Tests that sometimes fail(samsaffron.com) |
Tests that sometimes fail(samsaffron.com) |
In my few years of automation experience, I've only seen 2 actual instances where the flaky tests were an actual issues and one of them should've been found by performance testing. Almost all of the rest were environment related issues. It's tough testing across all of the different platforms without running into some environment instability.
I noticed discourse had a lot of flaky tests while using their repo to test my knapsack_pro ruby gem to run test suite with CI parallelisation. A few articles with CI examples of parallelisation can be found here https://docs.knapsackpro.com
I need to try the latest version of discourse code, maybe now it will be more stable to run tests in parallel.
I fixed it for me by creating a random selection from /usr/share/dict/words to make a large array of sorted words to choose from. This made the fixtures have better and amusing names such as "string trapezoidal, string understudy"
"To this I would like to add that flaky tests are an incredible cost to businesses."
I think that the misconception here is that "tests should not fail", because they are "cost", "has to be analyzed and fixed", etc.
An integration or functional test that is guaranteed to never fail is kind of useless for me. Good test with a lot of assertions will fail occasionally since things are happening - unexpected data are provided, someone manually played with the database, ntp service was accidentally stopped and date in not accurate and filtering by date might be failing, someone plugged in some additional system that alters/locks data.
In case of unit tests, well, if everything is mocked and isolated then yes, such test probably should never fail, but unit tests are mostly useful only if there is some complicated logic involved.
I think that's an important distinction between functional and integration tests. Generally, a functional test is supposed to exercise a particular set of APIs or code paths - across components in a semi-realistic arrangement, so unlike a unit test where all but one would be mocked, but still pretty focused. It's OK for such a test to ignore concerns outside of its own scope. Data validation/sanitization should have its own tests, for example, and not be a part of every other functional test. That's just duplication of effort for very little benefit.
By contrast, it's reasonable for an integration test to fail due to something external like NTP failure ... once. After that, there should be a separate functional/regression test to ensure that the dependency is properly isolated, and integration tests should be expected to pass consistently unless there's a new kind of fault. That allows integration tests to capture all of those dependencies over time, until the full set approximates the set that exists in production.
Don't worry too much about the precise dividing line between functional and integration tests, though. The important thing is that they're not synonyms. Whatever one calls them, there are different classes of tests with different purposes. Statements like "tests should never fail" or "tests that fail are better" are too general to be useful across all kinds of tests.
In my projects I either fix the nondeterminism or delete such tests.
These are not exactly nondeterministic but sometimes people end up with that instead of pseudorandom ones.
Ha, yes! The problem sounds super dumb and obvious once you explain it, but can be a PITA to track down or recognise in the code.
For impure code, it made no sense to make a unit test.
Ability to separate pure vs impure code determines your test suites, where should be put in unit test, where should be put in integration test.
That is a small piece of actual software, everything everywhere works with IO or state like databases, each of which comes with ordering and concurrency assumptions. Every time you have a variable that is changed, you have more state to test.
Almost all code is impure.
If you have entire builds that are flaky, you end up training developers to just click "rebuild" the first one or two times a build fails, which can drastically increase the time before realizing the build is actually broken.
An important realization is that unit testing is not a good tool for testing flakyness of your main code - it is simply not a reliable indicator of failing code. Most of the time it's the test itself that is flaky, and it's not worth your time making every single test 100% reliable.
Some things we've implemented that helps a lot:
1. Have a system to reproduce the random failures. It took about a day to build tooling that can run say 100 instances of any test suite in parallel in CircleCI, and record the failure rate of individual tests.
2. If a test has a failure rate of > 10%, it indicates an issue in that test that should be fixed. By fixing these tests, we've found a couple of techniques to increase overall robustness of our tests.
3. If a test has a failure rate of < 3%, it is likely not worth your time fixing it. For these, we retry each failing test up to three times. Not all test frameworks support retying out of the box, but you can usually find a workaround. The retries can be restricted to specific tests or classes of tests if needed (e.g. only retry browser-based tests).
