Automated Test Case Generation Model from UML Diagrams based on Monotonic Genetic Algorithm

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Jyoti Gautam Tiwari, Ugrasen Suman


The procedure of developing package includes software testing as an imperative phase. The three components of the testing process are test execution, test evaluation and test case generation. The creation of test cases remains at the heart of challenging automation. It decreases the amount of mistakes and flaws while saving time and effort. A new way to automate the testing process has been developed to reduce the high tot test evaluation al of software testing and to improve the dependability of the testing procedures. In this paper, a innovative technique for creating and refining test cases using UML Activity Chart diagrams is proposed. The Genetic Algorithm's crossover method was used to create the new test sequence, and the test sequences' effectiveness was assessed by Mutation Analysis. As a result, they are unable to effectively combat multilayer perceptrons when faced with incorrect properties. Monotonic genetic algorithm is a Concept that is easy to understand and Supports multi-objective. The radial basis function (RBF) neural network algorithm currently in use has challenges counting the amount of neurons in the hidden layer and has poor weight learning ability from the hidden layer to the output layer. RBF networks have the drawback of giving respectively attribute a comparable weight since all factors are taken into account equally while calculating distance unless the attribute weight parameters are included in the entire optimization procedure.

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Jyoti Gautam Tiwari. (2024). Automated Test Case Generation Model from UML Diagrams based on Monotonic Genetic Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 12(2), 376–384. Retrieved from