CVS Data Extraction and Analysis: A Case Study
Version control repositories contain a wealth of detailed information about the evolution of a codebase. In this paper, we outline our experiences parsing and analyzing data from a large collection of CVS repositories created by many students working on a small set of assignments in a second year undergraduate computer science course. We believe the data to be quite unique because rather than a single team working on a single large project we have many individuals working separatly on identical projects. We describe a system for parsing repositories into a SQL database. From the database and repositories we extract various statistical measures of the code and version histories. Finally, we attempt to correlate the code and repository features with grades. Despite using 166 features derived from the code and repository histories, we nd that grade performance cannot be accurately predicted; certainly no predictors stronger than simple lines-of-code were found.