An Empirical Study of Proactive Coding Assistants in Real-World Software Development
Large language model (LLM)-based coding assistants have made substantial progress, yet most systems remain reactive, requiring developers to explicitly formulate their needs. Proactive coding assistants aim to infer latent developer intent from integrated development environment (IDE) interactions and repository context, thereby reducing interaction overhead and supporting more seamless assistance. However, research in this direction is limited by the scarcity of large-scale real-world developer behavior data. Existing studies therefore often rely on LLM-simulated IDE traces, whose fidelity to real development behavior remains unclear. In this paper, we investigate this simulation-to-real...
| Authors | Lehui Li, Ruixuan Jia, Guo-Ye Yang et al. |
|---|---|
| Primary category | cs.SE |
| Published | 7 May 2026 |
| Last updated at source | 7 May 2026 |
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