How do you know? What you say is plausible, but it's also plausible that these rarely-failing tests also rarely-fail in production, and occasionally break things badly and cause outages or make customers think of your software as flaky.
Since you say this, I presume you've spent the time to actually track down the root causes of several tests that fail < 3% of the time? If so, what did you find? Some sort of issues with the test framework, or issues with your own code that you're confident would only ever be exposed by testing, or something else? I'm very curious.
It's sort if a "bug" in that yes, clicking here and then here 1ms later doesn't do do the best thing, but it's basically irrelevant.
Testing is inherently a probabilistic endeavor.
"What can I do that is most likely to prevent the largest amount of bugginess?"
Fixing tests that rarely fail is -- in my experience -- a poor answer to such a question.
The most common places these flaky tests occur are with integration/browser-based tests, where there are multiple layers of tools that each fail a small percentage of the time.
Unit tests also sometimes fail because of not cleaning up state properly, which only breaks things when tests run in a very specific order. Or sometimes subtle assumptions in the tests about database ordering that is only valid 99% of the time.
I have always understood that unit tests must inherently be deterministic for the reason you explain.
A small test that is not deterministic is testing something other than "the unit" since there is another independent variable unaccounted for, often the state of the database or the configuration of a test environment.
Not that unit tests are perfect. Unit testing a concurrent data structure without threads (which are inherently nondeterministic) is not especially useful.
So it's a balance. Sometimes it really is worth it to just attack that one function with its weird snarl of if statements and initial conditions— totally. But there are other cases where part of what you want is to inspect what happens in the adjacent object, on a different thread, as a result of stimulating something under test conditions. This isn't wrong, and these kinds of tests can be really hard to get completely deterministic, especially if the CI environment is some heavily-loaded VM host with totally different thread switching characteristics from your laptop.
> 3. If a test has a failure rate of < 3%, it is likely not worth your time fixing it. For these, we retry each failing test up to three times. Not all test frameworks support retying out of the box, but you can usually find a workaround. The retries can be restricted to specific tests or classes of tests if needed (e.g. only retry browser-based tests).
to be pretty terrifying. I know that folks are under different amounts of pressure but we'd reject that code from merging here (or revert it out when we discovered the flakiness) as it's basically just a half-finished test that requires constant baby sitting.
Sometimes you can't just point at one thing and say reject this or revert that without a long investigation.
I recently went through a heavy de-flaking on a suite of Selenium tests. I found this comment to be true in my case; it was reasonable-seeming assumptions in the tests that caused flakiness more often than anything else. The second most common cause was timing or networking issues with the Selenium farm.
Spending the time actually de-flaking the tests was quite enlightening and lead to some new best practices for both writing tests, and for spinning up Selenium instances.
Because of that experience, I'm not sure I would agree with giving up on tests that fail less than 3% of the time, because fixing one of those cases can sometimes fix all of them. Learning the root causes of test failures lead me to implement some fixes that increased the stability of the entire test suite. Sometimes there's only one problem but it causes all the tests in a group to fail one at a time, infrequently and seemingly randomly.
A test suite with 1 test that fails 3% of the time will succeed 97% of the time. (1-.03)
A test suite with 10 tests that fail 3% of the time will succeed 74% of the time. (.97^10)
100 flaky tests? Now half your test runs fail. (.97^100)
You're retrying three times? Now your test suite is slow, but you can have up to 2,000 flaky tests before it starts becoming a real problem. Or 60,000 if you retry four times. (1-.03^3)^2000 = 94.7%; (1-.03^4)^60000 = 95.3%.
My conclusion: rerunning flaky tests is a legit way of solving the problem, as long as your tests aren't too slow. Still makes my skin itch, though. Fixing flaky tests forces me to face design flaws in my code.
(The math, in case I did it wrong: .03^3 = f = chance of a 3% failure test failing three runs in a row. 1-f = s = chance of test succeeding. s^1000 = chance of test run with 1000 flaky tests succeeding.)
