Daily Life in an Ivory Basement

On top of dreamhost dropping off the 'net just when I posted a bunch of screencasts... our socal-piggies meeting nearly got whacked because this month's organizer uses Yahoo, and most of the messages going through my mail server (which hosts the mailing list) were filed as "spam".

Now Yahoo is actively bouncing my e-mail.

... You can see for yourself that 69.55.232.123 isn't a known spam relay, isn't open, etc. It's just Yahoo looking at the volume of mail being sent and going "hey! wait a sec!" and listing me as a spam host.

Grr.

--titus

I put together an unofficial screencast about the Google Summer of Code based on a pitch I gave to the Michigan State undergrad CSE population. Enjoy. Please forward on to anyone who might be interested...

gsoc-2008-screencast.mp4.

--titus

The simple application I demoed at PyCon '08 during my talk on the OLPC and testing is now available: I call it "peekaboo".

peekaboo is a way to watch your code being executed in another process using sys.settrace, figleaf, and XML-RPC.

The two screencasts below should explain it.

The first screencast, peekaboo-screencast.mp4 , is a simple demo of peekaboo running on a really simple demo app. To try it out yourself, take the peekaboo code and run 'peek_view.py' and 'demo_app.py', in that order; then visit 'localhost:8080' (for the viewer) and 'localhost:8081' (for the demo app). Click around. Watch stuff happen. Whee.

The second screencast, peekaboo-olpc-gui-driving.mp4, is a demo of using peekaboo to watch code execute in the One Laptop Per Child GUI, Sugar. In this screencast I use twill and xmacro to drive the OLPC GUI and then retrieve trace information to the peekaboo viewer. The OLPC GUI driving code isn't "released" but then again it's trivial. Ask me if you want to see it before I get a chance to polish it up a bit.

Ultimately I'd like to use peekaboo to host "exploratory test" parties for the OLPC, where we all run through a bunch of activities and try to get every last line of code covered by our exploratory tests. Well, that, and I'd like to get a bunch of automated testing into the OLPC GUI!

Anyway, peekaboo is basically a really simple mashup of figleaf and a Web viewer, and it's not anything special in terms of code; the trickiest bit was getting the XML-RPC server to run inside of Sugar ;).

You can go download the peekaboo source if you like.

Note that peekaboo is in no way, shape or form ready to be released, but I just don't have time to work on it at the moment so I'm pushing it out the door for those who are interested in seeing the raw, ugly code!

Comments welcome, of course!

--titus

p.s. Apologies for the heavy breathing in the second screencast; peekaboo gets me excited!

p.p.s. No, seriously, it's late at night here and I just want to go home... so I don't want to redo the screencast!

At PyCon '08, I gave a talk on testing and the OLPC project where I referred to the "Testing Death Spiral". My accompanying slide, which aimed to be simple rather than comprehensive, had this scenario:

1. Write a bunch of code & manually test it.

   (Good so far.)

2. Start adding features over here.

3. Watch code break over there.

4. Rinse, lather, repeat

   (Where do you think this ends?)

---

OK, so that format doesn't really work in a blog post, but hopefully you get the gist of the scenario. This is a scenario I see a lot: a project gets hacked together & works well enough that people start using it; then the project starts to expand. Many new features are added. However, as these new (and presumably solid) features are being added, the old code becomes increasingly ignored, uncovered by manual testing, and fragile.

This is a simple consequence of an inescapable fact: the amount of testing needed to detect regressions scales with the number of features. Forget about finding new bugs in the code you just wrote -- I'm talking about breaking existing code.

I have seen people attempt to escape this scenario in a number of ways: improve the architecture and reduce internal linkages; open source it; release early, release often; alpha- and beta-test it; stop adding new features; and probably many more. These are all good thoughts, but they are all doomed to failure [1]. Nonetheless, I wish you well.

The only solution I have found is this: write automated tests.

Before I continue, let me say: automated tests are not a panacea. Writing good code is hard, getting your project "out there" is important, exploratory testing is mandatory, and writing appropriate automated tests is hard; there's a lot more to building software than writing good, automated tests. I stress that every time I talk about test automation. I just think automated tests are necessary [2].

Let us suppose, for the sake of argument, that you have some software that is actively evolving. Furthermore, this software has no automated tests. Every time you add a feature, you test the bejeezus out of that feature in order to satisfy yourself that it works. You do this for every new feature that is added, and thus consider your software to be solid.

I now have two questions to ask:

• are you adding features in isolation from each other? that is, is your architecture such that each new features only uses non-state-changing code from elsewhere in your project?

