发表机构
University of Houston（休斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RepairFormer是基于Transformer的结构化输入修复框架，通过格式标签、预言机验证等技术提升修复与内容恢复率，运行速度较现有最优方法快5倍，可高效修复JSON等结构化输入。
AI 中文摘要
JSON、DOT、OBJ、INI、S-表达式及TinyC等结构化输入文件在软件系统中被广泛使用，但微小的损坏就可能导致解析器拒绝原本有用的数据。修复这类输入十分重要，因为畸形的配置、程序及数据文件即便保留了大部分原始内容，也会中断测试、分析、部署及下游自动化流程。现有修复技术虽能生成结构合法的输入，但常依赖删除操作或重复搜索，可能丢失原始内容并导致语义错误。本文提出RepairFormer，一种基于Transformer的结构化输入修复框架。该方法将修复任务建模为监督序列生成任务，利用格式标签、预言机验证及边界定位修复来生成合法输出，同时保留原始内容。边界工作流将生成过程聚焦于检测到的故障区域，减小输入规模，支持更长文件的修复。评估显示，RepairFormer的修复准确率达88%，内容恢复率达94%，在修复成功时展现出最强的内容保留能力。在基准测试的额外实验中，RepairFormer的修复率达97.57%，内容恢复率达94.29%，运行速度较现有最优方法快5倍。
英文摘要
Structured input files such as JSON, DOT, OBJ, INI, S-expression, and TinyC are widely used in software systems, but small corruptions can cause parsers to reject otherwise useful data. Repairing such inputs is important because malformed configuration, program, and data files can interrupt testing, analysis, deployment, and downstream automation even when most of the original content remains intact. Existing repair techniques can produce structurally valid inputs, but they often rely on deletion or repeated search, which may lose original content and result in semantic incorrectness. This paper presents RepairFormer, a transformer-based framework for structured input repair. The approach formulates repair as a supervised sequence generation task and uses format tags, oracle validation, and boundary-localized repair to generate valid outputs while preserving content. The boundary workflow focuses generation on the detected fault region, reducing the input size, and supporting repair of longer files. In evaluation, RepairFormer achieves a 88% in repair and 94% in recovery, showing strongest content preservation when repairs are successful. Additional experiments on our benchmark shows RepairFormer repairs 97.57% and recovers 94.29% with 5x faster runtime compared to state of the art.
Comments5 pages, 2 figures, and 3 tables
Journal refSPLASH Companion '26: Companion Proceedings of the 2026 ACM SIGPLAN International Conference on Systems, Programming, Languages, and Applications: Software for Humanity
