Building a Tool for Optimal Test Cases Selection using Artificial Intelligence Techniques
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Abstract
Software testing is an important process for detecting errors in programs and reducing the risks of their use. With the rapid expansion of the software industry and the heavy dependence on increasingly popular and frequently used programs, there is a necessary need to use software testing techniques that are efficient, scalable, applicable, and effective in detecting errors. In this research, a tool was built that selects the optimal test cases using artificial intelligence techniques. The crow search algorithm was used to select test cases, and after modifications and improvements were made to the algorithm, the improved crow search algorithm was proposed, which generates and selects test cases that achieve the basic paths of the program, depending on the hybridization between the criterion of close to boundary value and branch coverage in calculating the fitness function, and relying on the crow's awareness probability value. In addition, the genetic algorithm was used for test case prioritization.
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