The question is a deep one. What is the Literate Programming Life Cycle? Why is it so difficult? What are the three barriers and how do we cross them?
Here's most of the original question.
"The issue of literate programming is an issue of writing a program
that LIVES rather than writing a program that WORKS. In a commercial
setting you pay to train new people on programs but in an open source
setting there is no training. ..."
"... But if your program needs to live forever then you
really need literate code."
Recently, I did some major overhauls of two literate programming exercises. I revised the pyWeb tool
to better handle LaTeX output, as well as add unit tests and -- consequently -- fix some long-standing problems. Also, I revised the COBOL DDE
parser to better handle numeric data, replace the old FixedPoint module with Decimal, add unit tests and -- of course -- fix other bugs that showed up.
Based on my recent experience, I have some advice on "Full Life-Cycle Literate Programming".
A Life Cycle
In order to identify the barriers, we need to look at the deliverables and the software development life cycle that produces those deliverables. Let's break the software development life-cycle down as follows.
- New Development
We'll presume that each of these efforts includes some elaboration of requirements, some design, and some transition to operational use. We only care about the coding part of the job, so we're not going to dwell on all of the other activities that are part of Application Life Cycle Management.
The question is about that transition from New Development to Maintenance or Adaptation. Doing new development seems somehow easier than maintenance or adaptation. How do we work with an established Literate Program?
New Development of a program is always a delicate subject. We have an explicit goal of creating some deliverable. We'll look at the deliverables next. First, we'll look at the conflicting forces that must be balanced.
- It must satisfy the need. There are requirements for the program's behavior, interfaces and implementation. Above all it must work.
- It must use appropriate resources. The data structures and algorithms must reflect sensible engineering choices. There's no call for "micro-optimization" of each silly piece of syntax. However, the algorithm's (and data structures) should be minimized.
- It must be adaptable.
- It must be maintainable.
- It must meet other organizational needs like cost, time-to-develop, language and toolset, infrastructure requirements, etc.
One can maximize one at the expense of others. For instance, one can reduce development costs to the minimum by creating a mess that's neither adaptable nor maintainable. Indeed, one can create software very cheaply if one starts relaxing functional requirements. Software that doesn't work well can be very cheap to create.
Forward vs. Reverse Literate Programming
As a digression, we'll note that some folks recognize two broad approaches to literate programming (LP). This isn't the whole story, however. Ordinary LP encourages the author to create a document that contains and explains working software. A simple tool extracts a nice final publication-ready document and working code from the author's original source document.
is the technique used by tools like JavaDoc, Sphinx, Epydoc, DOxygen. This usually takes the form of detailed API documentation, but it can be richer than simply the API's. In this case, comments in the source code are extracted to create the final publication-ready document. In Sphinx the author uses a mixture of source code plus external text to create final documentation. This isn't as interesting, since the resulting document can't easily contain the entire source.
We can assign the retronym "Forward Literate Programming" to ordinary LP to distinguish it from Reverse LP.
Code-First Literate Programming
There's an apparent distinction between two variations on the Forward LP theme: Document-First and Code-First LP. In Document-First, we aspire to a noble ideal of writing the document and the code from first principles, from scratch, "de novo", starting with a blank page. The code-first approach, on the other hand, refactors working code is into a literate programming document.
One can argue that code-first refactoring is A Bad Thing™ and subverts the intent of literate programming. The argument is that one should think the program through carefully, and the resulting document should be a tidy explanation of the development of the ideas leading to the working software.
However, Knuth's analysis of "The original Crowther/Woods Adventure game, Version 1.0, translated into CWEB form" (at ADVENT
) shows that even ancient Fortran code can be carefully analyzed and retro-actively transformed into a piece of literature.
Working forward -- starting with a blank sheet of paper -- isn't always the best approach. The bad ideas and dead-ends don't belong in that explanation. All of the erasing and rewriting should be left out of the LP document. This means that the document should really focus on the final, working, completed code. Not the process of arriving at the code. Why start with a blank page? Why not start with the code?
In short, code-first LP isn't wrong. Indeed, it isn't even a useful distinction. If the resulting document (a) contains the entire source and (b) stands as piece of well-written description, then the literate programming mandate has been satisfied.
Center of Balance
Literate Programming strikes a balance among the various development forces. It emphasizes working software with abundant documentation. It does not emphasize the short-term cost to develop. It does, however, emphasize the long-term value that's created.
Interestingly, the idea is to minimize the labor involved in creating and maintaining this documentation. To some folks, it seems odd that all that writing would somehow be "minimal". Consider the alternative, however.
We can try to create software and documentation separately, claiming it's somehow easier. First, we write the software, since that's the only deliverable that matters. Second, we slap on some extra documentation, since only the software really matters. While satisfying in some respects, most folks find -- in the long run -- that this is unworkable. They often diverge.
When the code and the comments disagree, probably both are wrong.
The goal of LP is to prevent this.
Literate Programming seems like a lot of work. But it's work we have to do anyway. And a non-literate approach is simply more work. Almost any approach that seems to create software "quickly" doesn't create any enduring value. Why not?
The point of all software development is to create a two-part deliverable.
- The working software
- Some supporting justification or reason for trusting the software
The justification can take several forms: test results, formal proof, API Documentation ("Reverse Literate Programming"), an explanation (separate from the code) or a Literate Programming document.
In many cases, our customers want most of the above. Folks don't expect a formal proof, but they often demand everything else.
Claiming that the software can exist without the supporting justification is to reduce software development to a hobby. The worst-run of amateur software development organizations do tolerate a piece of software without a single test or scrap of documentation. That only proves the point: if your organization tolerates junk software without supporting documentation, it's one of the worst-run of organizations; feel free to quit.
