This thesis presents two separate and complementary tools for understanding and analyzing design patterns. The first tool, the High-Level Pattern Representation (HiLPR), exposes the fundamental characteristics hidden within a design pattern's solution. This tool combines the information in parallel patterns' solutions and forces, and integrates information that is critical for pattern implementation. The second tool, the Dynamic Pattern Categorization (DPC), works between all of the patterns in an entire pattern language, and groups patterns of similar characteristics to support analysis and selection. Possible categories are presented and discussed, and further work can combine the exposure of characteristics from HiLPR into categorization by the DPC. The evaluation of these tools highlights a hidden weakness of current design pattern languages and practices. The conclusions raised by this work suggest that there are methods that will support pattern language construction. / Graduate