Crow Search Algorithm with Improved Objective Function for Test Case Generation and Optimization

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, including critical paths. The control flow graph covers the information flow among all the classes, functions, and conditional statements and provides test paths. The number of test cases examined through graph path coverage analysis. The minimum number of test paths is counted through complexity metrics using the cyclomatic complexity of the constructed graph. The proposed method is evaluated as mathematical optimization functions to validate their effectiveness in locating optimal solutions. The python codes are considered for evaluation and revealed that our approach is time-efficient and outperforms various optimization algorithms. The proposed approach achieved 100% path coverage, and the algorithm executes and gives optimum results in approximately 0.2745 seconds.