一句话：针对自动提交信息生成，指出现有方法受粗粒度代码变更表示限制，并提出 FIRA。
提交信息用自然语言总结每个提交的代码变更，帮助开发者无需深入实现细节即可理解变更，对理解软件演化至关重要。为减轻人工编写负担，已有研究提出基于模板、信息检索和学习的自动化生成技术。然而，这些方法常因代码变更表示过于粗粒度而效果有限。标题为 FIRA 的本文工作（从摘要可推断）针对这一瓶颈，尝试以更细粒度的代码变更表示改进提交信息生成。给出的摘要未说明具体模型、训练数据、基线对比或评价指标，因此无法确认其技术细节与量化结果；其核心问题与动机可概括为：粗粒度表示限制了自动提交信息生成的有效性。
Commit messages summarize code changes of each commit in natural language, which help developers understand code changes without digging into detailed implementations and play an essential role in comprehending software evolution. To alleviate human efforts in writing commit messages, researchers have proposed various automated techniques to generate commit messages, including template-based, information retrieval-based, and learning-based techniques. Although promising, previous techniques have limited effectiveness due to their coarse-grained code change representations.