Automatic Syntax Error Repair for Discrete Controller Synthesis using Large Language Model
Yusei Ishimizu, Takuto Yamauchi, Sinan Chen, Jinyu Cai, Jialong Li, Kenji Tei

TL;DR
This paper introduces an automated method using Large Language Models to repair syntax errors in Discrete Controller Synthesis models, significantly improving repair accuracy and developer productivity.
Contribution
It presents a knowledge-informed prompting strategy for LLMs, tailored to DCS syntax errors, and constructs a benchmark for evaluation.
Findings
High repair accuracy achieved
3.46 times speedup over human developers
Effective handling of realistic syntax errors
Abstract
Discrete Controller Synthesis (DCS) is a powerful formal method for automatically generating specifications of discrete event systems. However, its practical adoption is often hindered by the highly specialized nature of formal models written in languages such as FSP and FLTL. In practice, syntax errors in modeling frequently become an important bottleneck for developers-not only disrupting the workflow and reducing productivity, but also diverting attention from higher-level semantic design. To this end, this paper presents an automated approach that leverages Large Language Models (LLMs) to repair syntax errors in DCS models using a well-designed, knowledge-informed prompting strategy. Specifically, the prompting is derived from a systematic empirical study of common error patterns, identified through expert interviews and student workshops. It equips the LLM with DCS-specific domain…
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Taxonomy
TopicsPetri Nets in System Modeling · Formal Methods in Verification · Business Process Modeling and Analysis
