Making Software Reliable: The Importance of Testability | Hacker News Reader

Making Software Reliable: The Importance of Testability

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41 pointsmisoukrane23 comments

Using interfaces (whether literal interfaces in your language or the general concept) to isolate operations that rely on dependencies is so critical IMO. It makes testing so much easier, takes little additional upfront effort, and generally makes nicer to read code (subjectively).

For example, if I have my `MailSender` interface with an `SMTPMailSender` implementation but then I want to switch over to using the AWS SDK for interacting with SES for auth purposes, I can just implement my interface as always and create my new `SESMailSender` and plop it in place and all my code just works. This isn't just for testability, but also general modularity.

It takes so little additional upfront effort as well. Even just declaring an interface helps you isolate what functionality should actually be publicly exposed. I really don't think this creates any serious development drag. On top, it makes your tests so much easier to write, and tests are usually a good bang for your buck for the major cases.

If you truly only need a single implementation and not even the ability to have one for testing, then that's fine! Keeping in mind that adapting this to an interface in the future should be simple and do that when it comes up.

Combine that with the abstract test pattern, and teaching your harness how to instantiate an instance of a new concrete implementation is often all that’s necessary.

I'm actually rather negative on automated testing.

Every project, that has gotten rid of the Q/A team to rely on automated tests, hasn't exactly gone well. Windows being the obvious example, but there are others. I'm not seeing the quality improvement that should be there. And even when there are tests, elementary mistakes (like the time Windows 10 would delete your Documents folder) slip through the pipeline and screw everything up. How is it that some of the most reliable software in the world (like the moon landing suite, the IRS tax system, or stock trading mainframes, or the Windows NT kernel) were written before automated tests; yet some of the buggiest software in the world is the most well-tested (like Windows 10, or Google Drive, or just about every SPA)?

In a team setting, they certainly seem to catch bugs. But is that because the tests have caught so many bugs that would've slipped into production; or is it that the team can afford to be careless now and writes sloppier code to begin with? I'm increasingly suspecting it's the latter, and while the tests catch most of the sloppiness, sometimes the slop passes...

My view is that automated testing is not a substitute for QA, but an additional tool. It lets QA focus on harder to automated tasks.

For unit tests, a developer is going to try something out anyway, so capturing it in a unit test for the future should be just a little extra work. Also, writing a unit test means the developer has minimally used what they are writing.

Higher-level (system) testing, especially with GUIs, can be more work than the value added. It is a cost trade-off, but ultimately, a human adds value not matter how much automated.

AI will help too, but these are also tools. Drop QA and you are trading off costs for quality.

IMHO, not backed up by research.

Absolute one is not a replacement for the other.

I worked at a place that did pretty strict TDD and had a dedicated QA person embedded on each team. Our high-level systems tests severed more as a smoke tests and only ever tested the happy paths. Our integration and units tests of course covered a lot more, but QA was essential in covering corner cases we never thought about as developers.

There is one programming language that fascinated me (maybe it was Ada) where it tried to have some basic tests inline with the code, by defining basic guidelines for legitimate results of the function.

For example, you could make a function called `addLaunchThrusterAndBlastRadius` (I know it make no sense, but bear with me), and then right alongside declaring it was an integer, you could put a limit saying that all results that this function can return must be greater than 5, less than 100, and not between 25 and 45. You could also do it when declaring variables - say, `blastRadius` may never be greater than 100 or less than 10, ever, without an exception.

I wish we could go further that direction. That's pretty cool. Sure, you can get that manually by throwing exceptions, but it was just so elegant that there was just no reason not to do it for every function possible.

Modern C++ supports this pretty extensively via the type system. You can define/construct integer types with almost arbitrary constraints and properties that otherwise look like normal integers, for example. The template / generics / metaprogramming / type inference facilities in C++ make it trivial. Some categories of unsafe type interactions can be detected at compile-time with minimal effort, it isn't just runtime asserts.

