Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs. Aims: We apply an SE-oriented classification to PTMs and datasets on a popular open-source ML repository, Hugging Face (HF), and analyze the evolution of PTMs over time. Method: We conducted a repository mining study. We started with a systematically gathered database of PTMs and datasets from the HF API. Our selection was refined by analyzing model and dataset cards and metadata, such as tags, and confirming SE relevance using Gemini 1.5 Pro. All analyses are replicable, with a publicly accessible replication package. Results: The most common SE task among PTMs and datasets is code generation, with a primary focus on software development and limited attention to software management. Popular PTMs and datasets mainly target software development. Among ML tasks, text generation is the most common in SE PTMs and datasets. There has been a marked increase in PTMs for SE since 2023 Q2. Conclusions: This study underscores the need for broader task coverage to enhance the integration of ML within SE practices.

The ongoing dialogue about whether or not artificial intelligence will take away jobs and replace people has been going on for a while now. But what a lot of people are ignoring is the fact that robots will never be able to replace humans. They might replace tasks that are easily automated or even jobs that are basically task-oriented or repetitive. However, they will never be able to replace jobs where the personal element is key. Let me give you an example. Nursing is a thankless job that requires tireless effort, standing and walking around all day, dealing with cranky patients, doing crazy shifts, being ordered around by doctors and specialists… if you're a nurse you'll know what I mean.