Intelligent Software Defect Prediction (PDF)
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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.
Xiao-Yuan Jing is a Professor at the School of Computer Science, Wuhan University. Prof. Jing's research interests include software defect prediction, software effort estimation, and software engineering. His research has been published in authoritative software engineering journals and conference proceedings, such as IEEE Transactions on Software Engineering, Empirical Software Engineering, IEEE Transactions on Reliability, Information and Software Technology, Automated Software Engineering, ICSE, FSE, ASE, and ICSME. He has also pursued research on pattern recognition, machine learning, and artificial intelligence. He has published a range of studies in leading artificial intelligence journals and conference proceedings, including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Information Forensics and Security, CVPR, AAAI, and IJCAI. He has been selected as a highly cited Chineseresearcher by Elsevier.
Haowen Chen is a Ph.D. candidate at the School of Computer Science, Wuhan University. Mr. Chen is currently working toward his Ph.D. degree at the School of Computer Science, Wuhan University. His research interests include software engineering and machine learning. His research has been published in IEEE Transactions on Software Engineering, Information and Software Technology, ICSE, etc.
Baowen Xu is a Professor at the Department of Computer Science and Technology, Nanjing University. Prof. Xu has pursued research on the theory, methodology, and technology research of test-driven software defect diagnosis and analysis, since the late 1980s. He has obtained a number of internationally advanced research results, which have had a substantial impact in this research field. He has undertaken more than 30 research projects from the Ministry of Aerospace Industry, China State Shipbuilding Corporation, the National Natural Science Foundation of
- Autoren: Xiao-Yuan Jing , Haowen Chen , Baowen Xu
- 2024, 1st ed. 2023, 205 Seiten, Englisch
- Verlag: Springer Nature Singapore
- ISBN-10: 9819928427
- ISBN-13: 9789819928422
- Erscheinungsdatum: 17.01.2024
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- Dateiformat: PDF
- Grösse: 9.76 MB
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