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%.