Enhancing architectural recovery using concerns

Architectures of implemented software systems tend to drift and erode as they are maintained and evolved. To properly understand such systems, their architectures must be recovered from implementation-level artifacts. Many techniques for architectural recovery have been proposed, but their degrees of automation and accuracy remain unsatisfactory. To alleviate these shortcomings, we present a machine learning-based technique for recovering an architectural view containing a system´s components and connectors. Our approach differs from other architectural recovery work in that we rely on recovered software concerns to help identify components and connectors. A concern is a software system´s role, responsibility, concept, or purpose. We posit that, by recovering concerns, we can improve the correctness of recovered components, increase the automation of connector recovery, and provide more comprehensible representations of architectures.