That's why it's very important to retry individual tests, and not the entire test run.
Twitter had a comprehensive browser and system test suite that took about an hour to run (and they had a large CI worker cluster). Flaky tests could and did scuttle deploys. It was a never-ending struggle to keep CI green, but most engineers saw de-flaking (not just deleting the test) as a critical task.
PhotoStructure has an 8-job GitLab CI pipeline that runs on macOS, Windows, and Linux. Keeping the ~3,000 (and growing) tests passing reliably has proven to be a non-trivial task, and researching why a given task is flaky on one OS versus another has almost invariably led to discovery and hardening of edge and corner conditions.
It seems that TFA only touched on set ordering, incomplete db resets and time issues. There are many other spectres to fight as soon as you deal with multi-process systems on multiple OSes, including file system case sensitivity, incomplete file system resets, fork behavior and child process management, and network and stream management.
There are several aspects I added to stabilize CI, including robust shutdown and child process management systems. I can't say I would have prioritized those things if I didn't have tests, but now that I have it, I'm glad they're there.
Keeping the randomness in the test was the key factor in tracking down this obscure bug. If the test had been made completely deterministic, the test harness would never have discovered the problem. So although repeatable tests are in most cases a good thing, non-determinism can unearth problems. The trick is how to do this without sucking up huge amounts of bug-tracking time...
(Much effort was spent in making the test repeatable during debugging, but of course the crypto code elsewhere was deliberately trying to get as much randomness as it could source...)
* Non-determinism in the code - e.g. select without an order by, random number generators, hashmaps turned into lists, etc. - Fixed by turning non-deterministic code into deterministic code, testing for properties rather than outcomes or isolating and mocking the non-deterministic code.
* Lack of control over the environment - e.g. calling a third party service that goes down occasionally, use of a locally run database that gets periodically upgraded by the package manager - fixed by gradually bringing everything required to run your software under control (e.g. installing specific versions without package manager, mocking 3rd party services, intercepting syscalls that get time and replacing them with consistent values).
* Race conditions - in this case the test should really repeat the same actions so that it consistently catches the flakiness.
I have had to deal with non-deterministic tests with my embedded systems and robotic test suites and have found a few solutions to deal with them:
- Do a full power reset between tests if possible, or do it between test suites when you can combine tests together in suites that don't require a complete clean slate
- Reset all settings and parameters between tests. A lot of embedded systems have settings saved in Flash or EEPROM which can affect all sorts of behaviors, so make sure it always starts at the default setting.
- Have test commands for all system inputs and initialize all inputs to known values.
- Have test modes for all system outputs such as motors. If there is a motor which has a speed encoder you can make the test mode for the speed encoder input to match the commanded motor value, or also be able to trigger error inputs such as a stalled motor.
- Use a user input/dialog option to have user feedback as part of the test (for things like the LCD bug).
Robot Framework is a great tool which can do all these things with a custom Python library! I think testing embedded systems is generally much harder so people rarely do it, but I think it is a great tool which can oftentimes uncover these flaky errors.
[1] https://github.com/angular/angular.js/issues/5017
We do a lot of integration testing, more so than unit testing, and those tests, which randomly fail, are a real headache.
One thing I learned is that setting up tests correctly, independent of each other, is hard. It is even harder if databases, local and remote services are involved or if your software communicates with other software. You need to start those dependencies and take care of resetting their state, but there's always something: Services sometimes take longer to start, file handles not closing on time, code or applications which keeps running when another test fails... etc, etc...
There are obvious solutions: Mocking everything, removing global state, writing more robust test setup code... But who has time for this? Fixing things correctly can even take more time and usually does not guarantee that some new change in the future disregards your correct code...
I find that doing all of this tends to actually save time overall it's just that the up front investment is high and the payoff is realized over a long time.
Most software teams seem to prefer higher ongoing costs if it comes with quick wins to up front investment.
