Automated Software Engineering: A Deep Learning-Based Approach - Jagannath Singh,Suresh Chandra Satapathy,Saurabh Bilgaiyan,Ajay Kumar Jena
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This book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, 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 But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software¿s complexity ... Pilns apraksts
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Aprašymas
This book discusses various open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, 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 But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software¿s complexity.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 are proving insufficient to solve the problems now arising in software development. The book highlights a number of unique problems and effective solutions that reflect the state-of-the-art in software engineering. Deep learning is the latest computing technique, and is now gaining popularity in various fields of software engineering. This book explores new trends and experiments that have yielded promising solutions to current challenges in software 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.
Vairāk informācijas
| Autors | Jagannath Singh, Suresh Chandra Satapathy, Saurabh Bilgaiyan, Ajay Kumar Jena |
|---|---|
| Izdevējs | Springer Nature Switzerland |
| Series | Learning and Analytics in Intelligent Systems |
| Izlaides gads | 2021 |
| Vāka tips | Mīkstais vāks |
| EAN | 9783030380083 |