RECAP: An End-to-End Platform for Capturing, Replaying, and Analyzing AI-Assisted Programming Interactions
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 1 May 2026.
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14 of 28 references.
References · 14
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2022CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilitiescited by 249
- 2022Grounded Copilot: How Programmers Interact with Code-Generating Modelscited by 218
- 2022Reading Between the Lines: Modeling User Behavior and Costs in AI-Assisted Programmingcited by 91
- 2024CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needscited by 84
- 2026SWE-chat: Coding Agent Interactions From Real Users in the Wildcited by 43
- 2023StudentEval: A Benchmark of Student-Written Prompts for Large Language Models of Codecited by 32
- 2024The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmerscited by 30
- 2025Code with Me or for Me? How Increasing AI Automation Transforms Developer Workflowscited by 28
- 2025Copilot Arena: A Platform for Code LLM Evaluation in the Wildcited by 26
- 2025Not Everyone Wins with LLMs: Behavioral Patterns and Pedagogical Implications for AI Literacy in Programmatic Data Sciencecited by 6
- 2026Evolving with AI: A Longitudinal Analysis of Developer Logscited by 4
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Citing papers per year: 2026: 1.
1 paper.