9 make sure you use a clear exception class that indicates this is a test timeout that's failing. This will help make it clear what's gone wrong should it happen, and perhaps allow a more sophisticated test harness to take account of this information in its display. The time values, in particular the waitLimit, should never be literal values. Make sure they are always values that can be easily set in bulk, either by using constants or set through the runtime environment. That way if you need to tweak them (and you will) you can tweak them all quickly. All this advice is handy for async calls where you expect a response from the provider, but how about those where there is no response. These are calls where we invoke a command on something and expect it to happen without any acknowledgment.
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As a result I strongly urge you to never use bare sleeps like this. Never use bare sleeps to wait for asynchonous responses: use a callback or polling. There are basically two tactics you can do for testing an asynchronous response. The first is evil for the asynchronous service to take a callback which it can call when done. This is the best since it means you'll never have to wait any longer than you need. The biggest problem with this is that the environment needs to be able to do this and then the service provider needs to. This is one of the advantages of having the development team integrated with testing - if they can provide a callback then they will. The second option is to poll on the answer. This is more than just looking once, but looking regularly, something like this /pseudo-code makeasyncCall startTime w; while! ResponseReceived) if (w - startTime waitLimit) throw new TestTimeoutException; sleep (pollingInterval readResponse The point of this approach is that you can set the pollingInterval to a pretty small value, and know that that's the maximum amount of dead time you'll lose to waiting for. This means you can set the waitLimit very high, which minimizes the chance of hitting it unless something serious has gone wrong.
Asynchronous Behavior, asynchrony is a boon that allows you to keep software responsive while taking on long term tasks. Ajax calls allow a browser to stay responsive while going back essay to the server for more data, asynchronous message allow a server process to communicate with other system without being tied to their laggardly latency. But in testing, asynchrony can be curse. The common mistake here is to throw in a sleep: /pseudo-code makeasyncCall; sleep(aWhile readResponse; This can bite you two ways. First off you'll want to set the sleep time to long enough that it gives plenty of time to get the response. But that means that you'll spend a lot of time idly waiting for the response, thus slowing down your tests. The second bite is that, however long you sleep, sometimes it won't be enough. There will be some change in environment that will cause you to exceed the sleep - and you'll get false failure.
In particular be aware with static data and singletons. A good example for this kind of problem is contextual environment, such as the currently logged in user. If you have an explicit tear-down in a test, be wary of exceptions that occur during the tear-down. If this happens the test can pass, but cause isolation failures for subsequent tests. So ensure that if you do get a problem in a tear-down, it makes about a loud noise. Some people prefer to put less emphasis on isolation and more on defining clear dependencies to force tests to run in a specified order. I prefer isolation because it gives you more flexibility in running subsets of tests and parallelizing tests.
Rebuilding the database each time can add a lot of time to test runs, so that argues for switching to a clean-up strategy. 6, one trick that's handy when you're using databases, is to conduct your tests inside a transaction, and then to rollback the transaction at the end of the test. That way the transaction manager cleans up for you, reducing the chance of errors. Another approach is to do a single build of a mostly-immutable starting fixture before running a group of tests. Then ensure that the tests don't change that initial state (or if they do, they reverse the changes in tear-down). This tactic is more error-prone than rebuilding the fixture for each test, but it may be worthwhile iff it takes too long to build the fixture each time. Although databases are a common cause for isolation problems, there are plenty of times you can get these in-memory too.
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4, lack of Isolation, in order to get tests to run reliably, you wine must have clear control over the environment in which they run, so you have a well-known state at the beginning of the test. If one test creates some data in the database and leaves it lying around, it can corrupt the run of another test which may rely on a different database state. Therefore i find it's really important to focus on keeping tests isolated. Properly isolated tests can be summary run in any sequence. As you get to larger operational scope of functional tests, it gets progressively harder to keep tests isolated. When you are tracking down a non-determinism, lack of isolation is a common and frustrating cause. Keep your tests isolated from each other, so that execution of one test will not affect any others.
There are a couple of ways to get isolation - either always rebuild your starting state from scratch, or ensure that each test cleans up properly after itself. In general I prefer the former, as it's often easier - and in particular easier to find the source of a problem. If a test fails because it didn't build up the initial state properly, then it's easy to see which test contains the bug. With clean-up, however, one test will contain the bug, but another test will fail - so it's hard to find the real problem. Starting from a blank state is usually easy with unit tests, but can be much harder with functional tests 5 - particularly if you have a lot of data in a database that needs to be there.
