  Many software developers rely on open source software for developing their
applications and writing their source codes. Measuring an independent project's
overall productivity is still an open problem for many technology companies. In
this project, we address to bridge the gap of analyzing which are the most
important features for prediction of a productivity based system. We have
chosen to collect data from GitHub via their application programming interfaces
(API) and analyze the data we gathered to understand the relation between the
average time to close an issue and the features that we collected. Since most
of the data we gathered were not Gaussian, we had to preprocess the data using
outlier detection and applying transformations before statistical modeling. The
best model we observed was polynomial regression with degree 5. Overall, we
noticed that there are many aspects of software development that make
developers increase their productivity.
