Intelligent Software Defect Prediction

Software defect prediction (SDP) is essential for software quality assurance, as it helps developers reduce maintenance costs by identifying and repairing defects. This book provides a comprehensive overview of SDP research, including machine-learning-based approaches for different scenarios, and insights into their performance and lessons learned. It is valuable for researchers, graduate students, and practitioners in the field.

Format: Hardback
Length: 205 pages
Publication date: 18 January 2024
Publisher: Springer Verlag, Singapore

Software products face numerous challenges, including low quality, high prices, and difficulty in maintenance. Software defects, which result in incorrect or unexpected results and behaviors, are a major contributor to these issues. Software defect prediction (SDP) is a critical research field in software engineering that aims to identify potential defects before they occur. This book provides a comprehensive overview of SDP research, including machine-learning-based approaches for different scenarios such as white-box, black-box, and hybrid prediction. It also shares insights into the performance of current SDP approaches and lessons learned for future research efforts. The book is intended for researchers, graduate students, and practitioners who want to gain deeper insights into SDP and find new research directions. It offers a comprehensive introduction to the current state of SDP and detailed descriptions of representative SDP approaches.

Dimension: 235 x 155 (mm)
ISBN-13: 9789819928415
Edition number: 1st ed. 2023