Compiler construction is at the heart of computer science. As I continue to make my way, if very slowly, though The Dragon Book, it is becoming absolutely clear to me that some things cannot be understood without building them. What Halmos stated so strikingly about mathematics is applicable here:

The only way to learn mathematics is to do mathematics […] The right way to read mathematics is first to read the definitions of the concepts and the statements of the theorems, and then, putting the book aside, to try to discover the appropriate proofs. If the theorems are not trivial, the attempt might fail, but it is likely to be instructive just the same. To the passive reader a routine computation and a miracle of ingenuity come with equal ease, and later, when he must depend on himself, he will find that they went as easily as they came. The active reader, who has found out what does not work, is in a much better position to understand the reason for the success of the author’s method, and, later, to find answers that are not in books. — Paul Halmos, A Hilbert Space Problem Book.

The only way to understand compilers is to build them. It is my goal, therefore, to attach a project to each chapter (or sequence of chapters) that addresses a particular topic: and for chapter 3, which is on lexical analysis, the only possible answer was an implementation of Lex.

In this post I will describe my implementation at a high level and then speak briefly about what I learned in the process.

## My implementation

To understand this section one must be familiar with the material in chapter 3 of The Dragon Book, i.e. with the basics of finite automaton theory (and its relationship to regular expressions). I will quote freely from the book without attribution below.

### Lex

Lex is a lexical-analyser generator, which means that it takes in a Lex source file and produces a lexical-analyser (a lexer, more briefly) in C. A Lex source file has three components:

• Definitions of patterns in the form of regular expressions

• Rules which describe actions to be taken when certain patterns (which we shall call tokens) are encountered

• User subroutines or auxiliary functions: blocks of code which are copied verbatim into the generated file.

The rules each have the form

pattern { action }


where pattern is basically1 defined in the Definitions section, and action is C code to be executed, usually referencing something in the User subroutines section.

Lex works by first building NFAs that match the patterns for each of the tokens in the Rules section, and then synthesizing these into a “total” NFA, as it were, which accepts when any one of the patterns is matched (and contains the necessary information to identify which token has been matched). The generated lexer makes use of this total automaton and the input sequence, creating a function yylex as beneath:

/* nexttoken: obtain name of next matched pattern. return NULL on EOF */
char *
nexttoken();

/* yylex: lex until next token with returning action. return 0 on EOF */
int
yylex()
{
char *tk = nexttoken();
if (NULL == tk) {
return 0; /* EOF */
} else if (strcmp(tk, "pattern0") == 0) {
/* pattern 0 action */
} else if (strcmp(tk, "pattern1") == 0) {
/* pattern 1 action */
} /* else if ... {
...
} */

fprintf(stderr, "unknown pattern %s\n", tk);
exit(EXIT_FAILURE);
}


### Implementation

In order to construct such a yylex from a Lex source file, an implementation must be able to do the following:

1. Parse the input source file such that the distinct sections and their components are identified

2. Build and combine NFAs corresponding to the patterns in the file

3. Simulate NFAs based on input sequences, identifying when they enter accepting (as well as failed) states

4. Generate a lexer that makes use of the parsed output and the NFA functionality to provide a yylex routine as above.

My implementation is made up of four main modules:

• parse.h: parses Lex source file and treats the regexes simply as blocks of text (terminated by newlines)

• thompson.h: parses the regexes using a grammar that describes simple regular expressions

• automata.h: builds, manipulates and simulates automata

• gen.h: generates lexers based on the output of the parser.

The main function simply reads in the input file and then invokes the

struct lexer *
parse(char *);


function from parse.h to obtain a struct lexer corresponding to the file. It submits this to gen.h, using

void
gen(FILE *, struct lexer *); /* simplified slightly */


with the output file being lex.yy.c (unless specified otherwise).

Because my goal was to get a simple implementation, I have used NFA simulation rather than NFA-to-DFA conversion. Thus, all that the gen function above does is hard-code the patterns it finds in the struct lexer provided to it, together with outputting the User subroutine section directly, in such a way that when the lex.yy.c (otherwise named output) file is executed, it immediately builds the total NFA using thompson and automata.