This is common in C++ for reliable systems. You infrequently see a naked 'int' or similar (usually at OS interfaces), almost all of the primitive types are constrained to the context. It is a very useful type of safety. You can go pretty far with a surprisingly small library of type templates if the constraint specification parameters are flexible.

(This is also a good exercise to learn elementary C++ template metaprogramming. A decent constrained integer implementation doesn't require understanding deep arcana, unlike some other template metaprogramming wizardry.)

You can do that in Swift (and, I suspect, lots of languages).

Swift has a fairly decent assertion/precondition facility, as well as reflection[0]. They also have a decent test framework[1] (and I know that some folks have extended it, to do some cool stuff).

Some of these add significant overhead, so they aren't practical for shipping runtime, but they can be quite useful for debug-mode validation.

Assertions are a very old technique. I think I first encountered them in Writing Solid Code, in the 1990s. Back then, we had to sort of "roll our own," but they have since, become integrated into languages.

Of course, all the tools in the world, are worthless, if we don't use them.

[0] https://developer.apple.com/documentation/swift/debugging-an...

[1] https://developer.apple.com/documentation/xctest/

> Of course, all the tools in the world, are worthless, if we don't use them.

True...

I wonder if there would be any way, to simplify the syntax, and require basic assertions to be on every function. There might be a super easy cop-out like `any`, but at least by being forced to type it, you become aware of what it means and that it exists.

Almost like:

`public any int addXandY (any int x, any int y) {`

I also wonder, if there could be such a thing as an `assertion exception` (or whatever it would be called). Maybe it would just make things a mess, but I'm just thinking out loud. Basically, you could have a function that behaves a specific way 90% of the time, but for that 10% of the time where it doesn't work, you could pass that assertion exception to override. Maybe that would just be awful... or it would keep functions much cleaner?

Maybe you wouldn't even call it an exception. You'd just have multiple sets of assertions that could be applied to each function call.

I just had another thought. What if you could have a bank of assertions? Like this pseudocode:

```

assertion acceptableBlastNumber (int x) { x < 25; x > 5; }

assertion acceptableBlastRadius (int x) { x > 500; x < 1000; ! (x > 750 && x < 800) }

assertion acceptableBlastAddedNumber (int x) { x < 1025; x > 505; }

public acceptableBlastAddedNumber int addBlastNumbers (acceptableBlastNumber int x, acceptableBlastRadius int y) { return x + y; }

addBlastNumbers (10, 720) => 730

addBlastNumbers (26, 750) => Exception

```

Though I suppose that this is getting really close to just... classes. It would just be a little more... inline? Less complicated because it would never hold state? Though I suppose, this would also mean your class can just focus on being an objec, and not on having all the definitions for the things inside it, because you can have an <assertion> <object> rather than just <object>.

Preconditions and postconditions around procedures. I thought that it was an innovation from Eiffel, though wikipedia lists Ada as an influence, so maybe it did originate there!

> My view is that automated testing is not a substitute for QA, but an additional tool. It lets QA focus on harder to automated tasks.

This is my take as well. Automation is great for a lot of the repetitive work, but humans are better at creatively breaking stuff, handling UX testing, and improving and enhancing the automation itself.

I tend to prefer test harnesses, over unit tests[0], but each definitely has its place.

Testing is good. Integration testing is very good. Someone posted a story, a few days back, that linked to a GIF of this video[1] (I want to go to the source -Facebook, unfortunately), with the caption: "When the unit tests pass, but the integration test does not."

[0] https://littlegreenviper.com/miscellany/testing-harness-vs-u...

[1] https://www.facebook.com/100001967368624/posts/2559941464081...

