Sub-graph Mining: Identifying Micro-architectures in Evolving Object-Oriented Software

Developers introduce novel and undocumented micro-architectures when performing evolution tasks on object-oriented applications. We are interested in understanding whether those organizations of classes and relations can bear, much like cataloged design and anti-patterns, potential harm or benefit to an object-oriented application. We present SGFinder, a sub-graph mining approach and tool based on an efficient enumeration technique to identify recurring micro-architectures in object-oriented class diagrams. Once SGFinder has detected instances of micro-architectures, we exploit these instances to identify their desirable properties, such as stability, or unwanted properties, such as change or fault proneness. We perform a feasibility study of our approach by applying SGFinder on the reverse-engineered class diagrams of several releases of two Java applications: ArgoUML and Rhino. We characterize and highlight some of the most interesting micro-architectures, e.g., the most fault prone and the most stable, and conclude that SGFinder opens the way to further interesting studies.