Title
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. (c) 2020 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2021
10.1016/j.asoc.2020.106956
APPLIED SOFT COMPUTING
Keywords
DocType
Volume
Semantic Inference, Memetic Algorithm, Attribute Grammars, Domain-Specific Languages
Journal
100
ISSN
Citations 
PageRank 
1568-4946
0
0.34
References 
Authors
41
4
Name
Order
Citations
PageRank
Miha Ravber1151.95
Željko Kovačević200.34
Matej Črepinšek300.34
Marjan Mernik43256154.23