To facilitate this dependency, the gen function includes within an xxd-generated copy of the source of the thompson and automata header and source files, which is triggered in the Makefile using a script gen-preamble.sh similar to this:

#!/bin/sh
cat thompson.h thompson.c automata.h automata.c \
| xxd -name lex_gen_file -i -


The Makefile entry corresponding to this is

# avails thompson and automata libraries
lex_gen.c: $(OBJECTS) gen-preamble.sh @./gen-preamble.sh >$@


Thus, the gen.c source file contains an #include "lex_gen.c", meaning it has access to the latest version of the thompson and automata libraries.

This whole process is summarised in the following diagram:

## Things I learned

### C is almost perfect

One of the most interesting takeaways for me from this project has been the wonderful experience I have had working in C. Of all the things that slowed me down, at no point did I feel that if I were working in a higher-level language (say in Go) that I would’ve moved faster. That realisation alone is astounding given the fact that C is now past the half-century mark.

My main gripes with C are

• Lack of a module/package system leading to names like foo_bar, foo_baz, etc.

• Poor debugging support for simple errors (Surely runtime NULL dereferences could be traced by a good compiler in some debug mode? Are obscure segfaults the only way?)

• Lack of first-class type-support for functions.

I honestly cannot think of anything else to add to the above list. The language is remarkably simple and predictable: in a word, it fits in your head. The more I use it, the more I find myself relying on manpages and K&R rather than on Stack Overflow, which points to its intuitiveness. Most modern languages require large online communities to understand and use.

### Balancing top-down, bottom-up (and TDD)

I lost a lot of time by focusing too hard on the wrong details, and too little on the right overall model. This kind of time-loss is sometimes inescapable when the problem one is solving is novel. However, in this case, I fully understood the theory from the beginning, so I would have spent my time better if I began by fully writing out my high-level model in a diagram like the one above.

When building a sophisticated program that involves several modules, the question of whether to approach it top-down or bottom-up seems to turn on how well one understands the problem. If it is perfectly, or nearly so, understood, it makes sense to draw a rough sketch of the modules and then immediately begin implementing them one-by-one. In fact, this is the case in which a test-driven development model may be applicable (within reason and not in a slavish way). Well-understood components, such as the regex-parsing and automata libraries, are modules for which tests can be written in advance.

However, to the extent that one is solving a problem that is not well understood, a top-down approach seem to be wisest. Going into this project, although I knew what Lex was doing in theory, some things were not clear to me, such as the fact that the gen library could generate code that relies on the source of the thompson and automata libraries. If I had spent more time diagramming and even writing header files in detail, with careful analysis I would have saved myself countless hours (and indeed, days).

The moral of the story is that the right way to solve a complex problem is to try and identify distinct centres of complexity within it, and to make these the modules. If one is able to identify the centres accurately, then it is possible to proceed in a bottom-up manner from this point onward. But until a clear model emerges bottom-up development should be avoided.

One of the most time-consuming parts of my experience was in implementing the automata library, yet I fully understood what it was supposed to do at a high-level. The fact that its complexity was firmly bounded within itself as a module meant that I was able to prevent this complexity from creeping into the rest of the program.

### Lex is a great project but probably an unnecessary tool

Anyone who has written a few lexers can see that, although Lex incorporates many interesting ideas (such as finite automata theory), it is overkill for almost every use case conceivable, because lexers are actually fairly easy to implement.

This notwithstanding, I would argue that Lex has amazing pedagogical value. It is based on so many wonderful and beautiful ideas, and requires such a thorough grasp of them, that implementing it is an excellent exercise for the mind. It is also a great stepping stone, I hope, towards the next project, corresponding to the chapter on parsing: I will be back when, by God’s grace, I have implemented Yacc.

1. Lex implementations generally allow string literals here that have not been defined in the Definitions section to match themselves, as well as regular definitions involving patterns defined in Definitions within them. I have only included the former functionality in my implementation. ↩︎