Parsimonious classifiers for software quality assessment

Miyoung Shin; Sunida Ratanothayanon; Amrit L. Goel; Raymond A. Paul

doi:10.1109/HASE.2007.60

Parsimonious classifiers for software quality assessment

Miyoung Shin, Sunida Ratanothayanon, Amrit L. Goel, Raymond A. Paul

School of Electronics Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

Modeling to predict fault-proneness of software modules is an important area of research in software engineering. Most such models employ a large number of basic and derived metrics as predictors. This paper presents modeling results based on only two metrics, lines of code and cyclomatic complexity, using radial basis functions with Gaussian kernels as classifiers. Results from two NASA systems are presented and analyzed.

Original language	English
Title of host publication	Proceedings - 10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007
Pages	411-412
Number of pages	2
DOIs	https://doi.org/10.1109/HASE.2007.60
State	Published - 2007
Event	10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007 - Dallas, TX, United States Duration: 14 Nov 2007 → 16 Nov 2007

Publication series

Name	Proceedings of IEEE International Symposium on High Assurance Systems Engineering
ISSN (Print)	1530-2059

Conference

Conference	10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007
Country/Territory	United States
City	Dallas, TX
Period	14/11/07 → 16/11/07

Keywords

Classification
Parsimonious classifiers
Software metrics
Software quality

Access to Document

10.1109/HASE.2007.60

Cite this

Shin, M., Ratanothayanon, S., Goel, A. L., & Paul, R. A. (2007). Parsimonious classifiers for software quality assessment. In Proceedings - 10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007 (pp. 411-412). Article 4404779 (Proceedings of IEEE International Symposium on High Assurance Systems Engineering). https://doi.org/10.1109/HASE.2007.60

@inproceedings{9795990827c84219a4db679ed28024f3,

title = "Parsimonious classifiers for software quality assessment",

abstract = "Modeling to predict fault-proneness of software modules is an important area of research in software engineering. Most such models employ a large number of basic and derived metrics as predictors. This paper presents modeling results based on only two metrics, lines of code and cyclomatic complexity, using radial basis functions with Gaussian kernels as classifiers. Results from two NASA systems are presented and analyzed.",

keywords = "Classification, Parsimonious classifiers, Software metrics, Software quality",

author = "Miyoung Shin and Sunida Ratanothayanon and Goel, {Amrit L.} and Paul, {Raymond A.}",

year = "2007",

doi = "10.1109/HASE.2007.60",

language = "English",

isbn = "0769530435",

series = "Proceedings of IEEE International Symposium on High Assurance Systems Engineering",

pages = "411--412",

booktitle = "Proceedings - 10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007",

note = "10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007 ; Conference date: 14-11-2007 Through 16-11-2007",

}

Shin, M, Ratanothayanon, S, Goel, AL & Paul, RA 2007, Parsimonious classifiers for software quality assessment. in Proceedings - 10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007., 4404779, Proceedings of IEEE International Symposium on High Assurance Systems Engineering, pp. 411-412, 10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007, Dallas, TX, United States, 14/11/07. https://doi.org/10.1109/HASE.2007.60

Parsimonious classifiers for software quality assessment. / Shin, Miyoung; Ratanothayanon, Sunida; Goel, Amrit L. et al.
Proceedings - 10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007. 2007. p. 411-412 4404779 (Proceedings of IEEE International Symposium on High Assurance Systems Engineering).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Ratanothayanon, Sunida

AU - Goel, Amrit L.

AU - Paul, Raymond A.

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AB - Modeling to predict fault-proneness of software modules is an important area of research in software engineering. Most such models employ a large number of basic and derived metrics as predictors. This paper presents modeling results based on only two metrics, lines of code and cyclomatic complexity, using radial basis functions with Gaussian kernels as classifiers. Results from two NASA systems are presented and analyzed.

KW - Classification

KW - Parsimonious classifiers

KW - Software metrics

KW - Software quality

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DO - 10.1109/HASE.2007.60

M3 - Conference contribution

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BT - Proceedings - 10th IEEE International Symposium on High Assurance Systems Engineering, HASE 2007

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Y2 - 14 November 2007 through 16 November 2007

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Parsimonious classifiers for software quality assessment

Abstract

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Keywords

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