Introduction to iMatix MOP


  1. Introduction
  2. Becoming a Very Good Programmer
  3. Tools that Write Software
  4. Abstractions and Modeling Languages
  5. Leverage to Move Mountains
  6. Model-Oriented Programming
  7. Other Model-Driven Architectures
  8. Why Use MOP?
  9. A Rapid History of Code Generation
  10. Myths about Code Generation
  11. The Correctness of Generated Code
  12. Starting with GSL
  13. Templates and Scripts
  14. Conclusion

1. Introduction

This is the first in a series of articles, aimed at the professional programmer. I'm going to attack a complex subject, something that few people know about. It's a new way of programming called "model-oriented programming". I'm not going to ask you to throw out your programming languages or tools. MOP works as a layer on top of everything you know today. I am going to ask you to rethink what it means to "write a program", and to see that most of the code you write could be better written by robots, meaning other programs. And I'm going to teach you how to design and make such robots.

MOP works for every kind of area you write code for. Whether you write games, Linux drivers, servers, applications, plug-ins, whether you use Java, C, Perl, Ruby, Python, Gnome or KDE... once you start to see the world as models you'll find yourself writing more code, faster, than you ever thought possible.

In this article you will learn what MOP is, and why we invented it. I'll also explain some of the underlying technologies.

Be warned. This might hurt a little. All I can promise is that if you learn to use MOP you will use for the rest of your life, and wonder how you ever worked without it.

2. Becoming a Very Good Programmer

My name is Pieter Hintjens, and I am a programmer. My team and I build infrastructure software - frameworks, middleware, servers, etc. I've learned a few things about software since I wrote my first small program in 1981 or so. First: if it's not impossible, it's not worth doing. Second: software design is about overcoming human limitations, not technical ones. Third: very few people can actually design good, useful, large-scale software systems, which for me is the goal of programming.

In my experience there are at least four main aspects to becoming a very good programmer:

  • Never throw out anything that works until it is really worn out. This mainly means writing portable code.
  • Never solve the same problem more than once in parallel. This mainly means building tools.
  • Solve the same problem often in serial. This means being willing to throw out code and rewrite it when you find better ways.
  • Write code, write code, write code, until it is as natural as speaking.

Of course you also need talent, opportunity, and guidance, but a focus on portability, obsessive tool building, and years of practice can turn talent into real skill.

3. Tools that Write Software

In this series of articles I'll focus on the second part, making tools. There are quite a few metaphors for software tools. For example, the Unix metaphor consists of tools as filters: read some data, do some work, produce some output. It's a simple model that lets you chain together tools. Linux has many tools that work as filters.

A more subtle but much more powerful metaphor is to build tools as languages. That is, when you come across a new class of problem, you create a new language that lets you express solutions to those problems in a simple and clear way.

Programming languages are one example of tools that work like this. Most programming languages have their strong and weak points, but basically they are all equivalent: they solve the general problem of "programming", not specific problems like "constructing a firewall" or "building a static web site".

Now consider HTML. This is a language that takes a different approach. You don't use HTML to write programs: you use it to define structured documents, and then you give these documents to programs that can do useful things with the definitions, like show them on a screen.

It's worth comparing HTML to a language like PostScript, also used to get documents looking pretty on paper or screen. PostScript is a programming language (a threaded stack-based interpreter descended from Forth, in fact). People have written, for example, web servers in PostScript. No writer or designer actually sits down and writes PostScript, though people did this before HTML existed.

I sometimes use a tool that turns HTML pages into PostScript documents. Now, as a writer, I can use HTML to write my documents and then push a button to turn this HTML into PostScript. What I am actually doing is converting a descriptive language into a programming language. A HTML-to-PostScript converter lets millions of non-technical people suddenly produce perfect programs at will. Millions of people who never think of themselves as "programmers" can write PostScript, via higher-level abstractions like HTML. And the PostScript programs they produce are much better than an average PostScript programmer can write in a reasonable time.

