NDLI: Towards improving statistical modeling of software engineering data: think locally, act globally!

Content Provider	Springer Nature Link
Author	Bettenburg, Nicolas Nagappan, Meiyappan Hassan, Ahmed E.
Copyright Year	2014
Abstract	Much research in software engineering (SE) is focused on modeling data collected from software repositories. Insights gained over the last decade suggests that such datasets contain a high amount of variability in the data. Such variability has a detrimental effect on model quality, as suggested by recent research. In this paper, we propose to split the data into smaller homogeneous subsets and learn sets of individual statistical models, one for each subset, as a way around the high variability in such data. Our case study on a variety of SE datasets demonstrates that such local models can significantly outperform traditional models with respect to model fit and predictive performance. However, we find that analysts need to be aware of potential pitfalls when building local models: firstly, the choice of clustering algorithm and its parameters can have a substantial impact on model quality. Secondly, the data being modeled needs to have enough variability to take full advantage of local modeling. For example, our case study on social data shows no advantage of local over global modeling, as clustering fails to derive appropriate subsets. Lastly, the interpretation of local models can become very complex when there is a large number of variables or data subsets. Overall, we find that a hybrid approach between local and traditional global modeling, such as Multivariate Adaptive Regression Splines (MARS) combines the best of both worlds. MARS models are non-parametric and thus do not require prior calibration of parameters, are easily interpretable by analysts and outperform local, as well as traditional models out of the box in four out of five datasets in our case study.
Starting Page	294
Ending Page	335
Page Count	42
File Format	PDF
ISSN	13823256
Journal	Empirical Software Engineering
Volume Number	20
Issue Number	2
e-ISSN	15737616
Language	English
Publisher	Springer US
Publisher Date	2014-01-15
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Software metrics Statistical modeling Clustering Software Engineering/Programming and Operating Systems Programming Languages, Compilers, Interpreters
Content Type	Text
Resource Type	Article
Subject	Software

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
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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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