Abstract
Software testing has been one very time-consuming and expensive phases in the process of software development. It needs plenty of effort in the development of tools for the process to reduce both cost and time in the development of software. The test cases were the input parameters along with expected results and conditions of execution that are used for testing. The test case selection (TCS) is approaches that aim at the selection of subsets for the test cases in a particular domain based on the criterion of interest. The primary aim is the elimination of unwanted and redundant test data aside from maximizing fault detection. Optimization techniques are applied for TCS to efficient testing. A local method of search is very popular among algorithms for the performance of optimization which is known as the gradient descent. This makes use of structural information from the nonlinear model. Simulated annealing (SA), on the other hand, is one which is global heuristic that minimizes the cost function. This work has proposed a high level of gradient descent and simulated annealing for the selection of software test case. Experiments showed that the modified SA has a lower number of print tokens by 3.82% for the 10,000 cost, by about 2.5% for the 30,000 cost, by about 1.17% for the 50,000 cost and finally by about 1.14% for the 70,000 cost.
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Nithya, T.M., Chitra, S. Soft computing-based semi-automated test case selection using gradient-based techniques. Soft Comput 24, 12981–12987 (2020). https://doi.org/10.1007/s00500-020-04719-9
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DOI: https://doi.org/10.1007/s00500-020-04719-9