Prioritizing software anomalies with software metrics and architecture blueprints

According to recent studies, architecture degradation is to a large extent a consequence of the introduction of code anomalies as the system evolves. Many approaches have been proposed for detecting code anomalies, but none of them has been efficient on prioritizing code anomalies that represent real problems in the architecture design. In this sense, our work aims to investigate whether the prioritization of instances of three types of classical code anomalies, Divergent Change, God Class and Shotgun Surgery, can be improved when supported by architecture blueprints. These blueprints are informal models often available in software projects, and they are used to capture key architecture decisions. Moreover, we are also investigating what information may be useful in the design blueprints to help developers on prioritizing the most critical software anomalies. In many cases, developers indicated that it would be interesting the insertion of additional information on the blueprints in order to detect architecturally-relevant anomalies.