Keywords: software repository, self-organizing map, growing hierarchical self-organizing map, software reuse, data mining
Received July 1, 2005; accepted November 24, 2005.
Communicated by Sung Shin.
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Songsri Tangsripairoj and M. H. Samadzadeh*
Department of Computer Science, Faculty of Science
Mahidol University
Bangkok 10400, Thailand
E-mail: ccsts@mahidol.ac.th
*Department of Computer Science
Oklahoma State University
Stillwater, OK 74078, U.S.A.
E-mail: samad@cs.okstate.edu
A software repository, a place where reusable components are stored and searched
for, is a key ingredient for instituting and popularizing software reuse. It is vital that a
software repository should be well-organized and provide efficient tools for developers
to locate reusable components that meet their requirements. The growing hierarchical
self-organizing map (GHSOM), an unsupervised learning neural network, is a powerful
data mining technique for the clustering and visualization of large and complex data sets.
The resulting maps, serving as retrieval interfaces, can be beneficial to developers in obtaining
better insight into the structure of a software repository and increasing their understanding
of the relationships among software components. The GHSOM, which is an
improvement over the basic self-organizing map (SOM), can adapt its architecture during
its learning process and expose the hierarchical structure that exists in the original
data. In this paper, we demonstrate the potential of the GHSOM for the organization and
visualization of a collection of reusable components stored in a software repository, and
compare the results with the ones obtained by using the traditional SOM.
Received July 1, 2005; accepted November 24, 2005.
Communicated by Sung Shin.