Parsimonious classifiers for software quality assessment
2007pp. 411–412
Citations Over Time
Abstract
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.
Related Papers
- → A survey on metric of software complexity(2010)91 cited
- On the Improvement of Cyclomatic Complexity Metric(2015)
- → Measuring the quality of various version an object-oriented software utilizing CK metrics(2018)10 cited
- → Validation of measurement tools to extract metrics from open source projects(2012)9 cited
- → Software metrics: using measurement theory to describe the properties and scales of static software complexity metrics(1989)48 cited