Authors:
- Offers potential deep learning concepts for handling open issues in software engineering, such as the efficiency of automated testing techniques, predictions for cost estimation, data processing, and automatic code generation
- Presents a deep learning based approach to Automated Software Engineering
- Provides new ideas in the field of software engineering
Part of the book series: Learning and Analytics in Intelligent Systems (LAIS, volume 8)
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Table of contents (6 chapters)
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Front Matter
About this book
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 But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity.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.
Authors and Affiliations
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School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, India
Suresh Chandra Satapathy, Ajay Kumar Jena, Jagannath Singh, Saurabh Bilgaiyan
Bibliographic Information
Book Title: Automated Software Engineering: A Deep Learning-Based Approach
Authors: Suresh Chandra Satapathy, Ajay Kumar Jena, Jagannath Singh, Saurabh Bilgaiyan
Series Title: Learning and Analytics in Intelligent Systems
DOI: https://doi.org/10.1007/978-3-030-38006-9
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-38005-2Published: 08 January 2020
Softcover ISBN: 978-3-030-38008-3Published: 08 January 2021
eBook ISBN: 978-3-030-38006-9Published: 07 January 2020
Series ISSN: 2662-3447
Series E-ISSN: 2662-3455
Edition Number: 1
Number of Pages: XI, 118
Topics: Computational Intelligence, Data Engineering, Software Engineering