Identifying Objects in Procedural Programs Using Clustering Neural Networks

This paper presents a general approach for the identification of objects in procedural programs. The approach is based on neural architectures that perform an unsupervised learning of clusters. We describe two such neural architectures, explain how to use them in identifying objects in software systems and briefly describe a prototype tool, which implements the clustering algorithms. With the aid of several examples, we explain how our approach can identify abstract data types as well as groups of routines which reference a common set of data. The clustering results are compared to the results of many other object identification techniques. Finally, several case studies were performed on existing programs to evaluate the object identification approach. Results concerning two representative programs and their generated clusters are discussed.