SWE-chat: Coding Agent Interactions From Real Users in the Wild
📰 ArXiv cs.AI
Learn how to analyze and apply SWE-chat, a large-scale dataset of real coding agent interactions, to improve AI coding agents' usefulness in practice
Action Steps
- Collect and preprocess the SWE-chat dataset using Python and relevant libraries
- Apply data analysis techniques to identify patterns and trends in user prompts and agent tool calls
- Use the insights gained to fine-tune and improve the performance of AI coding agents
- Evaluate the effectiveness of the improved agents using metrics such as accuracy and user satisfaction
- Integrate the improved agents into real-world development workflows to enhance productivity and efficiency
Who Needs to Know This
AI engineers, data scientists, and software engineers can benefit from this dataset to improve the performance and usability of AI coding agents
Key Insight
💡 Analyzing real-world interactions with AI coding agents can significantly improve their usefulness in practice
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🤖💻 SWE-chat dataset reveals how users interact with AI coding agents in the wild! 🌐
Key Takeaways
Learn how to analyze and apply SWE-chat, a large-scale dataset of real coding agent interactions, to improve AI coding agents' usefulness in practice
Full Article
Title: SWE-chat: Coding Agent Interactions From Real Users in the Wild
Abstract:
arXiv:2604.20779v1 Announce Type: new Abstract: AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful in practice. We present SWE-chat, the first large-scale dataset of real coding agent sessions collected from open-source developers in the wild. The dataset currently contains 6,000 sessions, comprising more than 63,000 user prompts and 355,000 agent tool calls. SWE-chat is a living dataset; our collecti
Abstract:
arXiv:2604.20779v1 Announce Type: new Abstract: AI coding agents are being adopted at scale, yet we lack empirical evidence on how people actually use them and how much of their output is useful in practice. We present SWE-chat, the first large-scale dataset of real coding agent sessions collected from open-source developers in the wild. The dataset currently contains 6,000 sessions, comprising more than 63,000 user prompts and 355,000 agent tool calls. SWE-chat is a living dataset; our collecti
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