NDLI: Low-rank representation for semi-supervised software defect prediction

Content Provider	IET Digital Library
Author	Zhang, Zhi Wu Jing, Xiao Yuan Wu, Fei
Abstract	Software defect prediction based on machine learning is an active research topic in the field of software engineering. The historical defect data in software repositories may contain noises because automatic defect collection is based on modified logs and defect reports. When the previous defect labels of modules are limited, predicting the defect-prone modules becomes a challenging problem. In this study, the authors propose a graph-based semi-supervised defect prediction approach to solve the problems of insufficient labelled data and noisy data. Graph-based semi-supervised learning methods used the labelled and unlabelled data simultaneously and consider them as the nodes of the graph at the training phase. Therefore, they solve the problem of insufficient labelled samples. To improve the stability of noisy defect data, a powerful clustering method, low-rank representation (LRR), and neighbourhood distance are used to construct the relationship graph of samples. Therefore, they propose a new semi-supervised defect prediction approach, named low-rank representation-based semi-supervised software defect prediction (LRRSSDP). The widely used datasets from NASA projects and noisy datasets are employed as test data to evaluate the performance. Experimental results show that (i) LRRSSDP outperforms several representative state-of-the-art semi-supervised defect prediction methods; and (ii) LRRSSDP can maintain robustness in noisy environments.
Starting Page	527
Ending Page	535
Page Count	9
ISSN	17518806
Volume Number	12
e-ISSN	17518814
Issue Number	Issue 6, Dec (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-sen/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-sen.2017.0198
Journal	IET Software
Publisher Date	2018-05-16
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Defect Collection Combinatorial Mathematics Data Handling Technique Defect Reports Defect-prone Module Diagnostic, Testing, Debugging And Evaluating System Graph Theory Historical Defect Data Insufficient Labelled Data Insufficient Labelled Sample Knowledge Engineering Technique Learning in AI Low-rank Representation LRRSSDP Noisy Data Noisy Defect Data Pattern Clustering Program Diagnostics Semisupervised Defect Prediction Approach Semisupervised Software Defect Prediction Software Engineering Software Repositories Unlabelled Data
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design

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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
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