On a Neural-Fuzzy Technique with GA-Optimization for Software Quality Models
Ekkehard Baisch, Christoph Ebert · Deutscher Universitätsverlag eBooks · 1997
Managing software development and maintenance projects requires early knowledge about quality and effort needed for achieving this quality level. Effort estimations and staffing decisions are often based on productivity knowledge of the software development process. Quality-based productivity management is introduced as one approach for achieving and using such process knowledge. Fuzzy rules are used as a basis for constructing quality models that can identify outlying software components that might cause potential quality problems. This provides us with a fuzzy expert system tailored to the corresponding development environment. The value of any knowledge-based system is determined by the accuracy and cost of such predictions which are used to improve life-cycle productivity. Using the self-learning capability of neural networks, prestructured with genetic algorithms, the fuzzy rules can be automatically generated from example data to reduce the cost and improve the accuracy. The premises of the found rules consist of metrics drawing conclusions on the desired quality factors. The generated quality model - with respect to changes 2D provides both quality of fit (according to past data) and predictive accuracy (according to ongoing projects).