Fine-Grained Design Pattern Detection

Design patterns have been used successfully to build well-organized, flexible software systems. In the reverse engineering field, detecting design patterns can simplify program comprehension by providing clues about the rationale behind the system´s design. However, the precision and recall of existing approaches to design pattern detection leave room for improvement. In this paper, we introduce a novel technique called FiG that complements existing detection methods by utilizing fine-grained static information contained in the software system. Our approach filters a large number of false positives by utilizing fine-grained rules that describe the static structure of a design pattern. We have implemented our approach in the context of the Eiffel programming language. Several experiments were performed to determine the effectiveness of the proposed approach.