Factbase and Decomposition Generation

The software maintenance research community has developed a large number of approaches that can help maintainers understand large software systems accurately and efficiently. However, tools that can facilitate research in program comprehension are rarely publicly available. In this paper, we introduce two approaches that generate artifacts, such as fact bases and decompositions, that can be used to study the behaviour of existing software clustering approaches for the comprehension of large software systems. We also present three distinct applications of these approaches: the development of a simple evaluation method for clustering algorithms, the study of the behaviour of the objective function of the Bunch tool, and the calculation of a congruity measure for clustering evaluation measures. Implementations of the two approaches are available online.