I will use Natural Language Processing and Machine Learning techniques to analyze issue backlogs in applications from institutions throughout the community. I will apply a variety of techniques in an attempt to answer questions like:
What kinds of open issues do we have in general as a community?
- Can I extract an interesting set of widely-desired features or widely-held use cases?
- Can I identify connections that might lead to collaboration across institutions?
What solutions already exist that might advance open issues?
- Can I link open issues in one backlog to merged PRs in another repository?
What have people been working on recently?
- Can we characterize the full set of issues that have been closed over the past year?
What patterns of development do repositories follow?
- Can we describe the life cycle of repository development by aligning issues based on their creation / completion dates relative to the initial commit?
These may or may not be the exact questions my talk will address, depending on the direction the project naturally takes. I will focus on applications in use or under development at institutions, as opposed to community-maintained engines and core gems. This talk will describe my process, results, and evaluate the success of the endeavor.
Of particular interest to: Developers, Managers, Metadata people
