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 toolssuch 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.

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