NDLI: Active Semi-supervised Defect Categorization

Content Provider	IEEE Xplore Digital Library
Author	Ferdian Thung Le, X.-B.D. Lo, D.
Copyright Year	2015
Description	Author affiliation: Sch. of Inf. Syst., Singapore Manage. Univ., Singapore, Singapore (Ferdian Thung; Le, X.-B.D.; Lo, D.)
Abstract	Defects are inseparable part of software development and evolution. To better comprehend problems affecting a software system, developers often store historical defects and these defects can be categorized into families. IBM proposes Orthogonal Defect Categorization (ODC) which include various classifications of defects based on a number of orthogonal dimensions (e.g., Symptoms and semantics of defects, root causes of defects, etc.). To help developers categorize defects, several approaches that employ machine learning have been proposed in the literature. Unfortunately, these approaches often require developers to manually label a large number of defect examples. In practice, manually labelling a large number of examples is both time-consuming and labor-intensive. Thus, reducing the onerous burden of manual labelling while still being able to achieve good performance is crucial towards the adoption of such approaches. To deal with this challenge, in this work, we propose an active semi-supervised defect prediction approach. It is performed by actively selecting a small subset of diverse and informative defect examples to label (i.e., Active learning), and by making use of both labeled and unlabeled defect examples in the prediction model learning process (i.e., Semi-supervised learning). Using this principle, our approach is able to learn a good model while minimizing the manual labeling effort. To evaluate the effectiveness of our approach, we make use of a benchmark dataset that contains 500 defects from three software systems that have been manually labelled into several families based on ODC. We investigate our approach's ability in achieving good classification performance, measured in terms of weighted precision, recall, F-measure, and AUC, when only a small number of manually labelled defect examples are available. Our experiment results show that our active semi-supervised defect categorization approach is able to achieve a weighted precision, recall, F-measure, and AUC of 0.651, 0.669, 0.623, and 0.710, respectively, when only 50 defects are manually labelled. Furthermore, it outperforms an existing active multi-class classification algorithm, proposed in the machine learning community, by a substantial margin.
Starting Page	60
Ending Page	70
File Size	236385
Page Count	11
File Format	PDF
e-ISBN	9781467381598
DOI	10.1109/ICPC.2015.15
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-05-18
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Active learning Clustering algorithms Defect categorization Semisupervised learning Software systems Feature extraction Data models Support vector machine Labeling Clustering Semi supervised learning
Content Type	Text
Resource Type	Article

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 Libarray 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

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