Synthesizing Formal Semantics from Executable Interpreters

Program verification and synthesis frameworks that allow one to customize the language in which one is interested typically require the user to provide a formally defined semantics for the language. Because writing a formal semantics can be a daunting and error-prone task, this requirement stands in the way of such frameworks being adopted by non-expert users. We present an algorithm that can automatically synthesize inductively defined syntax-directed semantics when given ( i ) a grammar describing the syntax of a language and ( ii ) an executable (closed-box) interpreter for computing the semantics of programs in the language of the grammar. Our algorithm synthesizes the semantics in the form of Constrained-Horn Clauses (CHCs), a natural, extensible, and formal logical framework for specifying inductively defined relations that has recently received widespread adoption in program verification and synthesis. The key innovation of our synthesis algorithm is a Counterexample-Guided Synthesis (CEGIS) approach that breaks the hard problem of synthesizing a set of constrained Horn clauses into small, tractable expression-synthesis problems that can be dispatched to existing SyGuS synthesizers. Our tool SynAntic synthesized inductively-defined formal semantics from 14 interpreters for languages used in program-synthesis applications. When synthesizing formal semantics for one of our benchmarks, Synantic unveiled an inconsistency in the semantics computed by the interpreter for a language of regular expressions; fixing the inconsistency resulted in a more efficient semantics and, for some cases, in a 1.2 x speedup for a synthesizer solving synthesis problems over such a language.

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Publication Details

Journal
Proceedings of the ACM on Programming Languages
Published
2024-10-08
DOI
https://doi.org/10.1145/3689724
Primary Topic
Natural Language Processing Techniques
Type
article
Field-Weighted Citation Impact
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article

Synthesizing Formal Semantics from Executable Interpreters

Charlie Murphy, Keith J. C. Johnson, Jiangyi Liu, Anvay Grover et al.
Proceedings of the ACM on Programming Languages
Natural Language Processing Techniques
article

Synthesizing Formal Semantics from Executable Interpreters

Charlie Murphy, Keith J. C. Johnson, Jiangyi Liu, Anvay Grover, Thomas Reps, Loris D’Antoni
article en

Abstract

Program verification and synthesis frameworks that allow one to customize the language in which one is interested typically require the user to provide a formally defined semantics for the language. Because writing a formal semantics can be a daunting and error-prone task, this requirement stands in the way of such frameworks being adopted by non-expert users. We present an algorithm that can automatically synthesize inductively defined syntax-directed semantics when given ( i ) a grammar describing the syntax of a language and ( ii ) an executable (closed-box) interpreter for computing the semantics of programs in the language of the grammar. Our algorithm synthesizes the semantics in the form of Constrained-Horn Clauses (CHCs), a natural, extensible, and formal logical framework for specifying inductively defined relations that has recently received widespread adoption in program verification and synthesis. The key innovation of our synthesis algorithm is a Counterexample-Guided Synthesis (CEGIS) approach that breaks the hard problem of synthesizing a set of constrained Horn clauses into small, tractable expression-synthesis problems that can be dispatched to existing SyGuS synthesizers. Our tool SynAntic synthesized inductively-defined formal semantics from 14 interpreters for languages used in program-synthesis applications. When synthesizing formal semantics for one of our benchmarks, Synantic unveiled an inconsistency in the semantics computed by the interpreter for a language of regular expressions; fixing the inconsistency resulted in a more efficient semantics and, for some cases, in a 1.2 x speedup for a synthesizer solving synthesis problems over such a language.

Proceedings of the ACM on Programming LanguagesVol. 8(OOPSLA2)
University of Wisconsin–Madison (US), University of California San Diego (US)
Openalex Percentile: Top 85%
Natural Language Processing Techniques
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