A danger here is that tests keep getting thrown into quarantine and forgotten, which means your bug detection system is eroding. As a result it's worthwhile to have a mechanism that ensures that tests don't stay in quarantine too long. I've come across various ways to do this. One is a simple numeric limit:. Only allow 8 tests in quarantine.
Once you hit the limit you must spend time to clear all the tests out. This has the advantage of batching up your test-cleaning if that's how you like to do things. Another route is to put a time limit on how long a test may be in quarantine, such as no longer than a week. The general approach with quarantine is to take the quarantined tests out of the main deployment pipeline so that you still get your regular build process. However a good team can be more aggressive. Mingle team puts its quarantine suite into the deployment pipeline one stage after its healthy tests. That way it can get the feedback from the healthy tests but is also forced to ensure that it sorts out the quarantined tests quickly.
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Quarantine, my principal aim in this article is to outline common cases of non-deterministic tests and how to eliminate the non-determinism. But before i get there i offer one piece of essential advice: quarantine your short non-deterministic tests. If you have non-deterministic tests keep them in a different test suite to your healthy tests. That way you'll you can continue to pay attention to what's going on with your healthy tests and get good feedback from them. Place any non-deterministic test in a quarantined area. (But fix quarantined tests quickly.). Then the question is what to do with the quarantined test suites. They are useless as regression tests, but they do have a future as work items for cleaning. You should not abandon line such tests, since any tests you have in quarantine are not helping you with your regression coverage.
The trouble with non-deterministic tests is that when they go red, you have no idea whether its due to a bug, or just part of the life non-deterministic behavior. Usually with these tests a non-deterministic failure is relatively common, so you end up shrugging your shoulders when these tests go red. Once you start ignoring a regression test failure, then that test is useless and you might as well throw it away. 3, indeed you really ought to throw a non-deterministic test away, since if you don't it has an infectious quality. If you have a suite of 100 tests with 10 non-deterministic tests in them, than that suite will often fail. Initially people will look at the failure report and notice that the failures are in non-deterministic tests, but soon they'll lose the discipline to do that. Once that discipline is lost, then a failure in the healthy deterministic tests will get ignored too. At that point you've lost the whole game and might as well get rid of all the tests.
detection mechanism by acting as regression tests. When a regression test goes red, you know you've got an immediate problem, often because a bug has crept into the system without you realizing. Having such a bug detector has huge benefits. Most obviously it means that you can find and fix bugs just after they are introduced. Not just does this give you the warm fuzzies because you kill bugs quickly, it also makes it easier to remove them since you know the bug got in with the last set of changes that are fresh in your mind. As a result you know where to look for the bug, which is more than half the battle in squashing. The second level of benefit is that as you gain confidence in your bug detector, you gain the courage to make big changes knowing that when you goof, the bug detector will go off and you can fix the mistake quickly. 2, without this teams are frightened to make the changes code needs in order to be kept clean, which leads to a rotting of the code base and plummeting development speed.
In my conversations with our delivery teams, one recurring problem that we've run into is tests which have become unreliable, so unreliable that people don't pay much attention to whether short they pass or fail. A primary cause of this unreliability is that some tests have become non-deterministic. A test is non-deterministic when it passes sometimes and fails sometimes, without any noticeable change in the code, tests, or environment. Such tests fail, then you re-run them and they pass. Test failures for such tests are seemingly random. Non-determinism can plague any kind of test, but it's particularly prone to affect tests with a broad scope, such as acceptance or functional tests. Why non-deterministic tests are a problem. Non-deterministic tests have two problems, firstly they are useless, secondly they are a virulent infection that can completely ruin your entire test suite.
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I've enjoyed watching ThoughtWorks tackle many difficult enterprise applications, bringing successful deliveries to many clients who have rarely seen success. Our experiences have been a great demonstration that agile methods, deeply controversial and distrusted when we wrote the manifesto a decade ago, can be used successfully. There are many flavors of agile development out there, but in what we do there is a central role for automated testing. Automated testing was a core approach to Extreme Programming from the beginning, and that philosophy has been the biggest inspiration to our agile work. So we've gained a lot of experience always in using automated testing as a core part of software development. Automated testing can look easy when presented in a text book. And indeed the basic ideas are really quite simple. But in the pressure-cooker of a delivery project, trials come up that are often not given much attention in texts. As i know too well, authors have a habit of skimming over many details in order to get a core point across.