Keecle: Mining key architecturally relevant classes using dynamic analysis

Reconstructing architectural components from existing software applications is an important task during the software maintenance cycle because either those elements do not exist or are outdated. Reverse engineering techniques are used to reduce the effort demanded during the reconstruction. Unfortunately, there is no widely accepted technique to retrieve software components from source code. Moreover, in several architectural descriptions of systems, a set of architecturally relevant classes are used to represent the set of architectural components. Based on this fact, we propose Keecle, a novel dynamic analysis approach for the detection of such classes from execution traces in a semi-automatic manner. Several mechanisms are applied to reduce the size of traces, and finally the reduced set of key classes is identified using Naïve Bayes classification. We evaluated the approach with two open source systems, in order to assess if the encountered classes map to the actual architectural classes defined in the documentation of those respective systems. The results were analyzed in terms of precision and recall, and suggest that the proposed approach is effective for revealing key classes that conceptualize architectural components, outperforming a state-of-the-art approach.