Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80423
Title: HDA : cross-project defect prediction via heterogeneous domain adaptation with dictionary learning
Authors: Xu, Z 
Yuan, PP
Zhang, T
Tang, YT 
Li, S 
Xia, Z
Keywords: Heterogeneous cross-project defect prediction
Heterogeneous domain adaptation
Dictionary learning
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE access, 2018, v. 6, p. 57597-57613 How to cite?
Journal: IEEE access 
Abstract: Cross-Project Defect Prediction (CPDP) is an active topic for predicting defects on projects (target projects) with scarce-labeled data by reusing the classification models from other projects (source projects). Traditional CPDP methods require common features between the data of two projects and utilize them to construct defect prediction models. However, when cross-project data do not satisfy the requirement, i.e., heterogeneous CPDP (HCPDP) scenario, these methods become infeasible. In this paper, we propose a novel HCPDP method called Heterogeneous Domain Adaptation (HDA) to address the issue. HDA treats the cross-project data as being from two different domains with heterogeneous feature sets. It employs the domain adaptation method to embed the data from the two domains into a comparable feature space with a lower dimension, then measures the difference between the two mapped domains of data using the dictionaries learned from them with the dictionary learning technique. We comprehensively evaluate HDA on 94 cross-project pairs of 12 projects from three open-source defect data sets with three performance indicators, i.e., F-measure, Balance, and AUC. Compared with the two state-of-the-art HCPDP methods, the experimental results indicate that HDA improves 0.219 and 0.336 in terms of F-measure, 0.185 and 0.215 in terms of Balance, and 0.131 and 0.035 in terms of AUC. In addition, HDA achieves comparable results compared with Within-Project Defect Prediction (WPDP) setting and a state-of-the-art unsupervised learning method in most cases.
URI: http://hdl.handle.net/10397/80423
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2018.2873755
Rights: © 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Post with permission of the publisher.
The following publication Xu, Z., Yuan, P.P., Zhang, T., Tang, Y.T., Li, S., & Xia, Z. (2018). HDA : cross-project defect prediction via heterogeneous domain adaptation with dictionary learning. IEEE Access, 6, 57597-57613 is available at https://dx.doi.org/10.1109/ACCESS.2018.2873755
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Xu_HDA_Cross-Project_Defect.pdf3.01 MBAdobe PDFView/Open
Access
View full-text via PolyU eLinks SFX Query
Show full item record
PIRA download icon_1.1View/Download Contents

Page view(s)

19
Citations as of Aug 21, 2019

Download(s)

17
Citations as of Aug 21, 2019

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.