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Automated software engineering = a d...

Satapathy, Suresh Chandra.

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Automated software engineering = a deep learning-based approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Automated software engineering/ by Suresh Chandra Satapathy ... [et al.].
其他題名:	a deep learning-based approach /
其他作者:	Satapathy, Suresh Chandra.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	xi, 118 p. :ill., digital ;24 cm.
內容註:	Chapter 1: Selection of Significant Metrics for Improving the Performance of Change-Proneness Modules -- Chapter 2: Effort Estimation of Web based Applications using ERD, use Case Point Method and Machine Learning -- Chapter 3: Usage of Machine Learning in Software Testing -- Chapter 4: Test Scenarios Generation using Combined Object-Oriented Models -- Chapter 5: A Novel Approach of Software Fault Prediction using Deep Learning Technique -- Chapter 6: Feature-Based Semi-Supervised Learning to Detect Malware from Android.
Contained By:	Springer eBooks
標題:	Software engineering. -
電子資源:	https://doi.org/10.1007/978-3-030-38006-9
ISBN:	9783030380069

Automated software engineering = a deep learning-based approach /
Automated software engineeringa deep learning-based approach /[electronic resource] :by Suresh Chandra Satapathy ... [et al.]. - Cham :Springer International Publishing :2020. - xi, 118 p. :ill., digital ;24 cm. - Learning and analytics in intelligent systems,v.82662-3447 ;. - Learning and analytics in intelligent systems ;v.8..

Chapter 1: Selection of Significant Metrics for Improving the Performance of Change-Proneness Modules -- Chapter 2: Effort Estimation of Web based Applications using ERD, use Case Point Method and Machine Learning -- Chapter 3: Usage of Machine Learning in Software Testing -- Chapter 4: Test Scenarios Generation using Combined Object-Oriented Models -- Chapter 5: A Novel Approach of Software Fault Prediction using Deep Learning Technique -- Chapter 6: Feature-Based Semi-Supervised Learning to Detect Malware from Android.

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 But, with the rapid changes in software development, they often prove to be outdated or incapable of handling the software's complexity.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

ISBN: 9783030380069

Standard No.: 10.1007/978-3-030-38006-9doiSubjects--Topical Terms:

559826
Software engineering.

LC Class. No.: QA76.758

Dewey Class. No.: 005.1

Automated software engineering = a deep learning-based approach /
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245 0 0 $a Automated software engineering $h [electronic resource] : $b a deep learning-based approach / $c by Suresh Chandra Satapathy ... [et al.].
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505 0 $a Chapter 1: Selection of Significant Metrics for Improving the Performance of Change-Proneness Modules -- Chapter 2: Effort Estimation of Web based Applications using ERD, use Case Point Method and Machine Learning -- Chapter 3: Usage of Machine Learning in Software Testing -- Chapter 4: Test Scenarios Generation using Combined Object-Oriented Models -- Chapter 5: A Novel Approach of Software Fault Prediction using Deep Learning Technique -- Chapter 6: Feature-Based Semi-Supervised Learning to Detect Malware from Android.
520 $a 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.
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