Automatic compiler/interpreter generation from programs for Domain-Specific Languages: Code bloat problem and performance improvement

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@Article{KOVACEVIC:2022:cola,

• author = "Zeljko Kovacevic and Miha Ravber and Shih-Hsi Liu and Matej Crepinsek",
• title = "Automatic compiler/interpreter generation from programs for Domain-Specific Languages: Code bloat problem and performance improvement",
• journal = "Journal of Computer Languages",
• volume = "70",
• pages = "101105",
• year = "2022",
• ISSN = "2590-1184",
• DOI = "doi:10.1016/j.cola.2022.101105",
• URL = "https://www.sciencedirect.com/science/article/pii/S2590118422000132",
• keywords = "genetic algorithms, genetic programming, Semantic inference, Attribute grammars, Domain-Specific Languages, Code bloat",
• abstract = "Using advanced AI approaches, the development of Domain-Specific Languages (DSLs) can be facilitated for domain experts who are not proficient in programming language development. In this paper, we first addressed the aforementioned problem using Semantic Inference. However, this approach is very time-consuming. Namely, a lot of code bloat is present in the generated language specifications, which increases the time required to evaluate a solution. To improve this, we introduced a multi-threaded approach, which accelerates the evaluation process by over 9.5 times, while the number of fitness evaluations using the improved Long Term Memory Assistance (LTMA) was reduced by up to 7.3percent. Finally, a reduction in the number of input samples (fitness cases) was proposed, which reduces CPU consumption further",

}

Genetic Programming entries for Zeljko Kovacevic Miha Ravber Shih-Hsi Liu Matej Crepinsek

Citations