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Unsupervised Deep Domain Adaptation for Heterogeneous Defect Prediction
Lina GONG Shujuan JIANG Qiao YU Li JIANG
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/03/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Software Engineering
heterogeneous defect prediction, neural networks, maximum mean discrepancy, class-imbalance,
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Heterogeneous defect prediction (HDP) is to detect the largest number of defective software modules in one project by using historical data collected from other projects with different metrics. However, these data can not be directly used because of different metrics set among projects. Meanwhile, software data have more non-defective instances than defective instances which may cause a significant bias towards defective instances. To completely solve these two restrictions, we propose unsupervised deep domain adaptation approach to build a HDP model. Specifically, we firstly map the data of source and target projects into a unified metric representation (UMR). Then, we design a simple neural network (SNN) model to deal with the heterogeneous and class-imbalanced problems in software defect prediction (SDP). In particular, our model introduces the Maximum Mean Discrepancy (MMD) as the distance between the source and target data to reduce the distribution mismatch, and use the cross-entropy loss function as the classification loss. Extensive experiments on 18 public projects from four datasets indicate that the proposed approach can build an effective prediction model for heterogeneous defect prediction (HDP) and outperforms the related competing approaches.