Article by Ayman Alheraki on May 8 2025 10:16 AM
Designing a programming language has long been a fascinating endeavor combining deep theoretical knowledge with practical software engineering. This process has evolved significantly over time, transitioning from low-level, manual methods to modern modular, assisted frameworks. Let’s explore how this evolution unfolded and what tools are available today to help both hobbyists and professionals build programming languages more efficiently.
In the early days of computing, language creators had to build everything from scratch:
Lexical analyzers (lexers) and parsers were hand-written.
Intermediate representations didn’t exist.
Code generation often required direct output of Assembly or machine code.
Examples include early versions of FORTRAN, COBOL, and C. These languages were implemented in assembly or using very primitive toolchains. The effort required to build even a basic language was massive.
By the 1970s and 80s, tools like Lex and Yacc (and later Flex and Bison) became available. These tools enabled developers to define:
Lexical tokens using regular expressions.
Grammar rules using context-free grammars.
This was a major breakthrough, allowing language designers to focus more on logic rather than parsing mechanics.
Today, developing a programming language doesn’t always mean building everything from scratch. Modern tools allow you to build languages that are efficient, portable, and even production-ready. The most prominent example is the LLVM project:
LLVM provides a modular backend capable of targeting dozens of CPU architectures.
Clang, its frontend for C/C++, can be used as a model or starting point.
Rust, Swift, and even custom DSLs use LLVM as their backend.
Other tools like ANTLR, Tree-sitter, and PEG.js allow you to build custom parsers and interpreters more easily than ever.
Furthermore, with LSP (Language Server Protocol), developers can now integrate their language into modern code editors and provide features like syntax highlighting, auto-completion, and error checking.
From writing assembly by hand to building on top of LLVM and integrating into VS Code via LSP, the journey of programming language creation has come a long way. Today, with open-source ecosystems, community support, and educational resources, even a single developer can build a functional language in weeks or months—something that used to take years.
| Name | Description | Use Case | Official Site |
|---|---|---|---|
| LLVM | Open-source compiler infrastructure for IR & backend code generation. | Professional compilers, multi-arch support. | https://llvm.org |
| Clang | C/C++ frontend for LLVM. | Reusable frontend, code parsing. | https://clang.llvm.org |
| ANTLR | Parser generator from grammars. | Custom language parsing. | https://www.antlr.org |
| Flex | Lexical analyzer generator. | Define token rules using regex. | https://github.com/westes/flex |
| Bison | Parser generator based on grammar rules (GNU version of Yacc). | Build syntax tree from grammar. | https://www.gnu.org/software/bison/ |
| Tree-sitter | Incremental parsing library for editors. | Syntax highlighting and live parsing. | https://tree-sitter.github.io |
| PEG.js | JavaScript library for writing PEG-based parsers. | Browser/JS-based languages and DSLs. | https://pegjs.org |
| Cranelift | Fast code generator used in Wasmtime and Rust. | Lightweight alternative to LLVM. | https://bytecodealliance.org/cranelift |
| REPL | Read-Eval-Print Loop - Interactive language interface. | Debugging or learning new languages. | General concept |
| LSP | Language Server Protocol - used to link IDEs with compilers. | Autocomplete, diagnostics, refactoring. | https://microsoft.github.io/language-server-protocol |
