We get feedback from the compiler. We get feedback from Test-Driven Development. But what sources of feedback lie in between?
This is where linters come in. A linter goes beyond “Does the code compile?” A linter answers questions like, “Is the code idiomatic? Is it stylistically clean? Are there any red flags?”
A paper published in 2013 about Test-Driven Development included the following diagram. Unfortunately, it gets some things wrong:
A tweet from Nat Pryce sparked discussion:
Grumpy request to academics: if you're going to publish ideas about how to improve TDD, get the original process right! pic.twitter.com/FaSU8CF6ol
— Nat Pryce (@natpryce) September 7, 2017
First, let me say I’m happy to see more studies on TDD. The thrust of this particular study is that TDD can be soft on negative tests. That is, maybe the code works for good data, but it’ll break on bad data.
TDD is a development discipline, so I’m all for learning more from traditional testing disciplines. I certainly don’t want to discourage folks from doing studies and writing papers.
But. Let’s first make sure we’re doing proper TDD, shall we? Otherwise any studies, especially studies about efficacy, may be flawed.
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The “Single Responsibility Principle” (SRP) sounds so noble. But I’m afraid it’s misunderstood and misapplied. Ask your teammates: “What is the Single Responsibility Principle?” Go ahead, ask them. Then ask if the SRP is a good thing or a bad thing. I’d bet many of them will say something like this: “In principle, it’s a good idea. But in practice, it’s overkill.”
On Twitter, Chris Eidhof pointed to an example of taking the Single Responsibility Principle too far. Specifically, Chris was unhappy with the argument that Singletons violate the SRP because, besides their main responsibility, they also manage their own life cycle:
This argument against singletons made me cringe (specifically, the SRP point): https://t.co/C9wVVnqHFs
— Chris Eidhof (@chriseidhof) June 29, 2017
This led to a lively discussion. Many reacted against “over-architecture.” No doubt they experienced fragmented code that grew from over-zealous attempts at SRP.
I think that SRP isn’t just over-applied. It’s fundamentally misunderstood, even misquoted. The repeated misquotes perpetuate that misunderstanding.
Let’s see if we can clear things up, and point to a better way.
Those of you who are regular visitors to qualitycoding.org may have already read Jon’s article outlining the life cycle of XCTestCase objects during the execution of a test suite. If you haven’t, or you want to refresh your memory, take a moment to read the original article.
Jon discusses the importance of the setUp and tearDown methods. And, more specifically, why we should be deallocating our properties in the tearDown method.
Writing a tear down method is one of those repetitive tasks. It follows a specific pattern: all properties should be set to nil. It doesn’t really require any brain power. It’s just one of those annoying tasks that you have to remember to do when writing a test.
It’s tasks like this that should be automated. And now with the Swift tear down inspection AppCode plugin, we can.
I want to ensure my platform does the best possible job of answering your needs and interests. And that means I need to know more about you. To do that, I’ve created my 2017 Reader Survey.
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Refactoring. It’s a word I hear quite a bit. Usually, in the context of conversations with management, it means, “Rewriting that thing. Hopefully without introducing bugs.” Often, among developers, it means, “One of the options in the Refactoring menu in my IDE.”
Code that’s easier to understand, maintain, and extend — that’s the promise of Object-Oriented Programming. But the reality for many iOS developers is that our objects are bloated. They know too much, and do too much. …What if our code has hidden objects, waiting to be found?
Each hidden object could provide a new abstraction, a new tool. They could make the code more manageable. Is there a way to discover these hidden objects? Domain-Driven Design (DDD) provides a way.
I’ve written about my experience of going to try! Swift Tokyo 2017. Now thanks to the video and transcript provided by Realm, I can also share the talk I gave: “Making Mock Objects More Useful”.
I start by showing the basics of how to make a Swift mock object by hand. But this easily leads to fragile tests because the assertions are overspecified. We need ways to make tests more malleable, with mocks that are more flexible.
How can we unit test JSON parsing, handling every possible error? Can we generate immutable models? And for Swift, how can we keep our Response Models free of optionals?
Of course, there are many JSON parsing libraries out there. Plug one in, define all fields as non-optional, and you’re good to go! …Until your app crashes, because something was different in the actual JSON data.
Unlikely? “The backend team would never do that to us”? I’ve had a released app crash because the backend folks changed one field from a string to an integer. I’ve seen app development and QA forced to pause because a commit assumed all fields were non-optional. (It crashed on the missing field, because Swift.)
So let’s look at a pattern that will help us
Even if you never plan to do your own parsing, we’ll learn things along the way about design and testing.