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Overview: 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. In the Chapter 2, several common learning algorithms and their applications in software defect prediction are briefly introduced, including Deep Learning, transfer learning, dictionary learning, semi-supervised learning, and multi-view learning. In many real world applications, it is expensive or impossible to recollect the needed training data and rebuild the models. It would be nice to reduce the need and effort to recollect the training data. In such cases, transfer learning (TL) between task domains would be desirable. Transfer learning exploits the knowledge gained from a previous task to improve generalization on another related task.
Genre: Non-Fiction > Tech & Devices
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