NDLI: Semi-supervised learning for software quality estimation

Content Provider	IEEE Xplore Digital Library
Author	Seliya, N. Khoshgoftaar, T.M. Zhong, S.
Copyright Year	2004
Description	Author affiliation: Dept. of Comput. Sci. & Eng., Florida Atlantic Univ., Boca Raton, FL, USA (Seliya, N.; Khoshgoftaar, T.M.; Zhong, S.)
Abstract	A software quality estimation model is often built using known software metrics and fault data obtained from program modules of previously developed releases or similar projects. Such a supervised learning approach to software quality estimation assumes that fault data is available for all the previously developed modules. Considering the various practical issues in software project development, fault data may not be available for all the software modules in the training data. More specifically, the available labeled training data is such that a supervised learning approach may not yield good software quality prediction. In contrast, a supervised classification scheme aided by unlabeled data, i.e., semisupervised learning, may yield better results. This work investigates semisupervised learning with the expectation maximization (EM) algorithm for the software quality classification problem. Case studies of software measurement data obtained from two NASA software projects, JM1 and KC2, are used in our empirical investigation. A small portion of the JM1 dataset is randomly extracted and used as the labeled data, while the remaining JM1 instances are used as unlabeled data. The performance of the semisupervised classification models built using the EM algorithm is evaluated by using the KC2 project as a test dataset. It is shown that the EM-based semisupervised learning scheme improves the predictive accuracy of the software quality classification models.
Sponsorship	IEEE Comput. Soc.
Starting Page	183
Ending Page	190
File Size	255111
Page Count	8
File Format	PDF
ISBN	076952236X
ISSN	10823409
DOI	10.1109/ICTAI.2004.108
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-11-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Semisupervised learning Software quality Supervised learning Training data Software metrics Software algorithms Software measurement NASA Data mining Testing expectation maximization semi-supervise d learning software quality estimation unlabeled data software metrics
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 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

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