4. Abstractions and Modeling Languages

HTML is in fact a "modeling language", a language used to describe some system or entity. Modeling languages are very interesting because they provide levels of abstraction that programming languages cannot even conceive of. Abstraction lets you define and use high-level concepts like, "this is a document title", without having to specify what that actually means, on paper or on the screen. Abstraction relies on concepts that have enough meaning to be useful, without being too detailed. For example, "the web" is a useful abstraction for "various URL formats, protocols for transferring such resources, clients and servers that implement such protocols, and collections of resources that are thus interconnected".

Abstraction is an important concept. It is one of the keys to solving complex problems. Let me give you another example of abstraction. We can build an application using a shell script that does each step. This is not abstract, it is a literal set of steps. A makefile is more abstract: it adds the abstractions of "file type" (based on extension) and "target" and specifies how we transform one type of file into another in order to build a specific target. There are more abstract descriptions of projects too. Each time we make a good abstraction - a simple view that hides complex details - we eliminate a layer of manual work.

Now imagine you could use modeling languages as a way of writing programs. Instead of writing Perl, or Java, or C++, or Ruby, you would describe some kind of model and then press a button. In fact this is not a new idea: I've worked with systems that have done this, more or less successfully, for twenty years.

Historically, computer scientists have tried to make programming languages more powerful by adding functionality and by adding programming abstractions. The underlying assumption is that since programming languages are general purpose, they must be the best tools for building software. Adding general purpose programming capacity gets more and more complex as we reach for more abstraction. Thus we get languages that are so complex that to master them is a full career.

This is, I believe, a mistaken approach. Complexity is difficult to control, and complex languages (like Perl and C++) have a reputation for producing complex and hard-to-maintain code. As a programmer, I have quite a problem investing so much in any single language.

The trick that I've found (since I'm lazy and don't have the patience to read 500-page guides to programming languages) is to create simple abstractions - models - that solve useful problems, and to use these models to generate code, just as we produce documents on screen and on paper by generating code from abstractions like HTML.

For naive users, a model is a visual thing, but for us programmers, a textual modeling language is much more useful. There exist many modeling languages, and as I said, it is not a new concept. For example, in 1991 I wrote a tool, Libero, that turns finite state machine models into code. Libero was extremely useful, and it is still part of our toolbox today. What it does is take a state machine model (a text file), and turn that into code in arbitrary languages (we made code generators for twenty or so programming languages). State machines are a very useful model for writing programs, but that's a different story.

Libero took me about three months to build, time that I've won back on many projects. In a general sense, Libero is like the HTML-to-PostScript convertor. It takes a definition written in an abstract modeling language and turns that into code that makes the model come to life. The generated code is always perfect, and as invisible as the PostScript code.

5. Leverage to Move Mountains

I'm coming to the point of this article. This kind of model-to-code tool is very useful. It gives you leverage. That is, the ability to move mountains. It is much easier, faster, cheaper to change an abstract model than it is to change the code that makes it work. Look again at a PostScript program and now imagine the work needed to change a H1 item to H2.

Good models let you work 10 to 100 times faster than any programming language. As long as you stick to the problems the model was meant for, there is really no downside, no reason to prefer PostScript over HTML for writing texts.

I've said that part of being a world-class programmer is the ability to make useful tools. But how do you, as a programmer, make a tool that compiles a modeling language into code? You need to solve four main problems:

  • You need to learn, borrow, adapt, or invent useful models. This is not easy. Good models like finite state machines and hierarchical documents took many clever people many years to invent and refine.
  • You need to define a language that lets people make such models. Again, getting this right is delicate. There are hundreds of ways to write state machines, just as there were hundreds of document markup languages before HTML, most being far too complex and thus useless.
  • You need to build a parser that can read this language, check it, turn it into internal structures, optimise those structures, etc. Needless to say, writing parsers is not easy, there exist whole sets of tools just to solve this problem.
  • You need to build a code generator that can take these internal structures and spit out the final code in whatever target languages you want to produce. Writing code generators is a black art. There are almost no books on the subject, no standard technologies.

If you've ever studied how compilers work, it's much the same problem. What I'm talking about is building compilers for modeling languages.

