Towards Assessing Software Architecture Quality by Exploiting Code Smell Relations

Abstract

We can evaluate software architecture quality using a plethora of metrics proposed in the literature, but interpreting and exploiting in the right way these metrics is not always a simple task. This is true for both fixing the right metric threshold values and determining the actions to be taken to improve the quality of the system. Instead of metrics, we can detect code or architectural anomalies that give us useful hints on the possible architecture degradation. In this paper, we focus our attention on the detection of code smells and in particular on their relations and co-occurrences, with the aim to evaluate technical debt in an architectural context. We start from the assumption that certain patterns of code anomalies tend to be better indicators of architectural degradation than simple metrics evaluation.