If you do it from the beginning and structure your code in a testable way it doesn't take much time. It saved me a few time in my current company; make a small change -> turns out it breaks a feature from 3-4 years ago that no one even remember -> look at the tests -> understand the feature as well as why what you did broke it.
If you try to do it after X years of coding without thinking about tests you're doomed though.
The world is non-deterministic. A test suite that can represent non-determinism is much more powerful than one that cannot. To paraphrase Dijkstra, "Determinism is just a special case of non-determinism, and not a very interesting one at that."
If a test is non-deterministic then a test framework needs to characterize the distribution of results for that test. For example "Branch A fails 11% (+/- 2%) of the time and Branch B fails 64% (+/- 2%) of the time." Once you are able to measure non-determinism then you can also effectively optimize it away, and you start looking for ways to introduce more of it into your test suites e.g. to run each test on a random CPU/distro/kernel.
The best way that I know for doing this is to write tests that are flaky because they expose the underlying flakiness in the application.
If an application is flaky and its test suite always runs 100% then I'd be pretty suspicious about that test suite being adequate.
This is the only relevant factor. Forget the rest. Users don't experience your flaky tests just like they don't experience your messy Jira boards or your bad office coffee.
I think there is overlap and that it does not have to be a choice between either approach.
I really like the different approaches to dealing with these flaky tests, that is a good list.
Unit tests are great. You want them. Craft your interfaces to enable them.
Integration and system tests are important too. Again, crafting higher level interfaces that allow for testing will, in general, lead to a more ergonomic API.
Analogously: unit tests ensure each of your LEGO blocks are individually well-formed. Integration tests ensure that the build instructions actually result in something reasonable.
Having coded at both Lyft and at DoorDash, I noticed both companies had the exact same unit test health problems and I was forced to manually come up with ways to make the CI/CD reliable in both settings.
In my experience, most people want a turnkey solution to get them to a healthier place with their unit testing. "Flaptastic" is a flaky unit tests recognition engine written in a way that anybody can use it to clean up their flaky unit tests no matter what CI/CD or test suite you're already using.
Flaptastic is a test suite plugin that works with a SAAS backend that is able to differentiate between a unit test that failed due to broken application code versus tests that are failing with no merit and only because the tests are not written well. Our killer feature is that you get a "kill switch" to instantly disable any unit test that you know is unhealthy with an option to unkill it later when you've fixed the problem. The reason is this is so powerful is that when you kill an unhealthy test, you are able to immediately unblock the whole team.
We're now working on a way to accept the junit.xml file from your test suite. We can run it through the flap recognition engine allowing you to make decisions on what you will do next if you know all of the tests that failed did fail due to known flaky test patterns.
If Flaptastic seems interesting, contact us on our chat widget we'll let you use it for free indefinitely (for trial purposes) to decide if this makes your life easier.
One particular usecase for Undo (besides obviously recording software bugs per se) is recording execution of tests. Huge time saver. We do this ourselves - when a test fails in CI, engineers can download a recording file of a failing test and investigate it with our reversible debugger.
I think that's one message that is completely lost in the article and in the rest of the comments here: it is possible to improve technology so that flaky tests are more debuggable.
With enough investment (hardware and OS support for low-impact always-on recording) we could make every flaky test debuggable.
For the ID issue I have a monkey patch for Activerecord:
if ["test", "cucumber"].include? Rails.env
class ActiveRecord::Base
before_create :set_id
def set_id
self.id ||= SecureRandom.random_number(999_999_999)
end
end
end
No matter what, developers complain and try to avoid running the tests at all. I'd love to force their hand by making a successful test run an absolute requirement for committing code, but the very fact that tests have been slow and flaky since long before I got here means that would bring development to a standstill for weeks and I lack the authority (real or moral) for something that drastic. Failing that, I lean toward re-running tests a few times for those that are merely flaky (especially because of timing issues), and quarantine for those that are fully broken. Then there's still a challenge getting people to fix their broken tests, but life is full of tradeoffs like that.