6. Model-Oriented Programming

Modeling languages and programming languages can overlap. For example, objects are a type of model. The biggest problem with putting models into the programming language is that for real, large problems, we need many different types of model, and these cannot be expressed a single language. Languages that attempt this become too complex to work with. Imagine attempting to describe a hierarchical document using objects, and compare this to writing some HTML by hand.

I'll explain with cases taken from a real project. My team is building a large middleware server (this is a new open source product that we're going to release in 2006). We're using C as the target language for portability and performance, but we actually design the software as lots of high-level models. Each modeling language is part of a code generation process that produces real code. We have modeling languages for:

  • Classes to encapsulating functions.
  • Finite state machines for building protocol handlers.
  • Project definitions for building and packaging sources.
  • Grammar definitions for building parsers and code generators.
  • Grammar definitions for communication protocols.
  • Test scripting language.

The key to making these different modeling tools is cost. If it is cheap to design, test, and use modeling languages, we can cut the research and learning time dramatically.

Since I wrote Libero almost fifteen years ago, I've been working with Jonathan Schultz to make better technologies for building modeling languages and the code generators that bring them to life. We finished the last of the main tools last year, and we now have technology that lets us design and deploy new modeling languages in a matter of weeks. The process is so fast, and so efficient, that we are able to generate almost 100% of the middleware server, a half-million lines of C code, from about sixty thousand lines of modeling code.

The downside is that anyone wanting to understand the code has to learn the five or six models we use. The upside is that they only have sixty thousand lines of code to read, not half a million.

Let me take you through the main parts of our architecture, which we call "Model-Oriented Programming":

The basic technology is GSL, a programming language that we use to build code generators. Yes, you can write a web server in GSL, but that would be pointless. GSL is aimed very much at spitting out huge volumes of perfect code. GSL is an interpreter, it runs as a command, just like Perl or another scripting language.

The second main technology is XML, which we did not invent of course, but which we happily adopted in 1997, having spent several years designing our own very similar meta-languages. We use XML in a simplistic way, to model data, not to do any kind of complex document manipulation. No stylesheets or namespaces, thus.

The third main technology is XNF, which is a tool for building model-driven code generators. We start to get meta here. XNF (for "XML Normal Form") lets you define the grammar of an XML-based modeling language. From that grammar XNF produces parsers and a framework into which you plug hand-written back-end code generators. XNF is a modeling language for code generators. XNF is the basis for all our complex modeling tools, including XNF itself.

These tools are somewhat unusual. The techniques of code generation are not well understood, and no teams have ever pushed these techniques as far as we have. I don't promise that it will be easy to understand - abstraction can be hard to grasp - but once you "get it", you'll be able to produce tools that solve your programming problems ten times faster than using any other technique.

7. Other Model-Driven Architectures

Using models as the basis for designing applications is not new. I've worked with many tools that promised "an end to programming" through the magic of point and click modeling. Some of these - such as UML (Universal Modeling Language) - have become industry standards. In my experience, these tools do not work except as expensive and slow documentation tools.

Perhaps my opinion of classic modeling tools such as UML have been influenced by watching them being abused on large projects. The typical scenario is that a big team of analysts work for a year to produce a "model", which is then thrown out as a second team of developers write the actual code.

The fundamental problem is that no single modeling language can cover the variety needed to solve real world programming challenges. Just imagine someone suggesting that UML could be used to write a Linux device driver, or a high-performance game. That's a joke! Yet my team uses models to design and build very technical, very high-performance software. You just need the right models.

Classic MDA tools attempt to do everything with a single modeling language. This is doomed to failure except within a very narrow niche of work. Indeed, it is more expensive to "not write code" using a language like UML than to simply write the code in Java. Just as a single programming language cannot cover all abstractions, neither can a single modeling language.

To succeed with a model-driven architecture, you need a way to build, test, and improve a variety of different models, each solving one specific domain. What you need is not a single, do-it-all modeling language, but a technology that lets you build arbitrary modeling languages.

And this is iMatix MOP.

8. Why Use MOP?

Despite the trendy name, MOP is really about solving real problems in the most efficient possible way. Let's look at the main advantages my team gets from using MOP:

We have to write much less code to get the same results. I call this "leverage". One line of modeling code can be worth ten or twenty lines of a programming language. Using less code has many knock-on advantages: we work faster, better, and cheaper.

