Self organizing maps as a tool for software analysis

Software measures (metrics) are indicators describing complexity of software products and processes. By their very nature, software measures give rise to a number of complex and highly dimensional data (patterns) that attempt to provide some useful insights into the very nature of the software systems. Such findings help to investigate and quantify the key properties of the systems such as their reliability, maintainability, readability, etc. In this study, self-organizing maps (SOMs) are considered as a vehicle for analysis of multidimensional data. From the functional point of view, SOMs are neural networks that map highly dimensional data into low dimensional (usually two-dimensional) space in such a way that the topology of the data is preserved. The construction of a map is realized through a process of unsupervised learning. Owing to the visualization capabilities arising in the two-dimensional space, one can visualize a structure in the original data and identify potential clusters as well as their size (compactness) and mutual distribution in the map. In this study, analysis of software data concerning JAVA classes is being carried out