By Topic

Identifying design patterns in object-oriented software systems using unsupervised learning

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Czibula, I.G. ; Dept. of Comput. Sci., Babe-Bolyai Univ., Bolyai ; Czibula, G.

Design patterns offer timeless and elegant solutions to common problems in software design. From a program-understanding and a reverse engineering perspective the discovery of patterns in a software artifact (design or code) represents a step in the program understanding process. It would be useful to find instances of design patterns especially in designs where they were not used explicitly or where their use is not documented, as this could improve the maintainability of software. Clustering is considered the most important unsupervised learning problem. The aim of clustering methods is to differentiate groups (classes or clusters) inside a given set of objects. In this paper we introduce a hierarchical clustering based approach in order to localize instances of design patterns in existing software systems. We also provide an experimental evaluation of our approach, illustrating its advantages in comparison with similar existing approaches.

Published in:

Automation, Quality and Testing, Robotics, 2008. AQTR 2008. IEEE International Conference on  (Volume:3 )

Date of Conference:

22-25 May 2008