发表机构
University of New Hampshire（新罕布什尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出词典引导的HDL修复系统，结合ANTLR生成的DUT专用变异词汇与仿真分歧故障定位模块，在CirFix基准上实现14个缺陷的正确修复，速度较CirFix提升18倍。
AI 中文摘要
自动化硬件描述语言（HDL）设计的修复仍面临挑战，原因在于候选修复方案的搜索空间庞大，且HDL语法施加了严格的语法与语义约束。通用变异策略会生成大量语法无效的候选，浪费编译与仿真预算；而基于综合及模板的方法则对通用性与可移植性施加了自身的约束。本文提出一种词典引导的HDL修复系统，将基于ANTLR生成的待测设计（DUT）专用变异词汇与仿真分歧故障定位（FL）模块相结合。变异算子通过基于正则表达式的匹配，直接对Verilog源代码应用类别约束的符号替换、插入与删除，无需抽象语法树（AST）操作或综合。FL模块从单次仿真运行中识别出分歧的输出线，并按与这些信号的结构接近度对源代码行评分，引导变异搜索指向高可疑区域。确定性目标遍历会对最高评分行穷尽所有词典变异，之后再回退至遗传编程（GP）搜索。在涵盖6个待测设计（DUT）系列的CirFix基准套件上评估，所提方法在14个缺陷变体上生成了通过预言机验证的正确修复，其中包括CirFix无法修复的含6处修改的多缺陷实例；在一处含2处修改的基准变体上，实现了比CirFix快18倍的速度。这些结果表明，词典约束的变异算子与轻量级仿真分歧FL相结合，是针对常见缺陷类别的实用且具竞争力的自动化HDL修复方法，无需形式化分析或综合依赖。
英文摘要
Automated repair of Hardware Description Language (HDL) designs remains challenging due to the large search space of candidate repairs and the strict syntactic and semantic constraints imposed by HDL grammars. Generic mutation strategies overwhelmingly generate syntactically invalid candidates that waste compilation and simulation budget, while synthesis-driven and template-based approaches impose their own constraints on generality and portability. In this paper, we propose a dictionary-guided HDL repair system that combines ANTLR-derived DUT-specific mutation vocabularies with a simulation-divergence fault localization (FL) module. The mutation operator applies category-constrained token substitutions, insertions, and deletions directly to Verilog source via regex-based matching, without requiring AST manipulation or synthesis. The FL module identifies diverging output wires from a single simulation run and scores source lines by structural proximity to those signals, directing the mutation search toward high-suspicion regions. A deterministic targeted sweep exhausts all dictionary mutations on the highest-scored lines before falling back to a genetic programming (GP) search. Evaluated on the CirFix benchmark suite across six design under test (DUT) families, the proposed approach produces correct oracle-passing repairs on 14 bug variants, including a 6-edit multi-bug instance that CirFix cannot repair, and achieves an 18x speedup over CirFix on a two-edit benchmark variant. These results indicate that dictionary-constrained mutation operators, combined with lightweight simulation-divergence FL, are a practical and competitive approach to automated HDL repair for common bug classes without formal analysis or synthesis dependencies.
