Inferring Absolutely Non-Circular Attribute Grammars with a Memetic Algorithm

Abstract

When valid syntactical structures are additionally constrained with context-sensitive information the Grammar Inference needs to be extended to the Semantic Inference. In this paper, it is shown that a complete compiler/interpreter for small Domain-Specific Languages (DSLs) can be generated automatically solely from given programs and their associated meanings using Semantic Inference. In this work a wider class of Attribute Grammars has been learned, while only S-attributed and L-attributed Grammars have previously been inferred successfully. Inferring Absolutely Non-Circular Attribute Grammars (ANC-AG) with complex dependencies among attributes has been achieved by integrating a Memetic Algorithm (MA) into the LISA.SI tool. The results show that the proposed Memetic Algorithm is at least four times faster on the selected benchmark than the previous method.