Inferring Context-Free Grammars for Domain-Specific Languages

Authors:
Matej Črepinšek;Marjan Mernik;Barrett R. Bryant;Faizan Javed;Alan Sprague
Affiliations:
University of Maribor, Faculty of Electrical Engineering and Computer Science, Smetanova 17, 2000 Maribor, Slovenia;University of Maribor, Faculty of Electrical Engineering and Computer Science, Smetanova 17, 2000 Maribor, Slovenia;The University of Alabama at Birmingham, Department of Computer and Information Sciences, Birmingham, AL 35294-1170, U.S.A.;The University of Alabama at Birmingham, Department of Computer and Information Sciences, Birmingham, AL 35294-1170, U.S.A.;The University of Alabama at Birmingham, Department of Computer and Information Sciences, Birmingham, AL 35294-1170, U.S.A.
Venue:
Electronic Notes in Theoretical Computer Science (ENTCS)
Year:
2005

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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.