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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.
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