My view now is that tests are mostly there as additional documentation and a form of cross cutting type checks. You have two functions with various parameters and then you write a unit test to document how these two functions interact with each other and you make sure that a certain beaten path remains working. It generally won't catch any bugs whatsoever but it's like having it documented that this is in fact a path and it is supposed to be there and it should remain maintained. Almost like a map of streets and thanks to the map you know that these paths are there and they need to be snowplowed even though maybe you don't remember why exactly there is a street here. It just is and you want it to remain in functioning order anyway. Even in teams I've not really seen unit tests catch any bugs but that may be because once you've written the code with tests it's so well documented and the purpose is so clear that people aren't going to forget how things work anymore. At that point most people won't "run into a wall" anymore because they can see the wall now.

I’ve worked on a large project that got more reliable after they removed the QA team. Removing the QA team was incidental to the quality improvement though — the QA team got removed because basically it was just producing a nightly report of problems that didn’t matter.

What actually improved quality on that project was, basically, architecture improvements. A major subsystem was rewritten in a way that made large classes of important bugs impossible. That rewrite was substantially supported by automated tests, though — a rather advanced simulation system IIRC.

I do agree that “automated tests” aren’t especially good at finding bugs. For that there’s really no replacement for human beings who care looking carefully at the system’s behavior, whatever title those people have. They’re mostly useful when they make it safer to make changes to the code’s design.

yes I think the way we use the word "bug" generally means that it's a bug from a human being's point of view. But automated tests are automated, maybe what they catch shouldn't even be called bugs but just type/check/assumption errors. Then the expectation is far more aligned with reality.. just because a unit test doesn't run successfully doesn't mean anything. You simply have to check the assumption and adjust the type check perhaps.

I wouldn't dismiss the value of automated testing altogether, I think it really depends on the domain. I haven't seen a dedicated QA team in any of my recent companies and all the projects do just fine.

With that said, if I were to develop something high-profile I'd use a combination of both (and usually funding is not an issue for this types of projects)

can't access the article, but I'm big on testability. We used to have to watch each change like a hawk via monitoring and manual checks. We automated with SLO monitoring for prod and heavy use of gated builds protected behind integration and acceptance tests.

My favorite system designed and used so far:

PR -> gated integration build with docker-compose -> merge master -> async master/main re-run same test to prevent merge regression -> promote to staging -> semi-optional user test suite -> rolling deploy that will revert if error thresholds trip. This could deploy out to thousands of nodes in like 10 minutes from merge, though sometimes it would be closer to 20.

The whole team had huge confidence that anything deployed would not immediately explode. Unlocked a lot of velocity. Each team could do multiple deploys per day.

This is the way to go. With the recent push towards "more nimble teams" and high output / high velocity there is no place for QA teams to take days to review the releases, so you'd have to bake testability in.

Recent example how fairly small team in Meta built the Threads app https://engineering.fb.com/2023/09/07/culture/threads-inside...

Lots of words. Still somehow misses the definition. And everything valuable about testablity's tiny Wikipedia page.

https://en.wikipedia.org/wiki/Testability

Testability is not the ability to write tests but the ability to write tests for a thing correlates with testability.

And worth mentioning to all of the (dev)ops folks who think they own observability . . . observability is a strict subset of testability.

One concern I have with designing for testability (which often means, designing for ease of unit testing) is that everything becomes a abstract interface.

Whereas before you might have

  void Foo(){
    Bar();

  }

  void Bar(){
    Baz();
  }

  void Baz(){

  }

Where Bar and Baz only really have a single implementation

This is fairly easy to grok and debug.

You end up with

   void Foo(BarInterface* bar, BazInterface* baz){

   bar->Bar(baz);

   }

Now you have a lot more moving parts. If you have a problem with Foo, now you need to try to find out who all implements the interfaces and which implementation is being passed to.

Of course, after a while people get tired of passing in these interfaces and come up with some kind of "automated" dependency injection framework, which then triples the complexity.

> If you have a problem with Foo

If you have a problem with Foo, you instantiate a Foo inside FooTest, and then feed it problematic input.

You can't instantiate a Foo inside FooTest unless you can instantiate a Foo and a Bar. Foo could be a clock whose time-of-day you can't control, and Bar could be a database that you can't instantiate.

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