We get high-level models of important aspects of the system. All systems have key models, but they are usually hidden in the code and impossible to verify, formalise, or exploit fully. When the model is turned into a concrete language, it makes the software much better.

We can produce extremely high-quality code. This is an effect of doing code generation: the generated code we produce has no errors, and is as good as a human programmer can write, consistently.

We write less internal documentation, and often none at all, since each model is documentation.

We are immune to technological changes since MOP is entirely abstract from specific programming languages, operating systems, and trends. It can take years to develop really good models but they work for decades.

There are also disadvantages:

People do not rapidly understand or trust the approach. I've been accused of over-investing in tools (sometimes more than half the cost of a project goes into modeling tools). The look on the client's face when we deliver version after version of impeccable software in impossibly short deadlines is worth it.

Programmers do not rapidly understand the models. It takes time to learn each one, sometimes weeks or months.

So, MOP is best used in small, skilled, and long-lasting teams (like iMatix) that solve highly complex and critical problems. Before you can use MOP in a project you need complete confidence of the people paying for the work. If you're writing software for yourself, it's easy. If you're writing software for other people, this can be a hard sell. MOP can also be used to give structure to larger development teams, but it is a lot of work to train mediocre people to use sophisticated models.

The sad thing in the software business is that few people actually understand that better techniques save money. Still, there is no pleasure, as a programmer, in writing bad code using bad tools. So, learn to use MOP, then convince your bosses that they will save money, right away, by using this. Everyone wins.

9. A Rapid History of Code Generation

To understand and use MOP you need to appreciate code generation as a technology. I first started writing code generators in 1985, and I've seen these tools evolve through several stages (in my own work, but also in the general domain):

Hard-coded code generators that take some meta-data (a model) and output code using print statements. This is the most common, and the most limited form. Typical examples are all the classic "code generators" built into products.

Template-driven code generators that use symbolic insertion to inject meta-data into a template. This technology makes it much easier to write new ad-hoc templates. Typical examples are any technology that produces dynamic web page.

Scripted code generators that use a high-level language to manipulate meta-data and then inject it into templates. This technology makes it much easier to write new ad-hoc code generators. Typical examples are XSLT, GSL and some other scripted code generation languages.

Meta code generators that build code generators. This technology makes it possible to construct code generators for very complex modeling languages. The only examples we know are iMatix tools such as XNF and ASL.

Our first versions of GSL were born in 1995. We moved onto XML in 1997. The problem of writing the code generator scripting language (GSL) is largely solved, and at iMatix we don't consider this a priority task. At some point we want to rewrite GSL to be a lot faster, but what interests us now is how far we can push MOP to solve new programming challenges.

10. Myths about Code Generation

Code generators are often seen as a technological burden, rather than useful tools. I suspect that this is because primitive code generators (which covers most code generators) are so painful to use. Some of the common myths about code generation are:

Code generators only work for simplistic cases. This is often true, but only because most code generators are simplistic.

Generated code is unreadable, and generated code is low quality. This is often true because their authors focus on the application-specific problem, rather than on making the template easy to modify and improve. In a template-based code generator the code can be as good as or better than hand-written code.

Code generators are expensive to make. This is typical of hard-coded code generators where the slightest change to the template means modifying, compiling, linking, and distributing a new release of the code generator.

Code generators are too much effort to use. This says more about tool designers than about the problems that the tools solve.

Code generators are complex. This is true: all abstractions are internally complex - look at the work required to write a good web browser or web server.

GSL solves most of these problems, and even a beginner can make useful code generators for interfaces, database management, XNF solves the last problem - it applies MOP to MOP itself, letting us make sophisticated code generators much more rapidly than by hand.

11. The Correctness of Generated Code

When you use a tool that produces large amounts of code for you, you will naturally ask, "how do I know the code is correct". You need to be able to trust your tools.

A code generator, luckily, is not random. It is like a simple compiler: take high-level construct, translate into target code. If there is a bug in this translation step, you will get target code that has bugs.

