Software development is a process fraught with unpredictability, in part because software is created by people. Human interactions add complexity to development processes, and collaborative development can become a liability if not properly understood and managed. Recent years have seen an increase in the use of data mining techniques on publicly-available repository data with the goal of improving software development processes, and by extension, software quality. In this thesis, we introduce the concept of author entropy as a metric for quantifying interaction and collaboration (both within individual files and across projects), present results from two empirical observational studies of open-source projects, identify and analyze authorship and collaboration patterns within source code, demonstrate techniques for visualizing authorship patterns, and propose avenues for further research.