一句话：提出利用程序动态行为从期望行为变更预测软件变更，以弥补LLM难以理解代码语义的不足。
近年来，机器学习特别是大语言模型被广泛用于软件工程任务，但绝大多数工作聚焦于程序代码的 token 序列预测，程序动态特性几乎未被利用；LLM 在解释和预测代码语义方面仍面临挑战。该研究提出从期望的行为变更出发预测软件变更，试图借助程序动态行为来改进对代码语义的理解与变更预测。摘要主要阐述研究动机与方向，未提供具体方法、实现细节或实验评估结果。
In the past years, we have seen an explosion of Machine Learning techniques applied to software engineering tasks [1, 10, 12]. The vast majority of these approaches is applied to program code, using Large Language Models (LLMs) to predict token sequences in specific contexts [11]. However, the dynamic nature of programs is hardly exploited; on the contrary, interpreting and predicting the semantics of code remains challenging for LLMs.