Synthesis of Semantic Actions in Attribute Grammars
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Attribute grammars allow the association of semantic actions to the production rules in context-free grammars, providing a simple yet effective formalism to define the semantics of a language. However, drafting the semantic actions can be tricky and a large drain on developer time. In this work, we propose a synthesis methodology to automatically infer the semantic actions from a set of examples associating strings to their meanings. We also propose a new coverage metric, derivation coverage. We use it to build a sampler to effectively and automatically draw strings to drive the synthesis engine. We build our ideas into our tool, PANINI, and empirically evaluate it on twelve benchmarks, including a forward differentiation engine, an interpreter over a subset of Java bytecode, and a mini-compiler for C language to two-address code. Our results show that PANINI scales well with the number of actions to be synthesized and the size of the context-free grammar, significantly outperforming simple baselines.
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