RULE-BASED FUZZY CLASSIFICATION FOR SOFTWARE QUALITY-CONTROL

In the area of software development it would be of great benefit to predict early in the development process those components of the software system that are likely to have a high error rate or that need high development effort. This paper discusses fuzzy classification techniques as a basis for constructing quality models that can identify outlying software components that might cause potential quality problems. These models are using software complexity metrics that arc available early in the development process, thus providing support during the design and the code phase. Experimental results based on real project data are presented to underline the suggested approach and its advantages compared to crisp classification, and decision techniques. The application to given data''sets and to ongoing projects in the context of consulting activities indicates that a module quality model - with respect to changes - provides both quality of fit (according to past data) and predictive accuracy (according to the current projects).