langcc: A Next-Generation Compiler Compiler

langcc is a tool that takes the formal description of a language, in a standard BNF-style format, and automatically generates a compiler front-end, including data structure definitions for the language's abstract syntax trees (AST) and traversals, a lexer, a parser, and a pretty-printer. langcc also serves as the companion software implementation to the following technical reports, which describe several innovations on the classic LR parsing paradigm:

• Zimmerman, Joe. Practical LR Parser Generation. arXiv, 2022.
• Zimmerman, Joe. langcc: A Next-Generation Compiler Compiler. arXiv, 2022.

langcc can be used as a replacement for the combination of lex and yacc (or flex and bison). However, langcc provides many additional features, including:

• Automatic generation of AST data structures, via a standalone datatype compiler (datacc).
• Full LR parser generation as the default, rather than the more restrictive LALR.
• Clear presentation of LR conflicts via explicit "confusing input pairs", rather than opaque shift/reduce errors.
• Novel efficiency optimizations for LR automata.
• An extension of the LR paradigm to include recursive-descent (RD) parsing actions, resulting in significantly smaller and more intuitive automata.
• An extension of the LR paradigm to include per-symbol attributes, which are vital for the efficient implementation of many industrial language constructs.
• A general transformation for LR grammars (CPS), which significantly expands the class of grammars the tool can support.

Unlike previous compiler compilers, langcc is general enough to capture full industrial programming languages, for which existing parsers are typically written by hand or generated from procedural descriptions. Examples include Python 3.9.12 (grammars/py.lang) and Golang 1.17.8 (grammars/go.lang). In both cases, langcc automatically generates a parser that is faster than the standard library parser for each language. In fact, the class of grammars supported by langcc is general enough that the tool is self-hosting: that is, one can express the "language of languages" in the "language of languages" itself, and use langcc to generate its own compiler front-end. We do this in the canonical implementation; see the files bootstrap.sh and grammars/meta.lang for more details.

langcc is a research prototype and has not yet been used extensively in production. However, we believe it is essentially stable and feature-complete, and can be used as a standalone tool to facilitate rapid exploration of new compilers and programming languages.

Build

The build has been tested on Ubuntu 22.04 and macOS 12.5, but should also run on some other versions of Ubuntu and macOS with minor adaptations.

For Ubuntu 22.04:

./deps_ubuntu.sh
make -j8
sudo make install

For macOS 12.5 (requires Homebrew and MacPorts):

./deps_macos.sh
make -j8
sudo make install

And, in order to bootstrap the langcc front-end itself, subsequently run:

./bootstrap.sh

Examples

Once langcc (and its companion, datacc) have been installed, one can run various examples:

• In the examples directory, there are two examples: basic and calc. Each has its own local Makefile.
• The main build process itself compiles grammars/py.lang and grammars/go.lang, producing tests build/go_standalone_test and build/py_standalone_test. (Note: These binaries require, respectively, repositories for Golang 1.17.8 located in the directory ../go, and Python 3.9.12 located in the directory ../cpython.)
• There is the language of datatypes, grammars/data.lang, which describes the input of the additional standalone tool datacc (used by langcc to automatically generate C++ implementations of algebraic datatypes).
• Finally, there is the language of languages itself, grammars/meta.lang. This language also serves as basic documentation, as it enumerates all of its own features.

Documentation

For full documentation, see the accompanying technical report:

• Zimmerman, Joe. langcc: A Next-Generation Compiler Compiler. arXiv, 2022.

as well as the theoretical development:

• Zimmerman, Joe. Practical LR Parser Generation. arXiv, 2022.