is applied in software testing for automatic software testing. The data of test cases are produced randomly by put on the conditional coverage on the source code and creating the control flow graph (CFG) of the source code and then applying GA test cases that are automatically generated. GA makes the test cases optimized and outperforms which is produced by a type of testing named random testing (RT). It is an effective technique for enhancing test cases by applying GA and conditional coverage as well that implemented in MATLAB. Automated test case production improves the software testing approaches and advances the quality of software. Like this automated test, case production decreases the complete cost of software development for software-based systems.
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Srivastava, J., Malik, A., Bhushan, B., Parihar, V., Nair, S. (2023). Genetic Algorithm: An Approach for Software Testing Based on a Given Source Code. In: Sharma, D.K., Peng, SL., Sharma, R., Jeon, G. (eds) Micro-Electronics and Telecommunication Engineering . Lecture Notes in Networks and Systems, vol 617. Springer, Singapore. https://doi.org/10.1007/978-981-19-9512-5_49
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DOI: https://doi.org/10.1007/978-981-19-9512-5_49
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