Abstract
It is true that generating test cases manually can be time-consuming and may result in incomplete coverage of user requirements. Our approach of using a story board, natural language processing, and decision tree to generate test cases is a novel solution that can save time and improve the quality of software. By processing user acceptance criteria with natural language processing and using decision trees to generate test cases, our approach can help ensure that all user requirements are covered. The activity flow diagram and encoded graph that are generated can provide a visual representation of the test cases, which can make it easier to identify any gaps in coverage. We have evaluated our technique using a case study and compared it with other state-of-the-art methods. The significant reduction in overall effort proves that our technique is effective. Overall, our approach of generating test cases using a story board, natural language processing, and decision tree is a promising development that has the potential to improve the quality of software and reduce the effort required to generate test cases.
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Gupta, N., Yadav, V., Singh, M. (2023). Decision Tree Based Test Case Generation Using Story Board and Natural Language Processing. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_47
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