The ModelCC Model-Based Parser Generator

TL;DR

ModelCC is a model-based parser generator that separates language specification from processing, enabling easier modifications and supporting abstract syntax graphs through integrated reference resolution.

Contribution

It introduces a novel approach that decouples language design from processing, reducing maintenance effort and supporting complex data structures.

Findings

Supports abstract syntax graphs from input strings

Reduces effort in language specification updates

Integrates reference resolution in parsing

Abstract

Formal languages let us define the textual representation of data with precision. Formal grammars, typically in the form of BNF-like productions, describe the language syntax, which is then annotated for syntax-directed translation and completed with semantic actions. When, apart from the textual representation of data, an explicit representation of the corresponding data structure is required, the language designer has to devise the mapping between the suitable data model and its proper language specification, and then develop the conversion procedure from the parse tree to the data model instance. Unfortunately, whenever the format of the textual representation has to be modified, changes have to propagated throughout the entire language processor tool chain. These updates are time-consuming, tedious, and error-prone. Besides, in case different applications use the same language, several copies of the same language specification have to be maintained. In this paper, we introduce ModelCC, a model-based parser generator that decouples language specification from language processing, hence avoiding many of the problems caused by grammar-driven parsers and parser generators. ModelCC incorporates reference resolution within the parsing process. Therefore, instead of returning mere abstract syntax trees, ModelCC is able to obtain abstract syntax graphs from input strings.