Clustering software systems to identify subsystem structures using knowledgebase

Abstract

The structure of a software system deteriorates as a result of continuous maintenance activity. For the purpose of software reengineering or reverse engineering, often the software engineers get only the original source code as the most updated source of information due to lack of current documentation and limited or nonexistent availability of the original designers. The application of clustering techniques to the software systems aiming to discover the feature-oriented and meaningful subsystems helps the software engineers to understand the high-level features provided by those subsystems which is very essential for the purpose of software reengineering and reverse engineering. Continuous research is going on in the recent years -addressing different issues in the software clustering problem. Similarity measurement is the key to perform successful clustering. The similarity measurement criteria used in the existing clustering technique has the common drawback that they do not incorporate the diversity of software systems. Our approach introduces the use of the Knowledgebase which acts as the repository of information about the internal structure of the Generic types of the software systems to provide the guidelines on similarity measurement criteria and weights. The final clustering is done by integrating automatically generated subsystems with the known subsystems (provided by the Knowledgebase). Thus the new clustering technique is a semi automatic technique with the provision of tuning the results by the software engineers. In our research, we have developed a tool named BUET Cluster 1.0 which implements our new clustering technique. This clustering tool has been evaluated by using a benchmark named Mojo distance for different well known software systems. The experimental results show that our approach generates more appropriate subsystems than the other existing clustering techniques and outperforms other clustering techniques in different dimensions of software clustering quality.