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The optimal number of latent topics required to model the most accurate latent substructure for a source code corpus is an open question in source code analysis. Most estimates about the number of latent topics that exist in a software corpus are based on the assumption that the data is similar to natural language, but there is little empirical evidence to support this. In order to help determine the appropriate number of topics needed to accurately represent the source code, we generate a series of Latent Dirichlet Allocation models with varying topic counts. We use a heuristic to evaluate the ability of the model to identify related source code blocks, and demonstrate the consequences of choosing too few or too many latent topics.