Modeling?to?predict faultproneness 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.
Index Terms:
?Software quality, Software metrics, Classification, Parsimonious classifiers
Citation:
Miyoung Shin, Sunida Ratanothayanon, Amrit L. Goel, Raymond A. Paul, "Parsimonious?Classifiers?for?Software?Quality?Assessment," hase, pp.411-412, 10th IEEE High Assurance Systems Engineering Symposium (HASE'07), 2007