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1.
Automatic compiler/interpreter generation from programs for domain-specific languages using semantic inference : doktorska disertacija
Željko Kovačević, 2022, doctoral dissertation

Abstract: Presented doctoral dissertation describes a research work on Semantic Inference, which can be regarded as an extension of Grammar Inference. The main task of Grammar Inference is to induce a grammatical structure from a set of positive samples (programs), which can sometimes also be accompanied by a set of negative samples. Successfully applying Grammar Inference can result only in identifying the correct syntax of a language. But, when valid syntactical structures are additionally constrained with context-sensitive information the Grammar Inference needs to be extended to the Semantic Inference. With the Semantic Inference a further step is realised, namely, towards inducing language semantics. In this doctoral dissertation 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. For the purpose of this research work the tool LISA.SI has been developed on the top of the compiler/interpreter generator tool LISA that uses Evolutionary Computations to explore and exploit the enormous search space that appears in Semantic Inference. A wide class of Attribute Grammars has been learned. Using Genetic Programming approach S-attributed and L-attributed have been inferred successfully, while 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.
Keywords: Grammatical Inference, Semantic Inference, Genetic Programming, Attribute Grammars, Memetic Algorithm, Domain-Specific Languages
Published in DKUM: 17.02.2022; Views: 1158; Downloads: 121
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2.
Embedding DSLS into GPLS
Dejan Hrnčič, Marjan Mernik, Barrett Richard Bryant, 2011, original scientific article

Abstract: Embedding of Domain-Specific Languages (DSLs) into General-Purpose Languages (GPLs) is oftenused to express domain-specific problems using the domainćs natural syntax inside GPL programs. It speeds up thedevelopment process, programs are more self-explanatory and repeating tasks are easier to handle. End-users ordomain experts know what the desired language syntax would look like, but do not know how to write a grammar andlanguage processing tools. Grammatical inference can be used for grammar extraction from input examples. Amemetic algorithm for grammatical inference, named MAGIc, was implemented to extract grammar from DSLexamples. In this work MAGIc is extended with embedding the inferred DSL into existing GPL grammar.Additionally, negative examples were also incorporated into the inference process. From the results it can be concludedthat MAGIc is successful for DSL embedding and that the inference process is improved with use of negativeexamples.
Keywords: memetic algorithms, doamin-specific languages, grammatical inference, embedding
Published in DKUM: 01.06.2012; Views: 1668; Downloads: 50
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