Building a software experience factory using granular-based models

Software quality is one of the most important practical features of software development. Project managers and developers look for methods and tools supporting software development processes and ensuring a required level of quality. To make such tools relevant, they should provide the designer/manager with some quantitative input useful for purposes of interpretation of the results. Knowledge provided by the tools leads to better understanding of the investigated phenomena. In this paper, we propose a comprehensive development methodology of logic-based models represented by fuzzy neural networks. A process of model development is performed in the stages of structural and parametric optimization. The structural optimization framework utilizes mechanisms of evolutionary computing, which become especially attractive in light of the structural optimization of the models. The parametric optimization is performed using the gradient-base method. The study comprises two detailed case studies dealing with existing software data. The first one deals with the quality assessment of software objects in an exploratory biomedical data analysis and visualization system. The second case is concerned with the model of software development effort discussed in the setting of some medical information system.