Synthesizing Program Input Grammars
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Abstract (arXiv)
We present an algorithm for synthesizing a context-free grammar encoding the language of valid program inputs from a set of input examples and blackbox access to the program. Our algorithm addresses shortcomings of existing grammar inference algorithms, which both severely overgeneralize and are prohibitively slow. Our implementation, GLADE, leverages the grammar synthesized by our algorithm to fuzz test programs with structured inputs. We show that GLADE substantially increases the incremental coverage on valid inputs compared to two baseline fuzzers.
Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). First posted on arXiv 5 Aug 2016.
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Citing papers per year: 2024: 1; 2025: 1; 2026: 1.
3 papers.
- 2024Enhancing LLM Agents for Code Generation with Possibility and Pass-rate Prioritized Experience Replaycited by 3
- 2025Example-Free Learning of Regular Languages with Prefix Queriescited by 0
- 2026Learning Context-Free Grammars for Grammar-Constrained Decoding via Declarative Agentic Programming with Guaranteescited by 0
- 2026Learning Context-Free Grammars for Grammar-Constrained Decoding via Declarative Agentic Programming with Guarantees6 Aug 2026
- 2025Example-Free Learning of Regular Languages with Prefix Queries2 Apr 2025
- 2024Enhancing LLM Agents for Code Generation with Possibility and Pass-rate Prioritized Experience Replay16 Oct 2024