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Inferring Absolutely Non-Circular Attribute Grammars with a Memetic Algorithm
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DOI：10.1016/j.asoc.2020.106956.png)
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Abstract
En 中文
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. (c) 2020 Elsevier B.V. All rights reserved.
Keywords:
Semantic Inference
Memetic Algorithm
Attribute Grammars
Domain-Specific Languages
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