A neural fuzzy system to evaluate software development productivity

Managing software development and maintenance projects requires early knowledge about quality and effort needed for achieving this quality level. 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. A special fuzzy neural network is introduced to obtain the fuzzy rules combining the metrics as premises and quality factors as conclusions. Using the law of DeMorgan, this net structure is able to learn premises just by changing the weights. Note that the authors change neither the number of neurons nor the number of connections. This new type of net allows for the extraction of knowledge acquired by training on the past process data directly in the form of fuzzy rules. Beyond that, it is possible to transfer all the known rules to the neural fuzzy system in advance. The suggested approach and its advantages towards common simulation and decision techniques is illustrated with experimental results. Its application area is in maintenance productivity. A module quality model-with respect to changes-provides both quality of fit (according to past data) and predictive accuracy (according to ongoing projects).