  Modern software development is based on a series of rapid incremental changes
collaboratively made to large source code repositories by developers with
varying experience and expertise levels. The ZeroIn project is aimed at
analyzing the metadata of these dynamic phenomena, including the data on
repositories, commits, and developers, to rapidly and accurately mark the
quality of commits as they arrive at the repositories. In this context, the
present article presents a characterization of the software development
metadata in terms of distributions of data that best captures the trends in the
datasets. Multiple datasets are analyzed for this purpose, including Stack
Overflow on developers' features and GitHub data on over 452 million
repositories with 16 million commits. This characterization is intended to make
it possible to generate multiple synthetic datasets that can be used in
training and testing novel machine learning-based solutions to improve the
reliability of software even as it evolves. It is also aimed at serving the
development process to exploit the latent correlations among many key feature
vectors across the aggregate space of repositories and developers. The data
characterization of this article is designed to feed into the machine learning
components of ZeroIn, including the application of binary classifiers for early
flagging of buggy software commits and the development of graph-based learning
methods to exploit sparse connectivity among the sets of repositories, commits,
and developers.