  (if the answer is yes -- are you sure?)

• do you completely control the packages, libraries, compiler, operating system, and hardware that your software runs on?

  (if the answer is yes, do you plan to never, ever, change any of those components? and have you discussed these plans with anyone outside your development team? and do you believe your managers?)

I like to summarize these questions this way: are you feeling lucky, punk?

If the answer is "yes" to all of the above, then congratulations -- you are Apple, stuck at one point in time, and never planning to release a new piece of software or hardware :). Hopefully you'll do better than Apple did before they decided to change and adapt...

If the answer is "no" to any of the above, I encourage you to read on.

I assert that even with a perfectly decoupled architecture, brilliant software engineers, and nigh-complete control over the software and hardware that you use -- in itself a dream software development situation -- you will eventually need to add features that crosscut that architecture, and you will also need to upgrade the compiler, libraries, language version, operating system, and hardware. In order to make sure that your software still works, each time you add a feature or change a component, you will have to retest every feature and every piece of code. And, if you have no automated tests, you will have to do this manually. Every time.

If you have automated tests, however, your development process could look something like this:

1. change code
2. run tests
3. commit
4. test manually, do exploratory testing
5. find bugs, write automated tests to reduce bugs
6. goto 1

Even if you don't add any new features, this process applies to library, compiler, platform, and hardware changes. At the least, you will be able to quickly determine if you've broken something that you're testing for; at the best, you will be able to quickly and confidently release new versions of your software.

Fundamentally, then, automated testing is important for software maintenance. And since the cost of software maintenance is a significant portion of the cost of developing the software in the first place [3], it behooves you to pay attention to anything that will reduce the cost of software maintenance. This is without even considering other aspects of test utility, like increased developer velocity, ease of refactoring, increased confidence in your software, etc.

This maintenance situation is the scenario that led me into testing (or, if you prefer, "illuminated me as to the importance of automated tests by whacking me over the head with a clue bat".)

Let me assure you that this maintenance situation doesn't just apply to large bodies of code, either. I maintain a number of small projects and having automated tests means that I simply don't release code with regressions. Moreover, when my small projects "grow up" into bigger ones -- or, more frequently, are used in larger projects -- I'm not stuck in a situation where I suddenly have to write a bunch of tests to achieve stability. I always try to grow my test framework organically with the project, because I will never have the time to put into writing tests from scratch for my bigger projects.

---

So, automated tests are important for maintenance, and they are critical for making sure that your old code still works while you focus on new code. Without automated tests, you will be doomed to releasing increasingly buggy software as your body of code increases and the average level of testing decreases.

Does this actually happen?

---

This is precisely the scenario that led to our consulting work with ARINC, which went well. (As in, they're adding new features with great confidence after we helped them adopt automation tools and practices.)

---

This is also the scenario that leads to what Jamie Zawinski named the Cascade of Attention Deficit Teenagers. Open Source projects, facing a continually increasing number of bugs, often opt to completely rewrite their components in the expectation that this time, they'll get it right. This completely ignores our experience with software rewrites, which suggests that (barring brilliance and luck) any rewrite will contain as many bugs as the original software -- they'll just be different bugs. (As JWZ points out, though, it's more fun to write new code than to fix the crud someone else wrote before...)

---

And, finally, it is also the scenario faced by the One Laptop Per Child project, which has built a tower of cards on open source software. Their build system pulls in about fifty distinct packages live from the Internet, compiles them all, and then layers the Sugar user interface on top of them.

There is no automated testing in place.

---

OK, back to the Software Testing Death Spiral. What happens to projects that lack both automated tests and an exponentially increasing team of testers? Starting somewhere in the middle of the process:

1. They manually test the new features and bug fixes that they've just added.
2. They release their software.
3. Their software breaks in unexpected locations, bug reports are filed, and (optimistically) those bugs are fixed. Go to #1.

The inevitable consequence is a death spiral, barring only a complete rewrite (which will possibly fail, or likely lead to a product that's just as buggy, but with unknown bugs), trashing of the project, OR -- and this is an optimistic scenario -- the adoption of automated testing.

---

Here are a few straw men, with moderately snarky replies:

"We don't test, and we don't use version control. Which is more important?" Version control. But you're doomed, anyway.

"We don't have time to test." Why do you have time to write software, but not time to make sure it works?

"We don't have the expertise to build good tests, and/or we can't afford the tools, and/or we don't know how to use them." This is a pretty realistic scenario, actually. May I suggest: hire consultants, or read some good books, or dedicate your young new hire to learning the tools?

