Software systems are often evolving due to many changing requirements. As the software evolves, it grows in size and complexity, and consequently, its architecture design tends to degrade. Architecture degradation symptoms are often a direct consequence of the progressive insertion of code anomalies in the software implementation. A code anomaly is a recurring implementation structure that possibly indicates deeper architectural design problems. Code anomaly is considered critical when it is related with a structural problem in the software architecture. Its criticality stems from its negative influence on a wide range of non-functional requirements. For instance, the presence of critical code anomalies hinders software aintainability, i.e. these critical anomalies require wide refactoring in order to remove an architectural problem. Symptoms of architecture degradation have often to be observed in the source code due to the lack of an explicit, formal representation of the software architecture in a project. Many approaches are proposed for detecting code anomalies in software systems, but none of them efficiently support the prioritization and ranking of critical code anomalies according to their architecture impact. Our work investigates how the prioritization and ranking of such critical code anomalies could be improved by using blueprints. Architecture blueprints are usually provided by software architects since the early stages of the system development. Blueprints are informal design models usually defined to capture and communicate key architectural design decisions. Even though blueprints are often incomplete and inconsistent with respect to the underlying implementation, we aim to study if their use can contribute to improve the processes of prioritizing and ranking critical code anomalies. Aiming to address these research goals, a set of empirical studies has been performed. We also proposed and evaluated a set ofheuristics to support developers when prioritizing and ranking code anomalies in 3 software systems. The results showed an average accuracy higher than 60 percent when prioritizing and ranking code anomalies associated with architectural problems in these systems.