Condensing reverse engineered class diagrams through class name based abstraction
Paper i proceeding, 2014

In this paper, we report on a machine learning approach to condensing class diagrams. The goal of the algorithm is to learn to identify what classes are most relevant to include in the diagram, as opposed to full reverse engineering of all classes. This paper focuses on building a classifier that is based on the names of classes in addition to design metrics, and we compare to earlier work that is based on design metrics only. We assess our condensation method by comparing our condensed class diagrams to class diagrams that were made during the original forward design. Our results show that combining text metrics with design metrics leads to modest improvements over using design metrics only. On average, the improvement reaches 5.3%. 7 out of 10 evaluated case studies show improvement ranges from 1% to 22%.

Software Engineering

Data Mining

UML

Text Mining

Författare

M.H. Osman

M.R.V. Chaudron

P.W.H. Van Der Putten

Truong Ho Quang

Chalmers, Data- och informationsteknik, Software Engineering

2014 4th World Congress on Information and Communication Technologies, WICT 2014

158-163

Ämneskategorier

Programvaruteknik

Systemvetenskap

DOI

10.1109/WICT.2014.7077321

ISBN

978-1-4799-8115-1