Code anomalies can signal software architecture degradation. However, the identification of architecturally-relevant code anomalies (i.e. code anomalies that strongly imply architectural deficiencies) is particularly challenging due to: (i) lack of understanding about the relationship between code anomalies and architectural degradation, (ii) the focus on source code anomaly detection without considering how it relates to the software architecture, and (iii) lack of knowledge about how reliable these detection techniques are when revealing architecturally-relevant code anomalies. This thesis presents techniques for identifying architecturally-relevant code anomalies. Architecture-sensitive metrics and detection strategies were defined to overcome the limitations of conventional detection strategies. These metrics and strategies leverage traces that can be established between architectural views and system implementation. The thesis also documents code anomaly patterns (i.e. recurring anomaly relationships) that are strongly related to architectural problems. A tool, called SCOOP, was developed to collect the architecture-sensitive metrics, apply the new detection strategies, and identify the documented code anomaly patterns. Using this tool, we evaluated our technique in a series of empirical studies, comparing its accuracy with that of conventional detection techniques when identifying architecturally-relevant code anomalies.