代码的社会生活：通过代码嵌入与意见动态建模演变
Social Life of Code: Modeling Evolution through Code Embedding and Opinion Dynamics
AI总结：
本文通过代码嵌入与意见动态理论，构建量化框架分析协作开发过程，揭示开发者社区中的共识形成、影响传播及知识共享机制。
摘要：
软件仓库通过捕获拉取请求和修改等代码相关活动，提供了软件演化的详细记录。为更好地理解代码库演化的底层动态，我们提出一种新颖的方法，将语义代码嵌入与意见动态理论相结合，提供定量分析协作开发过程的框架。我们的方法首先使用最先进的代码嵌入模型将代码片段编码为高维向量表示，保留了语法和语义特征。这些嵌入随后通过主成分分析（PCA）进行降维，数据标准化以确保可比性。我们使用表达-私人意见（EPO）模型建模时间演化，推导信任矩阵并跟踪开发周期中的意见轨迹。这些意见轨迹反映了共识形成、影响传播以及开发者社区内演变的对齐（或偏离）的底层动态，揭示了隐含的协作模式和知识共享机制。通过将软件工程与计算社会科学相结合，我们的方法提供了一种量化软件演化的原理性方法，为开发者影响、共识形成和项目可持续性提供了新的见解。我们在三个著名的开源GitHub仓库数据上评估了我们的方法，证明了其揭示可解释的行为趋势和开发者互动变化的能力。结果突显了我们的框架在通过数据驱动的协作动态分析改进开源项目维护中的实用性。
英文摘要：
Software repositories provide a detailed record of software evolution by capturing developer interactions through code-related activities such as pull requests and modifications. To better understand the underlying dynamics of codebase evolution, we introduce a novel approach that integrates semantic code embeddings with opinion dynamics theory, offering a quantitative framework to analyze collaborative development processes. Our approach begins by encoding code snippets into high-dimensional vector representations using state-of-the-art code embedding models, preserving both syntactic and semantic features. These embeddings are then processed using Principal Component Analysis (PCA) for dimensionality reduction, with data normalized to ensure comparability. We model temporal evolution using the Expressed-Private Opinion (EPO) model to derive trust matrices and track opinion trajectories across development cycles. These opinion trajectories reflect the underlying dynamics of consensus formation, influence propagation, and evolving alignment (or divergence) within developer communities -- revealing implicit collaboration patterns and knowledge-sharing mechanisms that are otherwise difficult to observe. By bridging software engineering and computational social science, our method provides a principled way to quantify software evolution, offering new insights into developer influence, consensus formation, and project sustainability. We evaluate our approach on data from three prominent open-source GitHub repositories, demonstrating its ability to reveal interpretable behavioral trends and variations in developer interactions. The results highlight the utility of our framework in improving open-source project maintenance through data-driven analysis of collaboration dynamics.
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