Automated Software Engineering: A Deep Learning-Based Approach (Hardcover, 1st ed. 2020)

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 But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software's complexity.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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Learning and Analytics in Intelligent Systems, 8
Release date:	2020
First published:	2020
Authors:	Suresh Chandra Satapathy • Ajay Kumar Jena • Jagannath Singh • Saurabh Bilgaiyan
Dimensions:	235 x 155mm (L x W)
Format:	Hardcover
Pages:	118
Edition:	1st ed. 2020
ISBN-13:	978-3-03-038005-2
Categories:	Books > Computing & IT > Computer programming > Software engineering Books > Computing & IT > Applications of computing > Databases > General Books > Computing & IT > Applications of computing > Artificial intelligence > General
LSN:	3-03-038005-X
Barcode:	9783030380052

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