NDLI: Joint distribution matching model for distribution–adaptation-based cross-project defect prediction

Content Provider	IET Digital Library
Author	Qiu, Shaojian Lu, Lu Jiang, Siyu
Abstract	Using classification methods to predict software defect is receiving a great deal of attention and most of the existing studies primarily conduct prediction under the within-project setting. However, there usually had no or very limited labelled data to train an effective prediction model at an early phase of the software lifecycle. Thus, cross-project defect prediction (CPDP) is proposed as an alternative solution, which is learning a defect predictor for a target project by using labelled data from a source project. Differing from previous CPDP methods that mainly apply instances selection and classifiers adjustment to improve the performance, in this study, the authors put forward a novel distribution–adaptation-based CPDP approach, joint distribution matching (JDM). Specifically, JDM aims to minimise the joint distribution divergence between the source and target project to improve the CPDP performance. By constructing an adaptive weight vector for the instances of the source project, JDM can be effective and robust at reducing marginal distribution discrepancy and conditional distribution discrepancy simultaneously. Extensive experiments verify that JDM can outperform related distribution–adaptation-based methods on 15 open-source projects that are derived from two types of repositories.
Starting Page	393
Ending Page	402
Page Count	10
ISSN	17518806
Volume Number	13
e-ISSN	17518814
Issue Number	Issue 5, Oct (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-sen/13/5
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-sen.2018.5131
Journal	IET Software
Publisher Date	2019-04-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Weight Vector Algebra Classification Method Classifier Adjustment Conditional Distribution Discrepancy CPDP Performance Cross-project Defect Prediction Data Handling Technique Defect Predictor Distribution–adaptation-based Methods Effective Prediction Model Instances Selection JDM Joint Distribution Divergence Joint Distribution Matching Model Knowledge Engineering Technique Labelled Data Learning in AI Marginal Distribution Discrepancy Open-source Projects Pattern Classification Software Defect Software Lifecycle Source Project Target Project Vector Within-project Setting
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multiple-components weights model for cross-project software defect prediction

Manifold embedded distribution adaptation for cross-project defect prediction

Software Defect Prediction via Deep Belief Network

BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction

Domain Adaptation Network Based on Autoencoder

Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network

Cross-Project Defect Prediction Considering Multiple Data Distribution Simultaneously

Semi-supervised Software Defect Prediction Using Task-Driven Dictionary Learning

Multi-metric domain adaptation for unsupervised transfer learning

Joint distribution matching model for distribution–adaptation-based cross-project defect prediction

Similar Documents

Multiple-components weights model for cross-project software defect prediction

Manifold embedded distribution adaptation for cross-project defect prediction

Software Defect Prediction via Deep Belief Network

BiLO-CPDP: Bi-Level Programming for Automated Model Discovery in Cross-Project Defect Prediction

Domain Adaptation Network Based on Autoencoder

Within-project and cross-project just-in-time defect prediction based on denoising autoencoder and convolutional neural network

Cross-Project Defect Prediction Considering Multiple Data Distribution Simultaneously

Semi-supervised Software Defect Prediction Using Task-Driven Dictionary Learning

Multi-metric domain adaptation for unsupervised transfer learning

Joint distribution matching model for distribution–adaptation-based cross-project defect prediction