Meena Sharma, Babita Pathik*
Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1125-1140, 2022, DOI:10.32604/iasc.2022.022335
- 17 November 2021
Abstract Test case generation and optimization is the foremost requirement of software evolution and test automation. In this paper, a bio-inspired Crow Search Algorithm (CSA) is suggested with an improved objective function to fulfill this requirement. CSA is a nature-inspired optimization method. The improved objective function combines branch distance and predicate distance to cover the critical path on the control flow graph. CSA is a search-based technique that uses heuristic information for automation testing, and CSA optimizers minimize test cases generated by satisfying the objective function. This paper focuses on generating test cases for all paths,… More >
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