RepairFormer: Transformer-Based Framework for Structured Input Repair
A recent study presents RepairFormer, a transformer-based system aimed at the automatic correction of corrupted structured input files, including JSON, DOT, OBJ, INI, S-expression, and TinyC. This research, published on arXiv (2608.05060), tackles the issue of improperly formatted configuration, program, and data files that can hinder software testing, analysis, deployment, and automation processes. Traditional repair methods often involve deletion or repetitive searching, risking the loss of original content and leading to semantic errors. RepairFormer approaches the repair process as a supervised sequence generation task, utilizing format tags, oracle validation, and localized repair to ensure valid outputs while maintaining content integrity. The boundary-focused workflow enhances both efficiency and accuracy, marking a significant advancement in software reliability and automation through the innovative use of transformers.
Key facts
- RepairFormer is a transformer-based framework for structured input repair.
- It handles file formats including JSON, DOT, OBJ, INI, S-expression, and TinyC.
- The paper is available on arXiv with ID 2608.05060.
- Existing repair techniques often lose original content or result in semantic incorrectness.
- RepairFormer uses supervised sequence generation, format tags, oracle validation, and boundary-localized repair.
- The boundary workflow focuses on the detected fault region.
- The goal is to generate valid outputs while preserving content.
- The research addresses interruptions in testing, analysis, deployment, and automation.
Entities
Institutions
- arXiv