#parser #parsing #parser-generator #lr #yacc

bin+lib rustlr

LR/LALR parser generator that can automatically create abstract syntax trees

23 releases

Uses new Rust 2021

new 0.3.1 Aug 10, 2022
0.2.99 Aug 2, 2022
0.2.98 Jul 28, 2022
0.2.3 Mar 24, 2022
0.1.1 Nov 25, 2021

#29 in Parser tooling

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340 downloads per month
Used in lambdascript

MIT license

3MB
17K SLoC

rustlr

LR(1) and LALR(1) parser generator

A Tutorial with several examples is available.

Among the features that Rustlr supports are:

  1. The option of automatically creating the abstract syntax data types and semantic actions from the grammar.
  2. Recognizes regex-style operators *, + and ?, which simplify the writing of grammars and allow better ASTs to be created.
  3. Experimental features include the ability to signal delayed reduction transformations on a grammar.
  4. Operator precedence and associativity declarations further allow grammars to be written that's closer to EBNF syntax.
  5. The ability to train the parser, interactively or from script, for better error reporting.
  6. Semantic actions have access to mutable external state, which (with manually written actions) can recognize some non-context free languages.

Version 0.3.1 consists of minor patches

Major Features and the Versions that Introduced Them

Version 0.3.0: Experimental feature: markers that allow delayed-reduction

grammar transformations. Improved ability to generate reasonable abstract syntax.

Version 0.2.95:

Adds the ability to define custom regular expressions and custom token types to the built-in lexical analyzer; the lexterminal and valueterminal directives further simplify the creation of the lexical analyzer.

Version 0.2.9:

Experimental support for a wildcard token in writing grammars. Grammar production rules can use the now-reserved _ (underscore) symbol to mean unexpected token.

E -->  a _* b

The _ is regarded as a regular terminal symbol during the creation of the deterministic LR statemachine. But a state table entry for the special wildcard will apply to any unexpected input symbol. Please see the tutorial for its subtleties and usage.

Version 0.2.8:

The ability to automatically generate the abstract syntax tree data structures as well as the semantic actions required to create instances of them. Automatically generated actions can be combined with manually written overrides.

Limited support for *, + and ? expressions introduced.

Version 0.2.5:

The ability to write semantic actions returning values of different types has been added, without the need to use the Any trait (and can thus accomodate non-static references). Chapter 3 of the tutorial was rewritten to reflect this important new option. Backwards compatibility is retained.

A simplified syntax for forming LBox has been added: Grammar rules can now contain labeled symbols on the right hand side in the form E:[x], which means that the semantic value associated with grammar symbol E is automatically placed in an LBox and assigned to x.

Version 0.2.3:

The ability to automatically generate a lexical scanner from a minimal set of grammar declarations has been added, using the built-in RawToken and StrTokenizer. This vastly simplifies the process of producing a working parser. Other tokenizers can still be used in the previous way, by adopting them to the Tokenizer trait.

Version 0.2.0:

Significant improvements required that several components are now renamed, while the older ones are retained for compatibility with parsers already created.

  • A new, "zero-copy" lexer interface has been created
  • A general purpose lexical analyzer is now included, although it is still possible to use any lexer due to the use of trait objects.
  • Improved support for using LBox<dyn Any> as abstract syntax type by automatically generating runtime type casting. This means that semantic actions for grammar productions no longer need to return values of the same type. However, this also means that abstract syntax representations cannot contain non-static references due to the Rust restriction that such types cannot impl Any. An alternative approach would be to generate a enum type that includes all possible return types, but this approach is not compatible with allowing the lexical analyzer to be decoupled from the parser.

Version 0.1.4:

This version's main enhancements are pattern labels. In a grammar production, the value attached to nonterminal and terminal symbols can be extracted by specifying a pattern, which will cause an if-let statement to be automatically generated. For abstract syntax with many layers of enums and structs, but which shares a single "absyntype" for the grammar. For example, if Exp and Expl are variants of a common enum, one can now write rules such as

 Exprlist -->  { Expl(Vec::new()) }
 Exprlist --> Exprlist:@Expl(mut ev)@ , Expr:@Exp(e)@  {ev.push(e); Expl(ev)}

This capability was used to construct a parser for a scaled-down version of Java and is included in the examples directory of the repository.

Abilities for using LBox were also extended, which allows LBox<dyn Any> to be used as the abstract syntax type, with functions and macros for up/downcasting.

Version 0.1.2:

Added the LBox smartpointer for encapsulating lexical information (line and column) into abstract syntax.

The parse function has been decomposed into a parse_core, which takes a functional argument that handles error reporting. This allows a custom parser interface to be created if one does not wish to be restricted to the supplied one, which uses stdio.

Version 0.1.1:

The ability to train the parser has been added. The parse_train function will ask for user input to improve error reporting by augmenting the basic generated LR state machine with Error entries.

Constructing a parser that gives helpful error messages can be tricky, especially after a grammar has been modified and the parser is re-generated, which changes the state transition table. Interactive training with the parse_train function now produces, in addition to an augmented parser, a training-script that records each error encountered along with the line, column numbers and the unexpected token. It's the user's responsibility to keep track of the sample input used during interactive training and the script that was created from it. A parser can be retrained from the script, given the identical input (and tokenizer).

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