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Intelligent software defect prediction

Record Type:	Electronic resources : Monograph/item
Title/Author:	Intelligent software defect predictionby Xiao-Yuan Jing, Haowen Chen, Baowen Xu.
Author:	Jing, Xiao-Yuan.
other author:	Chen, Haowen.
Published:	Singapore :Springer Nature Singapore :2023.
Description:	xi, 205 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Software failures.
Online resource:	https://doi.org/10.1007/978-981-99-2842-2
ISBN:	9789819928422$q(electronic bk.)

Intelligent software defect prediction
Jing, Xiao-Yuan.

Intelligent software defect prediction[electronic resource] /by Xiao-Yuan Jing, Haowen Chen, Baowen Xu. - Singapore :Springer Nature Singapore :2023. - xi, 205 p. :ill., digital ;24 cm.

Chapter 1 Introduction -- Chapter 2 Application of Machine Learning Techniques in Intelligent SDP -- Chapter 3 Within-Project Defect Prediction -- Chapter 4 Cross-Project Defect Prediction -- Chapter 5 Heterogeneous Defect Prediction -- Chapter 6 Empirical Findings on HDP Approaches -- Chapter 7 Other Research Questions of SDP -- Chapter 8 Conclusions.

With the increasing complexity of and dependency on software, software products may suffer from low quality, high prices, be hard to maintain, etc. Software defects usually produce incorrect or unexpected results and behaviors. Accordingly, software defect prediction (SDP) is one of the most active research fields in software engineering and plays an important role in software quality assurance. Based on the results of SDP analyses, developers can subsequently conduct defect localization and repair on the basis of reasonable resource allocation, which helps to reduce their maintenance costs. This book offers a comprehensive picture of the current state of SDP research. More specifically, it introduces a range of machine-learning-based SDP approaches proposed for different scenarios (i.e., WPDP, CPDP, and HDP) In addition, the book shares in-depth insights into current SDP approaches' performance and lessons learned for future SDP research efforts. We believe these theoretical analyses and emerging challenges will be of considerable interest to all researchers, graduate students, and practitioners who want to gain deeper insights into and/or find new research directions in SDP. It offers a comprehensive introduction to the current state of SDP and detailed descriptions of representative SDP approaches.

ISBN: 9789819928422$q(electronic bk.)

Standard No.: 10.1007/978-981-99-2842-2doiSubjects--Topical Terms:

781787
Software failures.

LC Class. No.: QA76.76.F34

Dewey Class. No.: 005.14

Intelligent software defect prediction
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505 0 $a Chapter 1 Introduction -- Chapter 2 Application of Machine Learning Techniques in Intelligent SDP -- Chapter 3 Within-Project Defect Prediction -- Chapter 4 Cross-Project Defect Prediction -- Chapter 5 Heterogeneous Defect Prediction -- Chapter 6 Empirical Findings on HDP Approaches -- Chapter 7 Other Research Questions of SDP -- Chapter 8 Conclusions.
520 $a With the increasing complexity of and dependency on software, software products may suffer from low quality, high prices, be hard to maintain, etc. Software defects usually produce incorrect or unexpected results and behaviors. Accordingly, software defect prediction (SDP) is one of the most active research fields in software engineering and plays an important role in software quality assurance. Based on the results of SDP analyses, developers can subsequently conduct defect localization and repair on the basis of reasonable resource allocation, which helps to reduce their maintenance costs. This book offers a comprehensive picture of the current state of SDP research. More specifically, it introduces a range of machine-learning-based SDP approaches proposed for different scenarios (i.e., WPDP, CPDP, and HDP) In addition, the book shares in-depth insights into current SDP approaches' performance and lessons learned for future SDP research efforts. We believe these theoretical analyses and emerging challenges will be of considerable interest to all researchers, graduate students, and practitioners who want to gain deeper insights into and/or find new research directions in SDP. It offers a comprehensive introduction to the current state of SDP and detailed descriptions of representative SDP approaches.
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