This presentation was part of the Second International Workshop on Software Architecture Metrics, held at the 37th International Conference on Software Engineering.
We can evaluate software architecture quality using a plethora of metrics proposed in the literature, but interpreting and exploiting these metrics in the right way are not always simple tasks. This is true for both fixing the right metric threshold values and determining the actions to be taken to improve the quality of a system. Instead of metrics, we can detect code or architectural anomalies that give us useful hints about the possible architecture degradation. In this presentation, 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.