Second biggest is database transaction management and incorrect assumptions over when database changes become visible to other processes (which are in some way also concurrency problems, so it basically comes down to that). Third biggest is unintentional nondeterminism in the software, like people assuming that a certain collection implementation has deterministic order, but actually it doesn't, someone was just lucky to get the same order all the time while testing on the dev machine.
- the order of items on the page is different, due to the way tuples have been inserted (different external scheduling, different postgres internal scheduling) - concurrent sequential scans can coordinate relation scans, which is quite helpful for relations that are larger than the cache - different query plans, e.g. sequential vs index scans
Unless you specify the ORDER BY, there really isn't any guarantee by postgres. We could make it consistent, but that'd add overhead for everyone.
It will do things like separate out different kinds of test failures (by error message and stacktrace) and then measure their individual rates of incidence.
You can also ask it to reproduce a specific failure in a tight loop and once it succeeds it will drop you into a debugger session so you can explore what's going on.
There are demo videos in the project highlighting these techniques. Here's one: https://asciinema.org/a/dhdetw07drgyz78yr66bm57va
Ideally all state that's used in a test would be reset to a known value at or before the start of the test, but this is quite hard for external non-mocked databases, clocks and so on.
For integration tests, do you run in a controllable "safe" environment and risk false-passes, or an environment as close as possible to production and risk intermittent failure?
A variant I've seen is "compiled languages may re-order floating point calculations between builds resulting in different answers", which is extremely annoying to deal with especially when you can't just epsilon it away.
But why do all of this piecemeal? Our philosophy is to create a controlled test sandbox environment that makes all these aspects (including concurrency) reproducible:
https://www.cloudseal.io/blog/2018-04-06-intro-to-fixing-fla...
The idea is to guarantee that any flake is easy to reproduce. If people have objections to that approach, we'd love to hear them. Conversely, if you would be willing to test out our early prototype, get in touch.
First, don't delete them, flaky tests are still valuable and can still find bugs. We also had the challenge where a lot of the 'flakiness' was not the test or the application's fault but was caused by 3rd party providers. Even at Google "Almost 16% of our tests have some level of flakiness associated with them!" - John Micco, so just writing tests that aren't flaky isn't always possible.
Appsurify automatically raises defects when tests fail, and if the failure reason looks to be 'flakiness' (based on failure type, when the failure occurred, the change being made, previous known flaky failures) then we raise the defect as a "flaky" defect. Teams can then have the build fail based only on new defects and prevent it from failing when there are flaky test results.
We also prioritize the tests, which causes fewer tests to be run which are more likely to fail due to a real defect, which also reduces the number of flaky test results.
> We created a topic on our development Discourse instance. Each time the test suite failed due to a flaky test we would assign the topic to the developer who originally wrote the test. Once fixed the developer who sorted it out would post a quick post morterm.
What's the game here? It just seems like a process. Useful, sure, but not particularly fun...
My solution was to add a calibrated set of benchmarks. For each problem in the test suite, I measure the probability of failure. From that probability, I can compute the probability of n repeated failures. Small regressions are ignored, but large regressions (p < .001) splat on CI. It's fast enough, accurate enough, and brings peace of mind.
I understand that, and why, engineers hate this. But it's greatly superior to nothing.
* Puppeteer (browser automation) bugs or improper use. Certain sequence of events could deadlock it, causing timeouts relatively rarely. The fix was sometimes upgrading puppeteer, sometimes debugging and working around the issue.
* Vendor API, particularly their oauth screen. When they smell automation, they will want to block the requests on security grounds. We have routed all requests through one IP address and reuse browser cookies to minimize this.
* Vendor API again, this time hitting limits on rare situations. We could have less parallel tests, but then you waste more time waiting.
Eventually, we will have to mock up this (fairly complex) API to progress. It's got to a point where I don't feel like adding more tests because they may cause further flakiness - not good.