Luckily, it's easier, not harder, to get correct code from a machine than by hand. I'll explain how we do this:

Overall, we are quite strict about how we build our code generators. That makes bugs in the code generators rarer, and easier to find.

When we start a new code generator, we build it gradually, and of course we inspect the code that it produces as we develop it.

As we make the code generator more sophisticated we build a regression test suite that lets us catch any new errors in old code very rapidly.

In any programming environment, the key to producing good code is to test heavily, and to use appropriate automation, i.e. tools. When we say the "correctness of generated code", we really mean, "how to avoid bugs in the code generator".

Since we use the MOP approach to build the code generators themselves, we get very good code generators, cheaply. It is the same concept as a "self-hosting" compiler. On many projects where we've used MOP, I'm able to deliver hundreds of thousands of lines of code, and say, with confidence, "there is not a single bug in this code".

12. Starting with GSL

GSL, XNF, and our other tools are open source. You can download them from If you want to try the examples that follow, you should download the Base and GSL/4 packages and build them.

Our first step is to make a "hello world" program in GSL. It's quite simple. Make a file called hello.gsl that contains one line:

echo "hello world"

To run this, use the following command:

gsl hello

GSL is a simple language and you'll not have any difficulty understanding its syntax, except in a few places where it does specialised work. It will take you a little longer to understand what you can do with GSL, but that is the real point of these articles. GSL is not as rich as other scripting languages. It is a code generator scripting language, not a programming tool. It lacks some control structures, and it runs a little slowly.

Initially, GSL looks like any other scripting language. I can write little scripts like this:

amount = 1000
year = 2006
while year < 2026
    amount = amount * 1.05
    year = year + 1
echo amount

Which calculates the value of my savings account if I were to leave it untouched for twenty years, and the interest rate were steady at five percent. Note these syntax aspects:

  • variable = expression - Assign a value to a variable
  • while condition... endwhile - Repeat a block while the condition is true

To run the above program, assuming it was saved in a file called interest.gsl, I type this command:

gsl interest

This executes the script and tells me that if I am really patient, I'll be rich one day. Now I'm going to change this little program to make the same kind of calculation for different amounts, rates, and years. Where do I put these different terms and rates? The answer is, in an XML file. The file is called deposits.xml:

<?xml version="1.0"?>
<deposits script = "interest.gsl" >
    <deposit amount = "1000000" rate = "5" years = "20" />
    <deposit amount = "500000" rate = "4" years = "10" />
    <deposit amount = "2500000" rate = "6" years = "15" />

We change our script to give the result below.

.template 0
 for deposit
    year = 1
    accumulated = amount
    while year < years
        accumulated = accumulated * (rate / 100 + 1)
        year = year + 1
    echo "Original amount:" + amount + " becomes: " + accumulated

Note these syntax aspects:

  • .template 0 - Start script (non-template) block * for childname - Repeat block for all instances of child item called childname

We will run the new interest calculation script using this command:

gsl deposits.xml

Note the change of command syntax. We first ran the GSL script. Now we're running the XML file. This is one of GSL's features - you can run XML files as if they were scripts. It's the 'script =' setting that does the trick, working much like the hash-bang #! command in Linux.

Any GSL script, no matter how simple, works with an XML document loaded into GSL's memory as a data tree. In our first interest.gsl script, the data tree contains just this:

<root script = "interest" />

GSL automatically creates this data tree when we ask it to execute a GSL script.

If, on the other hand, we ask GSL to execute an XML file, it loads this XML file into its data tree. Assuming we also asked for it, it will then execute a GSL script against that XML tree. Technically speaking, GSL searches the root item - which can have any name - for an attribute called "script". We can put attributes into the root item in several ways. One is to simply add them to the XML file, as we did. The other is to place them on the command line, like this:

gsl -script:interest deposits.xml

All variables that we define and use are stored in the data tree, somewhere. This is the only data structure that GSL scripts work with, and it can get very complex. For many people, understanding this complexity is the most difficult thing about using GSL - hierarchies of data are one of those things most human brains do not handle very well. We use abstractions like XNF to make this simpler, but that is something I'll discuss later.

