DocumentCode
2401540
Title
Parsimonious classifiers for software quality assessment
Author
Shin, Miyoung ; Goel, Amrit L. ; Ratanothayanon, Sunidaa ; Paul, Raymond A.
Author_Institution
Kyungpook Nat. Univ., Daegu
fYear
2007
fDate
14-16 Nov. 2007
Firstpage
411
Lastpage
412
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
Gaussian processes; radial basis function networks; software fault tolerance; software metrics; software quality; Gaussian kernels; cyclomatic complexity; fault-proneness prediction; parsimonious classifiers; radial basis functions; software engineering; software metrics; software modules; software quality assessment; Kernel; Mathematical model; NASA; Neural networks; Predictive models; Software engineering; Software metrics; Software quality; System testing; Systems engineering and theory; Software quality; Classification; Parsimonious classifiers; Software metrics;
fLanguage
English
Publisher
ieee
Conference_Titel
High Assurance Systems Engineering Symposium, 2007. HASE '07. 10th IEEE
Conference_Location
Plano, TX
ISSN
1530-2059
Print_ISBN
978-0-7695-3043-7
Type
conf
DOI
10.1109/HASE.2007.75
Filename
4404779
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