Book Image

Build Your Own Programming Language

By : Clinton L. Jeffery
Book Image

Build Your Own Programming Language

By: Clinton L. Jeffery

Overview of this book

The need for different types of computer languages is growing rapidly and developers prefer creating domain-specific languages for solving specific application domain problems. Building your own programming language has its advantages. It can be your antidote to the ever-increasing size and complexity of software. In this book, you’ll start with implementing the frontend of a compiler for your language, including a lexical analyzer and parser. The book covers a series of traversals of syntax trees, culminating with code generation for a bytecode virtual machine. Moving ahead, you’ll learn how domain-specific language features are often best represented by operators and functions that are built into the language, rather than library functions. We’ll conclude with how to implement garbage collection, including reference counting and mark-and-sweep garbage collection. Throughout the book, Dr. Jeffery weaves in his experience of building the Unicon programming language to give better context to the concepts where relevant examples are provided in both Unicon and Java so that you can follow the code of your choice of either a very high-level language with advanced features, or a mainstream language. By the end of this book, you’ll be able to build and deploy your own domain-specific languages, capable of compiling and running programs.
Table of Contents (25 chapters)
1
Section 1: Programming Language Frontends
7
Section 2: Syntax Tree Traversals
13
Section 3: Code Generation and Runtime Systems
21
Section 4: Appendix

Regular expressions are not always enough

If you take a theory of computation course, you'll probably be treated to proof that regular expressions cannot match some common patterns that occur in programming languages, particularly patterns that nest instances of the same pattern inside themselves. This section shows that regular expressions are not always enough in other aspects.

If regular expressions are not always able to handle every lexical analysis task in your language, what do you do? A lexical analyzer written by hand can handle weird cases that a lexical analyzer generated from regular expressions can't handle, perhaps at the cost of an extra day, week, or month of your time. However, in almost all real programming languages, regular expressions can get you close enough to where you only need a few extra tricks to produce the finished scanner. Here is a small real-world example.

Unicon and Go are examples of languages that provide semicolon insertion. The...