13. Templates and Scripts

GSL uses the term "template" to describe text that is output as generated code. GSL works in two modes - script mode, and template mode. When you execute a GSL script directly, as we did in the first example, GSL starts in script mode. When you execute a GSL script indirectly, through an XML file, as we did in the second example, GSL starts in template mode. Try removing the .template 0 and .endtemplate lines and you'll see what I mean. The script just gets copied to the output stream

  • the console, by default.

In template mode, GSL commands start with a dot in the first column. In script mode, all lines are assumed to be GSL commands unless they start with ">" (output) in the first column, in which case they are handled as template lines.

Script mode is useful when you are doing a lot of GSL scripting work. Often you need to prepare data, check the XML tree, and so on, before you can start to generate code. Template mode is useful when you want to output a lot of data, or actually want to generate code.

You can mix GSL commands and template code by putting a dot at the start of lines with GSL commands. Like this:

.while year < years
.   accumulated = accumulated * (rate / 100 + 1)
.   year = year + 1

I'm now going to generate a little HTML report of how the different calculations. The listing below shows the third version of interest.gsl:

.output "deposits.html"
<title>So You Want To Be A Millionaire?</title>
<h1>So You Want To Be A Millionaire?</h1>
<tr><th>Original amount</th>
<th>Interest rate</th>
<th>Term, years</th>
<th>Final amount</th>
.for deposit
.   year = 1
.   accumulated = amount
.   while year < years
.       accumulated = accumulated * (rate / 100 + 1)
.       year = year + 1
.   endwhile

Note these syntax aspects:

  • output expression - Start sending output to the filename specified * $(name) - Insert value of attribute in output text

To produce the HTML report run the same command as before:

gsl deposits.xml

And then load deposits.html into your browser to see what it looks like.

If you're a web developer with any experience, you will see right away what's happening. We're generating a web page dynamically, just like a hundred other web tools. But there are significant differences:

Unlike a dynamic web page, here we explicitly specify the output file ourselves, using the "output" command. We can output zero, one, or a hundred different files if we want to.

We're working off a data tree that can be as complex as we want. Each "for" loop opens a new scope, acting on a set of child entities. A dynamic web page works off some flat data, coming from the browser or a database. You can make web pages that work on a hierarchical data set, but it's extra work.

GSL lets you load and navigate XML data so easily that you don't even realize you're busy. The combination of an explicit script language like GSL plus a hierarchical XML data tree works well.

14. Conclusion

A professional programmer always looks for ways of producing better code, at a lower cost. We have seen evolution in programming languages but these tend to get very complex as they try to solve wide types of problems using high-level constructs. Some of these languages take years to learn properly.

The author and his colleagues have developed an alternative way of writing better code, which they call "Model-Oriented Programming", or MOP. MOP turns high-level models into code, in the same way as an HTML-to-PostScript converter produces code.

iMatix provides tools that let you construct your own MOP models and the code generators that make them work. Using MOP in a project can give significant benefits. The author's team is currently building an open source messaging server using MOP techniques. The 500,000 lines of C in the server are produced from 60,000 lines of high-level modeling languages, including state machines, protocol grammars, class definitions, and project definitions. The iMatix MOP tools are all available as open source from

iMatix MOP is based on code generation techniques that we have been using and developing for twenty years. The core technologies are GSL, a code generator scripting language, and XML, which is the basis for the modeling languages that we use. A single MOP definition produces code, documentation, test cases, and so on.

MOP development can produce spectacular results. We have benchmarked our development costs against other highly-skilled teams and found a difference of 5 times and more. MOP code is much shorter than hand-written code, so costs less to maintain. While it is near impossible to hand-write huge applications without any bugs, very large MOP programs can be bug-free before they have come out of beta testing. This is partly because MOP code is easier to write correctly, but also because we can generate test cases automatically.

In our world, XML becomes a model-oriented programming language that achieves a new level of expression - a true fifth generation language - without becoming particularly complex, slow, or narrow. Unlike many of the approaches taken to overcome the cost of software engineering, we offer an approach that is easy to learn, based on simple concepts, and yet totally plastic, covering any software domain, language, and operating system one can imagine.

This is a radical evolution - possibly a revolution - for software engineering.