Application of Gradient-Based Optimizer for Development of Enhanced Fitness Function with Critical Path Weights for Generating Test Data - Vinita Tomar (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The testing phases are generally resource-intensive, which initially includes the development of test cases followed by their generation. Such phases significantly impact the entire testing process in terms of their effectiveness and efficiency. The identification of an efficient method for the further generation of test cases that could ensure the achievement of maximum path coverage with the limited available testing resources is the primary objective of this paper. To accomplish the above-mentioned task, the key component is the selection of the appropriate fitness function, which will play a vigorous role in the process of optimization. The proposed study in the paper introduces an enhanced combined fitness function that would influence the optimization of performance. The enhanced fitness function is also proposed to incorporate the weights for the critical paths, which would further allow the optimizer to prioritize the coverage of these paths while reducing the overall time essential for the generation of test cases. The criteria selected to assign weights to critical paths further collaborate with normalized branch distance (NBD) functions and approach level (AL). A gradient-based optimizer (GBO) is also employed for the generation of test cases, which is expected to result in impressive outcomes. To generate the test cases systematically, it is also combined with the refined fitness function. The experiments further reveal that the approach being proposed in this paper surpasses current state-of-the-art approaches in various aspects, such as the execution time required, the number of iterations required, and the average number of test instances generated.