The otherwise good advice for randomization has its drawbacks-
- it complicates issue reproduction, especially if the test flow itself is randomized and not just the data
- the same way it catches more issues, it might as well skip some
Something else that was mentioned but not stressed enough is the importance of clean environment as the basis for the test infrastructure.
A cleanup function is nice but using a virtual environment, Docker or a clean VM will save you a lot of debugging time finding environmental issues. The same goes for mocked or simplified elements if they contribute to the reproducibility of the system- a simpler in-memory database can help re creating a clean database for each test instead of reverting for example
In case of concurrent execution there are a only a few reasonably working tricks like Relacy and other exhaustive ordering checkers as well as formal proofs. Neither is cheap to use, so you will always get flaky tests there - or rather tests that do not always fall.
Subtle concurrency issues are indeed very difficult to be found debugged and reproduced and randomization could help with that simply by covering more space.
https://testing.googleblog.com/2016/05/flaky-tests-at-google...
They could do with being a little more humble and focusing on improving their engineering practices.
I’m founder at Tesults (https://www.tesults.com) where we have a flaky test indicator that makes identifying these tests easier. It’s free to try and if you can’t get budget for a proper plan send me an email and I’ll do what I can.
In general the only way to never have flaky tests is to have simpler tests but I find those often don’t provide as much value - that’s just my personal belief after having spent years focused on automated tests, e2e tests do have robustness issues but the bugs they find make them totally worth it. Out of the issues mentioned in the article that affected my tests the most, it’s timing. They can be overcome though, I’ve run test suites with a couple of thousand e2e tests (browser) that have been highly robust and reliable after time was devoted to hardening them. You do have to focus on that and refuse to add new test cases until the existing ones are sorted out in some cases.
It's a reference to RTFM, Read The Fine Manual.
TIL: RTFM was a phrase from the 40s : "Read the field manual."
Usually it's a kind of negative retort - 'well if you'd actually bothered to read TFA then ...' - but increasingly it seems to be used without such emotion (particularly, to me anyway, on HN) to mean simply 'the submission'.
There was the logic that generates the SSL key pair, and there is the faulty logic that consumes it. Based on the description, it seems it's an indication of missing test coverage around the faulty code. If, when the faulty code was written, more time were spent on understanding the assumptions the code has made, then maybe the test wouldn't appear in the first place.
This anecdote, however, does bring up a good point: Don't shrug off intermittently failed tests - Dig in and understand the root cause of it.
Now that this edge case has been found, of course it should be replaced by deterministic tests that tests the consuming code with different kinds of keys, including one with a leading zero.
Based on the description of the bug, the bug was in Microsoft's SChannel TLS stack (I know this, because I found it too, and got a workaround into OpenSSL). I don't know about you, but I haven't written a whole lot of comprehensive tests for any TLS stack that I've used, unless I wrote the stack. I'm assuming jooster didn't work for Microsoft, he just worked for a company that released software for some flavor of Windows and used their TLS stack, because it was there.
This definitely fits into the category of nobody is going to test this, because it's not going to occur to anyone to test how the third party TLS library they're using handle public keys encoded without leading zeros, until they've ran into it before. Having a random key generated in a test suite means you've got a chance to see it; if you're lucky (and if you don't just retry everything without knowing why).
The 'gotcha' part in this case was that the SSL keygen code did not just use a random number seed but was grasping for entropy from other sources too (PIDs, perhaps? I can't recall the detail). That unknown made initial attempts to recreate the problem difficult.
Plus the failing test in question was not directly testing the SSL, merely making use of it. There were other SSL-specific tests run separately but they missed this strange corner case (in 'normal' use, it wasn't like 1 in 256 transactions would fail, otherwise that would have been much more obvious and we'd have had spurious-seeming failures all over the place).
That brings up another test pain: You can write lots of specific unit tests for every individual feature your code has, and they can all pass just fine. But when feature A, D and H happen to all be in use at once, you hit a separate problem. Onwards to 100% coverage...
