Mining Architectural Patterns Using Association Rules

Software systems usually follow many programming rules prescribed in an architectural model. However, developers frequently violate these rules, introducing architectural drifts in the source code. In this paper, we present a data mining approach for architecture conformance based on a combination of static and historical software analysis. For this purpose, the proposed approach relies on data mining techniques to extract structural and historical architectural patterns. In addition, we propose a methodology that uses the extracted patterns to detect both absences and divergences in source-code based architectures. We applied the proposed approach in an industrial-strength system. As a result we detected 137 architectural violations, with an overall precision of 41.02%. Keywords-Software architecture conformance; Frequent itemset mining; Static analysis; Mining software repositories