Mining architectural violations from version history

Abstract

Software architecture conformance is a key software quality control activity that aims to reveal the progressive gap normally observed between concrete and planned software architectures. However, formally specifying software architectures is not a trivial task, as it must be done by an expert on the system under analysis. In this thesis, we present an approach for architecture conformance based on a combination of static and historical source code analysis. The proposed approach relies on four heuristics for detecting both absences (something expected was not found) and divergences (something prohibited was found) in source code based architectures. We also present an architecture conformance process based on the proposed approach. We followed this process to evaluate the architecture of two industrial-strength information systems, when 539 architectural violations were detected, with an overall precision of 62.7% and 53.8%. We also evaluated our approach in two open-source systems, when 345 architectural violations were detected, achieving an overall precision of 53.3% and 59.2%. Additionally, this thesis presents an exploratory study on the application of a data mining technique called frequent itemset mining, which was used to detect architectural patterns using static and historical information extracted from source code. Furthermore, the detected architectural patterns are used to identify absences and divergences in the code. We evaluated the proposed approach in an industrialstrength information system, founding 137 architectural violations, with an overall precision of 41.2%.