Abstract
In this study, we are concerned with the analysis of software quality data in the framework of neurofuzzy models. We discuss how a specificity of software data relates to the character of neurofuzzy processing and elaborate on the use of the main features of neurocomputing and fuzzy sets in this setting. It is shown how self organizing maps help reveal and visualize a structure of software data. We propose a new topology of the neurofuzzy system that seamlessly combines the geometry of feature spaces (being expressed in the form of perceptrons) and the logic of aggregation of these perceptrons that is realized through specialized fuzzy neurons. The experimental part of the study is concerned with the MIS data set available in the literature on software quality and dealing with dependencies between software complexity measures characterizing software modules and the ensuing number of changes (modifications) made to them.
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Pedrycz, W., Reformat, M., Pizzi, N. (2004). Neurofuzzy Analysis of Software Quality Data. In: Damiani, E., Madravio, M., Jain, L.C. (eds) Soft Computing in Software Engineering. Studies in Fuzziness and Soft Computing, vol 159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44405-3_9
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DOI: https://doi.org/10.1007/978-3-540-44405-3_9
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