Intelligent Software Defect Prediction Xiaoyuan Jing Haowen Chen by Xiao-yuan Jing, Haowen Chen, Baowen Xu 9789819928422, 9819928427 instant download after payment.

In the past few decades, more and more researchers pay attention to SDP and a lot of intelligent SDP techniques have been presented. In order to obtain the high-quality representations of defect data, a lot of machine learning techniques such as dictionary learning, semisupervised learning, multi-view learning, and deep learning are applied to solve SDP problems. Besides, transfer learning techniques are also used to eliminate the divergence between different project data in CPDP scenario. Therefore, the combination with machine learning techniques is conducive to improving the prediction efficiency and accuracy, which can promote the research of intelligent SDP to make
significant progress.
We propose to draft this book to provide a comprehensive picture of the current state of SDP researches instead of improving and comparing existing SDP approaches. More specifically, this book introduces a range of machine learning-based SDP approaches proposed for different scenarios (i.e., WPDP, CPDP, and HDP). Besides, this book also provides deep insight into current SDP approaches performance and learned lessons for further SDP researches.
This book is mainly applicable to graduate students, researchers who work in or have interests in the areas of SDP, and the developers who are responsible for software maintenance.