Guided Grammar Convergence
This addresses inconsistency management in grammar engineering, offering an automated alternative to human-expert methods, though it is incremental as it builds on existing convergence approaches.
The paper tackled the problem of relating formal grammars by proposing guided grammar convergence, a technique that automatically infers transformation steps between grammars through normalization and structural equivalence, enabling a case study with 11 grammars of an artificial functional language.
Relating formal grammars is a hard problem that balances between language equivalence (which is known to be undecidable) and grammar identity (which is trivial). In this paper, we investigate several milestones between those two extremes and propose a methodology for inconsistency management in grammar engineering. While conventional grammar convergence is a practical approach relying on human experts to encode differences as transformation steps, guided grammar convergence is a more narrowly applicable technique that infers such transformation steps automatically by normalising the grammars and establishing a structural equivalence relation between them. This allows us to perform a case study with automatically inferring bidirectional transformations between 11 grammars (in a broad sense) of the same artificial functional language: parser specifications with different combinator libraries, definite clause grammars, concrete syntax definitions, algebraic data types, metamodels, XML schemata, object models.