Improving the Accuracy of UML Class Model Recovery
Kun Wang, Wuwei Shen · 2007
The gap between UML class models and their implementations impedes program understanding and analysis, and is a source of program errors. Although many reverse engineering techniques were proposed to bridge this gap, two major problems still exist. First, the accuracy of association inference from container classes is not adequate without considering iterators. Second, associations implemented by inherited fields are missed by existing techniques. In this paper, we present an approach to precisely and automatically recover a class model from Java byte code. Our approach tackles the above problems and improves the accuracy of the recovered models. The preliminary empirical results show that our approach achieved a higher accuracy for association inference than existing reverse engineering tools.