Name: Automated Software Engineering: A Deep Learning-Based Approach (Learning and Analytics in Intelligent Systems) (en Inglés)
Price: 3014.49 MXN
Availability: InStock
Author: Suresh Chandra Satapathy; Ajay Kumar Jena; Jagannath Singh; Saurabh Bilgaiyan
ISBN: 9783030380052

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portada Automated Software Engineering: A Deep Learning-Based Approach (Learning and Analytics in Intelligent Systems) (en Inglés)

Formato

Libro Físico

Editorial

Springer

Autor

Suresh Chandra Satapathy; Ajay Kumar Jena; Jagannath Singh; Saurabh Bilgaiyan

Año

2020

Idioma

Inglés

N° páginas

118

Encuadernación

Tapa Dura

ISBN13

9783030380052

N° edición

Categorías

Ciencia y análisis de datos
Análisis y captura de datos

Automated Software Engineering: A Deep Learning-Based Approach (Learning and Analytics in Intelligent Systems) (en Inglés)

Suresh Chandra Satapathy; Ajay Kumar Jena; Jagannath Singh; Saurabh Bilgaiyan (Autor) · Springer · Tapa Dura

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Reseña del libro "Automated Software Engineering: A Deep Learning-Based Approach (Learning and Analytics in Intelligent Systems) (en Inglés)"

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 But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software’s complexity.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