一句话：提出 RepFix，结合追踪细粒度定位与历史补丁生成，自动修复不可复现构建问题。
软件可复现性通过比较不同用户获得的编译产出来建立源代码与构建产物之间的信任。尽管不可复现构建的检测可自动化，但修复此类问题仍面临挑战，其中定位粒度与历史知识利用尤为关键。为此，论文提出 RepFix：它将基于追踪的细粒度定位与基于历史知识的补丁生成机制相结合，用以自动修复导致构建不可复现的问题。该方法先利用追踪信息精确定位相关构建步骤或代码，再借助历史修复知识生成补丁。摘要未报告具体实验数据；其核心贡献在于为不可复现构建提供自动修补流程，缓解定位粒度不足与历史经验难以复用的问题。
Software reproducibility plays an essential role in establishing trust between source code and the built artifacts, by comparing compilation outputs acquired from independent users. Although the testing for unreproducible builds could be automated, fixing unreproducible build issues poses a set of challenges within the reproducible builds practice, among which we consider the localization granularity and the historical knowledge utilization as the most significant ones. To tackle these challenges, we propose a novel approach RepFix that combines tracing-based fine-grained localization with history-based patch generation mechanisms.