We apply machine learning to version control data to measure software development productivity. Our models measure both the quantity and quality of produced code. Quantity is defined by a model that predicts the labor hours supplied by the `standard coder’ to make any code change, and quality is defined by a model that predicts the distribution of different kinds of problems identified by a static code analysis tool.