The paper presents the framework of a scale oriented scheme for the presentation and classification of reverse engineered sections of procedural code into objects. The aim is to develop an extensible system framework, which allows the output from a suite of data analysis tools to be visually presented to a user. The relationship between the analysis and visualisation is a progressive cycle, where each time through the cycle, the overall quality of the classified objects improves. This framework supports two distinct methods of information feedback from the visualisation to the analysis suite. The two feedback loops aim to increase both the ease of understanding for the reverse engineer and the quality of the resultant objects. As the analyst views the visualisation, the perceived view of the relationships exhibited in the system may be modified, removed or added to. This results in a change to the underlying graph or the clustering of that graph, which must be addressed in the visual presentation of the information using a variety of techniques to maintain the users' 'mental map', or understanding each time through the cycle.
['Aaron J. Quigley', 'Margot Postema', 'Heinz W. Schmidt']
ReVis: reverse engineering by clustering and visual object classification