With the advent of large language models, research in automated software engineering has increasingly focused on leveraging these models to achieve a deeper semantic understanding of code or to engineer sophisticated agent-based processes. However, this research trajectory has largely overlooked a critical factor: the developers themselves. Programming is a deeply individualized activity; developers exhibit significant variation in their tool-chain preferences, domain-specific expertise, and problem-solving strategies. Consequently, the current paradigm of one-size-fits-all code intelligence systems struggles to accommodate the needs of individual developers. To address this gap, we introduce VirtualME, a novel IDE-embedded data infrastructure designed to model the developer by continuously capturing and interpreting their dynamic programming behaviors and preferences. VirtualME contains three components. (1) Log-level Behavior Extraction: it captures and extracts developers' log-level behaviors from IDE. (2) Task-level Behavior Recognition: it aggregates log-level behaviors into task-level behaviors via a multi-agent pipeline. (3) Developer-personality Measurement: it builds a rule engine to distill a four-dimensional developer persona: technology stack, ability, behavioral habits, and learning style. On top of VirtualME, we propose a solution for personalized repository-level knowledge Q&A by integrating the developer persona into the Q&A agent. We evaluated VirtualME by building a multi-repository benchmark with real-world developer trajectories, balancing correctness and personalization. Experimental results show that VirtualME-enhanced answers outperform generic baselines on five dimensions, yielding an average 33.80% improvement. Our results demonstrate that abundant, continuous developer-behavior data can pave the new way for adaptive and personalized code intelligence.

翻译：随着大语言模型的出现，自动化软件工程领域的研究愈加侧重于利用这些模型实现更深层次的代码语义理解，或构建基于智能体的复杂流程。然而，这一研究路径在很大程度上忽略了一个关键因素：开发者本身。编程是一项高度个性化的活动；开发者在工具链偏好、领域特定专长以及问题解决策略上存在显著差异。因此，当前“一刀切”式的代码智能系统范式难以满足个体开发者的需求。为弥补这一不足，我们提出了VirtualME，一种嵌入IDE的新型数据基础设施，旨在通过持续捕获并解释开发者的动态编程行为与偏好来对其建模。VirtualME包含三个组件：(1) 日志级行为提取：从IDE中捕获并提取开发者的日志级行为。(2) 任务级行为识别：通过多智能体流水线将日志级行为聚合为任务级行为。(3) 开发者个性度量：构建规则引擎，提炼出四维开发者画像：技术栈、能力、行为习惯与学习风格。在VirtualME之上，我们提出了一种个性化仓库级知识问答解决方案，通过将开发者画像集成到问答智能体中。我们通过构建包含真实开发者轨迹的多仓库基准来评估VirtualME，平衡了正确性与个性化。实验结果表明，VirtualME增强后的答案在五个维度上优于通用基线，平均提升33.80%。我们的结果证明，丰富且持续的开发者行为数据可为自适应与个性化代码智能开辟新路径。