Visualization and analysis of software engineering data using self-organizing maps

There is no question that accuracy is an important requirement of classification and prediction models used in software engineering management. It is, however, just one of a number of attributes that contribute to a model being 'useful'. Understandably much research has been undertaken with the objective of maximizing model accuracy, but this has often occurred with little regard for these other model attributes, which might include cost-effectiveness, credibility and, for want of a better term, meaningfulness. The research described in this paper addresses both model accuracy and meaningfulness as conveyed by self-organizing maps (SOMs). SOMs are neural-network based representations of data distributions that provide two-dimensional depictions of multi-dimensional relationships. As such they can enable developers and project managers (and researchers) to visualize often complex interactions among and between software measurement data. We illustrate the effectiveness of SOMs by building on two previous empirical studies. Not only are the maps able to portray graphically the distributions of variables and their interrelationships, they also prove to be effective in terms of classification and prediction accuracy. As a result we believe that they could be a useful supplementary tool for researchers and managers concerned with understanding, modeling and controlling complex software projects

Visualizing multidimensional data similarities:Improvements and applications

Multidimensional data is increasingly more prominent and important in many application domains. Such data typically consist of a large set of elements, each of which described by several measurements (dimensions). During the design of techniques and tools to process this data, a key component is to gather insights into their structure and patterns, which can be described by the notion of similarity between elements. Among these techniques, multidimensional projections and similarity trees can effectively capture similarity patterns and handle a large number of data elements and dimensions. However, understanding and interpreting these patterns in terms of the original data dimensions is still hard. This thesis addresses the development of visual explanatory techniques for the easy interpretation of similarity patterns present in multidimensional projections and similarity trees, by several contributions. First, we propose methods that make the computation of similarity trees efficient for large datasets, and also enhance its visual representation to allow the exploration of more data in a limited screen. Secondly, we propose methods for the visual explanation of multidimensional projections in terms of groups of similar elements. These are automatically annotated to describe which dimensions are more important to define their notion of group similarity. We show next how these explanatory mechanisms can be adapted to handle both static and time-dependent data. Our proposed techniques are designed to be easy to use, work nearly automatically, and are demonstrated on a variety of real-world large data obtained from image collections, text archives, scientific measurements, and software engineering