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.
%0 Journal Article
%1 Crepinsek:2006:ENTCS
%A Crepinsek, Matej
%A Mernik, Marjan
%A Bryant, Barrett R.
%A Javed, Faizan
%A Sprague, Alan
%D 2005
%J Electronic Notes in Theoretical Computer Science
%K Exhaustive Grammar Learning algorithms, and examples, from genetic induction, inference, negative positive programming, search
%N 4
%P 99--116
%R doi:10.1016/j.entcs.2005.02.055
%T Inferring Context-Free Grammars for Domain-Specific
Languages
%V 141
%X 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.
@article{Crepinsek:2006:ENTCS,
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.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Crepinsek, Matej and Mernik, Marjan and Bryant, Barrett R. and Javed, Faizan and Sprague, Alan},
biburl = {https://www.bibsonomy.org/bibtex/2b359032acefed37074158ec1c5cb61bc/brazovayeye},
doi = {doi:10.1016/j.entcs.2005.02.055},
interhash = {8c0d35a7a84a9255f2aa4c1ef99091f3},
intrahash = {b359032acefed37074158ec1c5cb61bc},
issn = {1571-0661},
journal = {Electronic Notes in Theoretical Computer Science},
keywords = {Exhaustive Grammar Learning algorithms, and examples, from genetic induction, inference, negative positive programming, search},
month = {12 December},
note = {Proceedings of the Fifth Workshop on Language
Descriptions, Tools, and Applications (LDTA 2005)},
number = 4,
pages = {99--116},
timestamp = {2008-06-19T17:38:14.000+0200},
title = {Inferring Context-Free Grammars for Domain-Specific
Languages},
volume = 141,
year = 2005
}