Titlebook: Automated Software Engineering: A Deep Learning-Based Approach; Suresh Chandra Satapathy,Ajay Kumar Jena,Saurabh B Book 2020 The Editor(s)

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Book 2020tware engineering. As such, it offers a valuable reference guide for a broad audience including systems analysts, software engineers, researchers, graduate students and professors engaged in teaching software engineering. .

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Automated Software Engineering: A Deep Learning-Based Approach

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Book 2020ion, data processing, and automatic code generation. Many traditional techniques are available for addressing these problems. But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity. Hence, many previously used methods a

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Selection of Significant Metrics for Improving the Performance of Change-Proneness Modules,ediction causes programming analyzers to streamline and focus their testing resources on the modules which have a higher likelihood of modification. Accurate estimation of characteristics such as effort, quality, and risk which are the major concerns for change proneness, is of significant worry in

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Effort Estimation of Web Based Applications Using ERD, Use Case Point Method and Machine Learning,studies and researches that accurate effort estimation rate increases the success rate for developing the application. There are quite a few methodologies for estimating the effort the web-based application. Earlier the effort estimation process did not include the back-end part or the database part

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