NDLI: Variance Analysis in Software Fault Prediction Models

Content Provider	IEEE Xplore Digital Library
Author	Yue Jiang Jie Lin Cukic, B. Menzies, T.
Copyright Year	2009
Abstract	Software fault prediction models play an important role in softwarequality assurance. They identify software subsystems (modules,components, classes, or files) which are likely to contain faults.These subsystems, in turn, receive additional resources forverification and validation activities. Fault prediction models arebinary classifiers typically developed using one of the supervisedlearning techniques from either a subset of the fault data from thecurrent project or from a similar past project. In practice, itis critical that such models provide a reliable predictionperformance on the data not used in training. Variance is animportant reliability indicator of software fault prediction models.However, variance is often ignored or barely mentioned in manypublished studies. In this paper, through the analysis of twelvedata sets from a public software engineering repository from theperspective of variance, we explore the following five questionsregarding fault prediction models:(1) Do different types ofclassification performance measures exhibit different variance? (2)Does the size of the data set imply a more (or less) accurateprediction performance? (3) Does the size of training subset impactmodel's stability? (4) Do different classifiers consistently exhibitdifferent performance in terms of model's variance? (5) Are theredifferences between variance from 1000 runs and 10 runs of 10-fold crossvalidation experiments? Our results indicate that variance is avery important factor in understanding fault prediction models andwe recommend the best practice for reporting variance in empiricalsoftware engineering studies.
Starting Page	99
Ending Page	108
File Size	467361
Page Count	10
File Format	PDF
ISBN	9781424453757
ISSN	10719458
DOI	10.1109/ISSRE.2009.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-11-16
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analysis of variance Predictive models Software quality Software engineering Fault diagnosis Supervised learning Data analysis Performance analysis Software measurement Stability machine learning variance fault prediction models
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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