Automating Software Testing with Multi-Layer Perceptron (MLP): Leveraging Historical Data for Efficient Test Case Generation and Execution
Keywords:
Software testing, automation, efficient testing, test case generation, ML in software testingAbstract
Software testing is an essential step in the software development process. Defects in software are mostly caused by newer technology, a lack of version control, and the complexity of systems. Because of these issues, the cost of software maintenance rises, as do its consequences. Manual testing necessitates the use of human labour to seek for and analyse data. As software systems get more complicated, automated software testing approaches are becoming increasingly important. Machine Learning approaches have proven extremely beneficial in automating this procedure. Machine learning is also utilised to find essential software testing variables that aid in forecasting software testing cost and time. Predicting testing effort, tracking process expenses, and measuring results all contribute to improve software testing efficiency. Previously, classification trees were used to identify key properties of software testing, and regression approaches were employed to categorise defective data sets. Our framework is useful for automating the software testing process.
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