Perhaps I shouldn't have it's the "spirit" of the article, since a larger goal for software engineering is defined with point 5 - regardless, the point is made that, even in the tools we work in today, these things are trivialities and are always a waste of time.
> and some of this can be alleviated by a decent modular design and taking on tech debt to refactoring/swapping out certain implementations for more robust implementations at a future date
Absolutely, but even that idea of "modular design" would be something that needed to be discussed by the team - but this article would label this a "fixation". This distinction really isn't helpful. What IS helpful is knowing how much time to spend on such a debate, and when to set that stuff aside in service of moving on with the work.
Seems like the spirit of this article is "don't debate test coverage, code design, etc. and just get the job done because meeting requirements is all that matters" and that we should focus on making code shorter. No evidence is offered that shorter code is somehow better than code that adheres to "best practices" or has higher test coverage. Has anyone here fit an entire enterprise software system into half a page, thereby obviating the need for unit tests and clean, understandable code?
This feels like someone that just wants to throw whatever code they want over the wall without being bothered to design it well, write tests for it, and adhere to a team's shared design guidelines. I can't stand working with people like this. Just my two cents.
EDIT: I see that this isn't the primary point of the article, and that maybe the author means we could rid ourselves of so much debate by creating more powerful tools and working at a higher level of abstraction. I think that some of these same things are being attempted, though, when someone suggests a design pattern or something within existing code that could raise the level of abstraction, and that it's worthwhile to have that debate rather than label code design best practices as trivialities.
Not sure if this is what segment is doing, but if it’s a worker based on the Kafka Streams library, the k/v state store can be backed by a changelog topic, and if you have a transaction between that topic and the topic that commits offsets, the scenario you described won’t occur. The worker will reprocess the event again, and it rocks won’t have it because it’ll only have the state that was persisted before the crash.
Can anyone who has tried doing this outside of university recommend some good benchmarks, or resources for setting up a personal lab testing environment for this? I want to give this a shot and didn't get to do it in university.
It would be amazing if this integrated with EKS to easily create remote k8s clusters for development. I couldn't tell from this press release if that's a possibility though.
When you set out to do this, had you studied docker's source code at all? Or did you just have a basic understanding of containers? Other than the link from OP, are there any resources you'd recommend to get one to the point where you have enough understanding of the concepts without having to "cheat" and look at the docker implementation?
I want to do this too, but it's not as much fun if you need to go to the source due to not understanding the fundamentals.
> Code coverage is entirely orthogonal to the type of test you are doing.
I did not say they were equivalent. Rather, I instead made a connection to the effort required to cover the state space in a given mode of testing. I've found it takes more integration tests to cover the state space than it does with unit tests, given the multiplicative nature of code paths in each layer. J.B. explains this in his talk, and I buy his argument, since I've experienced it myself.
For example, you could collapse several different types of failure at a lower layer into one code path in a higher layer using an exception, and verify that the HTTP layer returns the same thing when that exception happens. In this situation, you avoid having to set up the state necessary to cause the exception that the errors have been collapsed to, which may be 3 or 4 layers deeper. This means less test code, and less effort on my part.
> You appear to be fairly confused about the distinction between test coverage and modes of testing.
Nope, as explained above, the effort required to cover the state space is directly influenced by the mode of testing. For example, if you tested via typing on the keyboard with a pencil, you'd have to expend more effort to cover your state space. Of course, integration isn't as bad as pencil-testing, but the point is that one is easier than the other with respect to state space coverage.
Regarding reusable tooling - I've found this true as well. But tooling doesn't solve the whole problem. My main point is the amount of set up necessary to trigger a certain behavior, which tends to be too time consuming for me to justify the time. You can use tooling here as well, perhaps with data builders and object mothers. But that is also too much setup effort for my tastes.
> I have no problem with my tests chewing up 10,000% more CPU than yours and taking an extra 5 minutes if they've got even a 2% higher chance of finding bugs. CPU time is cheap, my time is expensive, bugs are expensive. Easy trade off.
Here you seem to assert that integration tests are more likely to find bugs. This is highly dependent on your skill in designing testable code that can be exercised without irrelevant portions of the stack. However, you do have a point that integration tests can find classes of bugs that unit tests may not (environmental setup, connection issues, etc.). I don't unit test those things of course.
Regarding CPU and the 5 minute difference - try 25 minutes difference. I have seen this in practice, and at that scale it really slows down the team. You can ease the pain with more machines, but I'd rather have focused, fast tests that can run right on my machine in as short a time as possible. I value the fast feedback loop.
> Still means you're writing and maintaining more code. That means more bugs and more work.
I haven't found this to be a problem. Sometimes I view messages between objects as behavior, so if the tests are inspecting that then I would actually want them to break if I change messages between objects. I've often found that if this is a hindrance to refactoring, there are problems in the code itself, not just the tests.
>Yup, and since changing the contracts between different subsystems is the most critical and important part of refactoring code...
I don't think that's true. If I'm changing contracts so often that it's a burden to maintain, I've likely missed an abstraction opportunity. Instead of blaming unit tests, I just fix the problem. Further, refactoring is not equivalent to changing subsystems. It encompasses many more useful activities that improve your code for the better. And further, if you take the stance that object interactions are behavior (sometimes, when useful), then changing that is not a refactor, but a re_work_. My favorite article on refactor vs. rework: http://www.daedtech.com/rewrite-or-refactor/
I'm not sure what you mean by "Realistic" tests. Perhaps you mean integrated tests that exercise, say, the entire stack from handling the HTTP request down to the database, if we're talking about a REST API.
If you don't care much about covering large portions of the state space and your tolerance for defects is high, then I'm sure your claim about improved productivity is true.
My tolerance for defects is low, which compels me to try and cover the state space more - happy paths, unhappy paths, etc. I've tried doing this with integrated tests, and found it to be very difficult to do in a timely manner. When I switched to using DI, coding to interfaces, and using mocks across architectural boundaries, my ability to cover the state space went up, and my time to deliver code with few defects went down.
I think this is because often the code I care about exercising is limited to a small section of the call stack. In an integrated test, I have to set up too much state and have the machine do too much irrelevant work just to verify this small portion of the code. With DI, interfaces, and mocks, I can isolate just this behavior much more quickly.
That leaves coupling and maintenance. This is a valid concern in the mockist style, but I haven't found it to be a limiting factor in maintaining my code. I think it comes down to testing the right things. If you're using mocks to verify a contract between two objects, and you want to change that contract, naturally your tests for the contract will break. In that case, I think it's fine to just delete them and write new tests that establish your contract. This kind of approach isn't right in all situations, but mockist style testing has helped my write some stable code that would have been a real pain to test due to external dependencies otherwise.
EDIT: That is not to say that your tolerance for defects is high, but rather that productivity is relative to your goals. Sometimes the time spent preventing defects and keeping maintainability high isn't worth it if the repercussions don't translate to dollars and cents.
Trying to cover your state space with coarse-grained tests in database-backed applications can lead to slow tests suites that are laborious to set up/tear down, which is a barrier to fast build times in CI. This harms productivity both while writing tests and in deployment.
You say "do what makes you successful" after "I have never let my teams go full TDD".
If someone on your team is most successful with TDD, do you still not allow it?
re: writing 30% less code - I've found TDD can reduce my percentage of lines of code, as you suggested. Adherence to the "refactoring" part encourages that you reduce duplication, which in my experience has been easier to do with good test coverage.