That's a cop out, you rarely know all the assumptions of all the code. The point of tests is to hopefully suss out those unknowns. Just saying "you should think harder and write better tests" doesn't result in better code.
- The temperature is much hotter/colder than normal
- Someone is inadvertently holding down a button or key on the machine under test
- The wrong version of software is loaded onto the machine
I like this 1999 story about a flaky test at Be:
> Two test engineers were in a crunch. The floppy drive they were currently testing would work all day while they ran a variety of stress tests, but the exact same tests would run for only eight hours at night. After a few days of double-checking the hardware, the testing procedure, and the recording devices, they decided to stay the night and watch what happened. For eight hours they stared at the floppy drive and drank espresso. The long dark night slowly turned into day and the sun shone in the window. The angled sunlight triggered the write-protection mechanism, which caused a write failure. A new casing was designed and the problem was solved. Who knew?
https://www.haiku-os.org/legacy-docs/benewsletter/Issue4-22....
I thought tests weren't meant to have external dependencies (or at least, ones outside the control of the test harness)?
For very complex dependencies I would build a mock that could run in a passthrough / mock mode where I could test realistically (in passthrough mode) and test deterministically (in mock mode, using a recording of the passthrough mode).
This would be helpful in getting rid of flaky tests (mock mode), ensure 3rd party services don't get hammered (mock mode) and being able to isolate and detect breakages caused by external service changes (passthrough mode).
There could be other types of test where a remote call would make sense, for example, "was the deployment successful?" tests might try to verify that the deployed version of the software can communicate with external dependencies correctly.
For some programs, testing without external dependencies is basically useless. Other times, you can remove them without much loss. But it's always better if you can keep them.
That's a pretty powerful insight!
I think that a lot of developers who are firmly in the test-driven camp don't realize this, but instead think that if you have 100% test coverage, your code will work 100% of the time. Fixing bugs, to them, is "just" an inevitable result of increasing your test coverage, so that's what they focus on.
But it’s interesting to hear about an identical sounding bug in similar code/routines. I’d say ‘what are the chances?’ but crypto code is always painfully hard.
https://www.microsoft.com/en-us/research/publication/chess-a...
If you have an Employee model and it returns certain attributes of an employee like Salary, you might have tests that depend on the structure of an employee. You might have, say, Job and Position models which define an employee-job and the base definition of the particular job. Say Position has a salary range associated, and Job has validation rules which check that the salary is in range.
You could define factories for all those things, or you could use real examples that are served by a live Employee API.
The canonical way to address this is with factories and mocks, if you have time do that! (It will probably save you in the long-run, when that complexity has grown a bit.)
If you just grab the example person whose salary is out of the range for their position and quickly test that the behavior in nearby modules matches your expectations, well, those are still tests, and you could be forgiven for writing them this way.
I think they call these the "London" and "Detroit" styles of mocking, but the short version IMHO is that a mistake was making dev as a clone of production, and any errors in judgement that came after that were merely coping mechanisms.
If you want your tests to tell you when something has changed that requires your attention, you need a test that hits this Employee API and will fail if the structure of the employees returned is no longer conforming to your expectations, even though it's external. The design of such a thing is something I won't profess to know how to do well.
(It's better to version your API and write a changelog that tells what you need to know if the old version has been replaced by a new version, but if you're writing these microservices all for yourself it can seem pedantic to explicitly version your API, too. There are also coping mechanisms you'll need to embrace once you get to "we're not incrementing the API version" and surprise, many of them are the same ones...)
Classic TDD as you describe (see the other reply, classic TDD is different) works great for algorithms: take some data, manipulate it, and get different data out. There is no need for mocks. This is where you business logic should be, and it is easy to test.
However this fails in the real world because algorithms are but a minority of code: most code in my experience is just moving data around from subsystem to subsystem, and external collaborators. Here you do have collaborators and the interactions are the point. Mocks now start to make sense because the point is my subsystem deliver data to that something else, and I shouldn't know or care what that something else is.
