Automated Software Engineering: A Deep Learning-Based Approach

Suresh Chandra Satapathy (u. a.)

ISBN 10: 303038005X / ISBN 13: 9783030380052

Published by Springer International Publishing, 2020

About this Item

Automated Software Engineering: A Deep Learning-Based Approach | Suresh Chandra Satapathy (u. a.) | Buch | xi | Englisch | 2020 | Springer International Publishing | EAN 9783030380052 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.

Seller Inventory # 117824397

Bibliographic Details

• Title: Automated Software Engineering: A Deep Learning-Based Approach
• Publisher: Springer International Publishing
• Publication Date: 2020
• Language: English
• ISBN 10: 303038005X
• ISBN 13: 9783030380052
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 13D millimeters 241 millimeters width by 160 millimeters height by 13 millimeters depth
• Weight: 371 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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 But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity.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