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Attribute grammars allow the association of semantic actions to theproduction rules in context-free grammars, providing a simple yet effectiveformalism to define the semantics of a language. However, drafting the semanticactions can be tricky and a large drain on developer time. In this work, wepropose a synthesis methodology to automatically infer the semantic actionsfrom a set of examples associating strings to their meanings. We also propose anew coverage metric, derivation coverage. We use it to build a sampler toeffectively and automatically draw strings to drive the synthesis engine. Webuild our ideas into our tool, PANINI, and empirically evaluate it on twelvebenchmarks, including a forward differentiation engine, an interpreter over asubset of Java bytecode, and a mini-compiler for C language to two-addresscode. Our results show that PANINI scales well with the number of actions to besynthesized and the size of the context-free grammar, significantlyoutperforming simple baselines.

Synthesis of Semantic Actions in Attribute Grammars