I've seen the above fail in several ways. I've seen people mock their algorithm from the communication, but in practice the communication and the algorithm are tightly coupled anyway so changes in once will change the other.
Worse, I see many people test not the subsystem boundaries, but boundaries within the subsystem. That is they start writing the subsystem, and then realize (correctly) that they need to break the subsystem up, then they test the subsystem as it is broken down. This seems good, but it leads to brittle systems that cannot be changed because the sub-subsystem is now not allowed to change because it would break tests..
To understand this, remember, a test is an assertion that something will not change. Thus if you mock a collaborator you are asserting that the collaborator is a different subsystem and you and not allowed to refactor across this boundary. If the boundary is not an architecture boundary you shouldn't mock it because you might want to change it.
I suppose it depends on your definition of "classic TDD dogma". Mocking really wasn't a thing until TDD had been around for about 5-10 years, so super classic TDD dogma has always been "don't mock" ;-)
"London School", GOOSE, Outside-in approach has always been to mock heavily. I call it "wish based programming". You write a test, wishing that you had some facility and since you don't have it, you mock it. Then once the test is in place, you can write your code and eventually write production code that represents the mock (and personally, I remove the mock at that point).
It was really after that, as far as I can tell, that people started to get the idea that you should mock all your collaborators in order to isolate your units. This kind of isolation was never a thing originally (see Kent Beck's original book on the subject). Even if you watch DHH's conversations with Kent Beck (and I think Martin Fowler???) on the topic and they state pretty clearly that "Chicago School" is to avoid mocking except as a last resort (my own personal preference as well). Also take a look at Michael Feather's discussion in his Legacy Code book for a good description of what the original ideas what fakes, stubs and mocks were. These days those definitions are practically lost.
I'm not sure why there has been this idea that mocking was always a part of TDD, but it definitely is a popular notion.
It's the black box/functional/integration test that exercises the production code from the standpoint of the enduser that proves whether a tested module's dependencies actually satisfy their contracts. Also, the functional tests are the only place that you can discover if DevOps is needed as well prior to deploying into a real test/prod environment. Plus, the functional test captures the user story that we're focused on in a way unit tests cannot, so the functional tests direct the overall work.
I must have those functional tests in place before I do my unit tests. Otherwise, those mocks really are creating a wish-based programming system.
I agree with you that mocking of today is indistinguishable from what Michael Feathers described in his Legacy Code book (which is excellent regardless, BTW.) Mocking today is so easy to express and change and grok with tools like Spock Framework.
Although I've never been a ruby programmer, you're right that I'm influenced by DHH and the ruby community's approach on a lot of these things.
On the setup side, the main networking issue for me was ssh. This might be different for you if you use either a Selenium cloud service, or a proper VPN setup. I was spinning up a virtual network on AWS for a Selenium farm in a fairly ad-hoc and manual way using ssh. The tests were sometimes (only very occasionally) starting before the ssh connection was actually ready, so I had to put a delay in the setup script to send & wait until a packet had actually been received before launching the test. I used netcat for that.
Just having this means a lot less time finding those problems for me.
It may take a while... but while that flakey test exists in your codebase it will leverage a constant cost on all of your developers.
Which code? When you randomly run into the flakey test, in most cases it's not coming from the change which was just tested. You'd reject some random, unrelated PR
See, once you know why the test fails and it's not the tested application, which is exceedingly rare in practice, you can just disable it or fix it. But only if you're actually sure, not before.
If your test suite can handle non-determinism then you can approach test-writing in a completely different - braver - way.
That's kind of unfair, it's pretty much the logical deduction if you think about randomness in your unit tests. I.e., suppose the test fails, then what? You want to reproduce, but really all you know is that the test indicates the possibility of a bug. In order to investigate, you really need to follow the same code path that the test did. How do you do that? Capture the seed.