  Context: GitHub hosts an impressive number of high-quality OSS projects.
However, selecting "the right tool for the job" is a challenging task, because
we do not have precise information about those high-quality projects.
Objective: In this paper, we propose a data-driven approach to measure the
level of maintenance activity of GitHub projects. Our goal is to alert users
about the risks of using unmaintained projects and possibly motivate other
developers to assume the maintenance of such projects. Method: We train machine
learning models to define a metric to express the level of maintenance activity
of GitHub projects. Next, we analyze the historical evolution of 2,927 active
projects in the time frame of one year. Results: From 2,927 active projects,
16% become unmaintained in the interval of one year. We also found that
Objective-C projects tend to have lower maintenance activity than projects
implemented in other languages. Finally, software tools---such as compilers and
editors---have the highest maintenance activity over time. Conclusions: A
metric about the level of maintenance activity of GitHub projects can help
developers to select open source projects.
