Large Language Models (LLMs) have gained significant attention in the software engineering community. Nowadays developers have the possibility to exploit these models through industrial-grade tools providing a handy interface toward LLMs, such as OpenAI's ChatGPT. While the potential of LLMs in assisting developers across several tasks has been documented in the literature, there is a lack of empirical evidence mapping the actual usage of LLMs in software projects. In this work, we aim at filling such a gap. First, we mine 1,501 commits, pull requests (PRs), and issues from open-source projects by matching regular expressions likely to indicate the usage of ChatGPT to accomplish the task. Then, we manually analyze these instances, discarding false positives (i.e., instances in which ChatGPT was mentioned but not actually used) and categorizing the task automated in the 467 true positive instances (165 commits, 159 PRs, 143 issues). This resulted in a taxonomy of 45 tasks which developers automate via ChatGPT. The taxonomy, accompanied with representative examples, provides (i) developers with valuable insights on how to exploit LLMs in their workflow and (ii) researchers with a clear overview of tasks that, according to developers, could benefit from automated solutions.

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