From: Researchers release SWE-chat dataset of AI coding interactions
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Vishakh Padmakumar@vishakh_pk·Quote tweet

Really excited to have this dataset released to the community! There's a gap in our understanding of how users interact with coding agents at scale. SWE-chat fills that need to help shape the next generation of human-centered evals and training objectives for coding agents! 🤖🚀

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Joachim Baumann @ ICLR'26@joabaum·View on

We present SWE-chat: the first large-scale dataset of coding agent interactions from real users in the wild. In 40% of real coding sessions, the agent writes ~all the code. Users push back 39% of the time – agents almost never stop to check. Data, paper, & findings in the 🧵👇

Overview of SWE-chat. Left: a data collection pipeline diagram. Open-source developers install the Entire.io CLI tool, which logs their coding agent sessions and pushes the logs to a dedicated branch on their public GitHub repository. We discover and aggregate these logs into the SWE-chat dataset, with line-level attribution of which lines of code were written by the human versus the agent. Right: a growth chart showing cumulative logged events over time, rising steeply through early 2026. As of April 2026, the dataset contains 2.7 million logged events from over 200 repositories, including 63,000+ user prompts and 355,000+ agent tool calls across nearly 6,000 sessions.
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Researchers release SWE-chat dataset of AI coding interactions · KRO · Digg