Inferring Context-Free Grammars for Domain-Specific Languages

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@Article{Crepinsek:2006:ENTCS,

• author = "Matej Crepinsek and Marjan Mernik and Barrett R. Bryant and Faizan Javed and Alan Sprague",
• title = "Inferring Context-Free Grammars for Domain-Specific Languages",
• journal = "Electronic Notes in Theoretical Computer Science",
• year = "2005",
• volume = "141",
• number = "4",
• pages = "99--116",
• month = "12 " # dec,
• note = "Proceedings of the Fifth Workshop on Language Descriptions, Tools, and Applications (LDTA 2005)",
• keywords = "genetic algorithms, genetic programming, Grammar induction, Grammar inference, Learning from positive and negative examples, Exhaustive search",
• ISSN = "1571-0661",
• DOI = "doi:10.1016/j.entcs.2005.02.055",
• abstract = "In the area of programming languages, context-free grammars (CFGs) are of special importance since almost all programming languages employ CFG's in their design. Recent approaches to CFG induction are not able to infer context-free grammars for general-purpose programming languages. In this paper it is shown that syntax of a small domain-specific language can be inferred from positive and negative programs provided by domain experts. In our work we are using the genetic programming approach in grammatical inference. Grammar-specific heuristic operators and nonrandom construction of the initial population are proposed to achieve this task. Suitability of the approach is shown by examples where underlying context-free grammars are successfully inferred.",

}

Genetic Programming entries for Matej Crepinsek Marjan Mernik Barrett R Bryant Faizan Javed Alan P Sprague

Citations