The point of LP is to create the software (and supporting documents) from a single LP source document. LP seeks to minimize the effort required to create software with supporting documentation that actually matches the software.
I'll emphasize that.
Literate Programming seeks to minimize the effort required to create software with supporting documentation
If we have to produce software, tests and explanations, clearly it is simpler to have a single source file which emits all of that stuff in a coherent, easy-to-follow format. While it's clearly simpler, there are some barriers to be overcome.
If It's So Much Easier... ?
The Jon Bentley issue with LP is that it doesn't feel easier to write a coherent document because we aren't all good writers. Bentley notes that there are good writers and good programmers and that some folks are not members of both sets. I think this misses the point. We're going to produce documentation, no matter how good a writer we are.
Most people do not see LP as simpler. They see it as a lot of work. Weirdly, it's work they already do, but they choose to keep the program and the explanation separate from each other, making it more work to keep them in synch. I can see why they claim it's more work.
If it's easier to do this in one document, why doesn't everyone simply create a literate program?
Generally, we've got three kinds of barriers that make Literate Programming hard. First, the tools at our disposal don't really support an LP kind of development effort. We get very used to intelligent syntax coloring and code folding. We find tools which lack these features to be harder to use. Second, we're working in multiple languages in a single document. Finally, it takes some experience to get settled into an LP mode.
The Tool Barrier
The first of the barriers to effective literate programming is the tool pipeline. The complaint is that "having to ‘compile’ my markdown to code, then compile my code to test it, all seems like too much hard work".
This is interesting, but specious. The multi-step process is what scons
, make, ant
are for. A simple SConstruct
file will handle web, weave, publication, compilation and unit test in a single smooth motion.
There are a lot of tools involved in literate programming. We've introduced an additional markup language into the mix, creating additional steps. This isn't any more complex than working with any compiled language. We often forget that the C compiler is really a multi-stage pipeline. Our LP tools -- similarly -- are multi-stage pipelines.
Also, for Python and F# programmers, there's something else that Seems Very Important™. It isn't. F# and Python have console interfaces (sometimes called the Read Evaluate Print Loop, REPL); this clutters up the problem with an irrelevant detail. Console hacking is helpful, but it isn't literate and it's barely programming.
The Language Barrier
In addition the tool barrier, we also have a language barrier. When we're doing literate programming we're working in at least three different languages concurrently. This makes our life seem difficult.
- Literate Programming Markup. This might be CWEB, pyWeb or any of a number of LP markup systems.
- Target Document Markup. This might be LaTeX, RST, Markdown, DocBook XML or some other markup.
- Target Programming Languages. For classic, Knuth-style projects, there's only a single language. However, for many projects this will not be a single language. For example, in a web environment, we'll have program source, SQL, HTML, CSS, and possibly other languages thrown in.
It's difficult to sort this out from an IDE's perspective. How to handle syntax highlighting and code coloring? How to handle code folding and indexing the document as presented?
The old-school techniques of decomposing a big document into small sections still applies to literate programming. The document sections do not in any way correspond with the final program source, making the LP document tree far, far easier to work with.
The Mental Barrier
The final barrier is entirely mental. This is really one of experience and expectation.
It's hard -- really hard -- to step back from the code and ask "What's this mean?" and "How would I explain it?"
Too often, we see a problem, we know the code, and we understand the fix -- as code. This is a skill as well as a habit we build up. It's not the best habit because the meaning and explanatory power can be ignored or misplaced.
Stepping back from the code seems slow. "It's a one-line change with a 10-paragraph explanation!" developers gripe. "I could make the change now or spend hours explaining the change to you. The value is in making the change and putting it into production."
And that's potentially wrong.
Only a very small part of the a developer's value is the code change itself. If code will be in production for decades (my personal best is 17 years in production) then the 10-paragraph explanation will -- over the life of the software -- be worth it's weight in gold. A one line fix may actually be a liability, not an asset.
I think the approach has to be the following.
- Create a Spike Solution. Something that works, is incomplete, but shows the core approach, algorithms and data structures.
- Outline the next more complete solution using LP tools. The component structure, the logical model, the basics of the first sprint.
- Create a publication pipeline to process the LP source into document, code and tests, and run the test suite. A kind of the Continuous Integration daily build. This is easily a double-clickable script, or "tool" in an IDE.
- Fill in the code, the unit tests, and the necessary packaging and release stuff. Follow TDD practices, writing unit tests and code in that order. What's cool is being able to write about them side-by-side, even though the unit tests are kept separate from the deliverable code in the build area.
- Review the final document for it's explanatory power.
Consider a number of things we do in comments that are better done outside the comments.
- TODO lists. We often write special TODO comments. These can go in the proper Literate Programming text, not in the code.
- Code samples. In JavaDocs, particularly, sample code isn't fun because of the volume of markup required. LP code samples are just more code; you can make them part of small "demo" or "test" structures that actually compile and are actually tested. Why not?
Consider a number of things we don't often do well.
- Background on an algorithm or data structure. Footnotes, links, etc., are often slightly easier to write in word-processing markup than comments in the code.
- Performance information on the choice of a data structure. Merely claiming that a HashMap is faster isn't quite as compelling as running timeit and including the results.
- Binding unit tests and code side-by-side. Current practice keeps the unit tests well separated from code. (Django framework models are a pleasant exception.) What could be nicer than a method followed by unit tests that show hot it works? You may write the tests first, but the code-first explanation is sometimes nicer than the test-first development.
I think that LP isn't all that hard, but we have three barriers to overcome. We don't have exceptional tools. We have a complex welter of languages. And we have bad habits to break and transform into new habits.