A Customizable Approach to Design Patterns Recognition Based on Feature Types

Abstract

Accurate recognition of design patterns from source code supports development-related tasks such as program comprehension, maintenance, reverse engineering, and re-engineering. Researchers focused on this problem for many years, and a variety of recognition approaches have been proposed. Though, much progress has been made, we still identify a lack of flexibility and accuracy in the pattern recognition process. This paper evaluates different design pattern recovery approaches and examines the detection accuracy of these approaches. We found that the major impedance in the accurate recovery of design patterns is the large number of variations for implementing the same pattern. Furthermore, we realized that the combination of multiple searching techniques is required to improve accuracy of pattern detection. Based on these observations, we propose variable pattern definitions, which can be customized and improved towards a pattern catalog that detects patterns in all their variations. The customizable pattern definitions are created from reusable feature types. Each feature type can use one or more searching techniques for efficient detection. The proposed approach supports detection of patterns from multiple programming languages. A prototype implementation of the approach was tested on seven different open-source software projects. For each software project, a baseline was determined and the trustworthiness of each pattern–project combination was rated. The extracted results have been compared with established baselines and with the results of previous techniques.