@inproceedings{he-etal-2026-recap,
    title = "{RECAP}: An End-to-End Platform for Capturing, Replaying, and Analyzing {AI}-Assisted Programming Interactions",
    author = "He, Keyu  and
      Ma, Qianou  and
      Chen, Valerie  and
      Chi, Wayne  and
      Wu, Tongshuang",
    editor = "Durrett, Greg  and
      Jian, Ping",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-demo.68/",
    pages = "692--701",
    ISBN = "979-8-89176-392-0",
    abstract = "Understanding how developers interact with AI coding assistants requires more than chat logs or git histories in isolation; it requires reconstructing the full context: which prompt led to which edit, what the developer tried and discarded, and how their strategy evolved over time. We present RECAP (Replay and Examine Captured AI Programming), an open-source platform that (1) passively records AI chat sessions and fine-grained code edits inside VS Code without disrupting the developer{'}s workflow, (2) merges them into a unified timeline for interactive session replay, and (3) exposes an extensible analysis layer, with example modules for behavioral classification and AI reliance measurement. Deployed in a university software engineering course, RECAP captured 2,034 prompts and 8,239 code edits from 41 students across a multi-week project. We demonstrate how the platform{'}s linked data and replay capabilities enable analyses of developer-AI interaction patterns that no single data source could support."
}