"We don't like to test." Well, at least you're honest ;). I would summarize your choices like this: either you can write crappy software, or you can learn to like testing. The former will most likely doom you to the rubbish bin of history. The latter gives you a better chance of "making it".

"We really do plan to rewrite our software in two years." Points for honesty, again! I think you're rolling the dice -- many software projects fail, but maybe you'll do better. Might I suggest an incremental rewrite rather than a complete rewrite? (For that you'll need testing, though...)

"We wrote a bunch of automated tests. They didn't help us. Ahh, a problem based in actual experience! I would like to suggest -- with no background in your particular problem -- that you try out several different kinds of tests, like functional tests or regression tests, and see what does help you.

"How do I test, if I don't know what the right answer is, anyway?" How do you know you got the right answer, then? If your customers don't care if you're right, then you've stumbled into a gold mine, but I daresay it will end badly. (This straw man was actually sighted at PyCon -- sorry, MC.) I hear this a lot in research, actually, but it's still nonsense. Perhaps another blog post in there...

"I can't convince my boss/team leader/PI that it's important to spend the time to write tests. (I even sent him/her your blog post.)" You could go one of three ways: try harder, integrate testing into your personal development strategy and view this situation as an opportunity to "manage up", or quit. The middle option is the interesting one: you can quietly start writing automated tests to "fence in" your own code, and explain to your boss that this is just how you code -- it's like using emacs instead of vi -- and you're not insisting that anyone else follow suit. Hopefully your productivity will not decrease much, while your reliability will increase. Good fellow programmers may follow suit and at some point your manager might realize that you've all evaded his dictat. Or not. But it beats working on untested code!

"I am but one lone programmer, and I can't convince my team to write/use tests. (I even sent them your blog post.)" See previous question/answer: you will find that most worthwhile programmers are in favor of anything that increases their productivity and reliability.

"There's so many other things to straighten out on my project before I can even think about what tests to write." I sympathize, I really do, but if your project is so undirected that you can't even figure out what it's supposed to do (and write tests for it) then you have far bigger problems than bad code to worry about.

"I took your advice and wrote tests. Then we changed a bunch of stuff, and now all the tests break, and I don't have time to fix them. What do I do now?" Hmm, this is a common complaint. First, try to separate out a subset of the tests that are of immediate use to you (as in, they pass and/or they exercise a lot of your code). Keep that subset working. Second, don't be afraid to simply delete your old tests. Tests should not be a maintenance headache; if you like and use tests, but don't see the point of maintaining a bunch of your broken tests, get rid of them! Then put new ones back in as necessary.

---

There really are a bunch of other reasons to write automated tests, too. For example, consider:

• cross-platform development is dramatically simplified when you have a moderately thorough test suite. In particular, you can develop on your favorite machine, in your favorite programming environment, and let the continuous integration boxes run and test your code on all the other machines.
• setting up new development environments and development machines is much easier when you can simply ... run the tests to figure out if it's all working.
• integrating new people into the development team is much easier when they can run tests to figure out if they just broke something.
• releasing "a quick bugfix" is a lot easier when you can be fairly confident that your quick new release is no more broken than your last release.

If these aren't enough to make you think seriously about testing, then I give up!

---

There's no real conclusion to this :). I'll talk more about the OLPC stuff later.

Don't get me wrong: testing is hard. Testing effectively is even harder. There are ways around this, but the best way to start may be to simply power through: write a bunch of tests, and ruthlessly discard those that don't help. Then refine your method over time. I have some advice to offer here, too, but that's for another blog post...

And remember... Darth Vader recommends testing!

--titus

p.s. Thanks to Tracy Teal, Lisa Crispin, Alex Gouaillard, Kumar McMillan, Shannon -jj Behrens, and Doug Hellmann for comments!

[1]	E-mail me if you think I should write about why :)

[2]	I can blog about "necessity" vs "sufficiency", too. Let me know.

[3]	I've heard estimates of 80-90% of the total cost of development for a successful software project, i.e. initial feature development is 10-20%, maintenance is 80-90%, but I have no good references for this.

Here are some of the materials from my PyCon activities:

The tutorial source code (which is really just a cut down version of my PyCon '07 talk's source code; see the README and my blog post from back then).

The tutorial slides (PPT)

My OLPC/testing talk (PPT).

(Sorry about the PPT format; printing to PDF increases the size 10x.)

As a bonus, here is a pair of example fixtures for resetting PostgreSQL databases. (See utils.py in the two subdirectories.)

I will post a demo/screencast and the source code from my OLPC/testing talk as soon as I clean it up a bit.

--titus