Paylalr

Paylalr is a parser generator that implements the LALR(1) parsing technique for the automatic construction of parsers from formal grammars. It is designed for use in compiler and interpreter toolchains, as well as in applications that require structured input parsing.

The tool accepts a grammar specification that defines tokens, nonterminals, productions, and optional precedence and associativity rules. Using this information, paylalr builds an LALR(1) parse table by computing item sets and merging states to form a compact finite automaton. The resulting parser can then be embedded in a host program or output as source code in a target language. Typical workflows include defining a grammar, running paylalr to generate a parser, integrating the parser into an application, and providing semantic actions to construct abstract syntax trees or other representations during parsing.

Common features associated with paylalr-style tooling include support for user-defined error handling and recovery strategies, hooks for building and attaching AST nodes, and options to generate verbose debugging output to trace parsing decisions. Many implementations also offer multiple backends or target languages, allowing the generated parser to be compiled and linked with the rest of a software project. Some tools provide incremental parsing capabilities, code generation templates, and integration with lexical analyzers.

Background and context: LALR parsing is a space-efficient form of LR parsing that aims to cover a large class of programming language grammars while keeping parse tables manageable. Paylalr sits within a family of parser generators that includes YACC, Bison, Lemon, and similar systems, and is typically used when a project requires a deterministic, shift-reduce parser with straightforward grammar specification.

See also: LR and LALR parsing, YACC, Bison, Lemon, ANTLR.