Comparative Study of Clustering Techniques for the Organization of Software Repositories

• Published in: 7th International Conference on Hybrid Intelligent Systems (HIS 2007) pp. 372 - 377
• Main Authors: Veras, R.C., Meira, S.R.L., Oliveira, A.L.I., Melo, B.J.M.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2007
• Subjects: Application software; Java; Machine learning; Productivity; Programming; Quantization; Self organizing feature maps; Software quality; Software reusability; Visualization
• DOI: 10.1109/HIS.2007.41

Software reuse is essential for improving the productivity and quality of software projects. One of the key issues to promote the adoption of software reuse in companies is the development of effective repositories of software components. It is also very important to have good methods for searching and retrieval of the components. Clustering techniques can help by providing a visualization of the repository of software components as well as in helping to refine the searches by grouping together similar components. In this paper we quantitatively compare two clustering techniques, namely, self-organizing maps (SOM) and growing hierarquical SOM (GHSOM) for clustering a repository of classes from a Java API for building mobile systems. The performance measure was the quantization error. The simulations have shown that GHSOM outperforms SOM in these tasks. GHSOM is more suitable for this task because it is a constructive technique, which is an advantage in tackling the growth of the repository of software components.