Article
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Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction
Version 1
: Received: 16 November 2018 / Approved: 19 November 2018 / Online: 19 November 2018 (11:48:50 CET)
How to cite: Ren, S.; Zhang, W.; Munir, H. S.; Xia, L. Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction. Preprints 2018, 2018110461. https://doi.org/10.20944/preprints201811.0461.v1 Ren, S.; Zhang, W.; Munir, H. S.; Xia, L. Dissimilarity Space Based Multi-Source Cross-Project Defect Prediction. Preprints 2018, 2018110461. https://doi.org/10.20944/preprints201811.0461.v1
Abstract
Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods using feature attributes to represent samples, which can not avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space ( DM-CPDP). This method first uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to form the dissimilarity space, and in this space the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the KNN algorithm is used to label those samples. Finally, we use TrAdaBoost method to establish the prediction model. The experimental results show that our approach has better performance than other traditional CPDP methods.
Keywords
Software quality; cross-project defect prediction; multi-source; dissimilarity space; arc-cosine kernel function
Subject
Computer Science and Mathematics, Computer Science
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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