[Dev Diaries] Code Coverage?

As you probably know by now, I'm fairly obsessed with tools that give me metrics about quality: linting, docs, tests...

Unfortunately, code coverage is fairly hard to get with SPM in a way that is usable.


Code coverage is the amount of code in the package that is covered with your tests. If you run your tests, are these lines run? Are those? Your tests pass, and that's fine, but have you forgotten to test anything?

You can enable it in Xcode using the option "gather code coverage" in the scheme you are running tests for, which allows you to find a decent visualization if you know where to look for it (a new gutter appears in your code editors).

In Swift Package Manager though, it's fairly obscure:

  • first you have to use the --enable-code-coverage option of the testing phase
  • then you have to grab the output json path by using --show-codecov-path
  • then you get a collection of things that is unique to SPM, and therefore unusable elsewhere

Now, if you look closely at the output, you can see it's fairly close to the lcov format, which is more or less a standard.

Let's make a script!

Because I'm very attached to my packages running on both Linux and MacOS, I need to grab the correct values from the environment (makes them dockerizable too).

I need:

  • the output of the swift test phase
  • llvm-cov which is used by the swift toolchain and can extract usable information
  • A few frills here and there

Looking here and there if anything existed already, I stumbled upon a good writeup setting some of the bricks up. I would suggest reading this first if you want to get the nitty gritty details.

Reusing parts of this and making my own script that can spit either human readable or lcov-compatible, and can work either on Linux or MacOS, and dockerizable, here's what I end up with:


swift test --enable-code-coverage > /dev/null 2>&1

if [ $? -ne 0 ]; then
echo "tests unsuccessful"
exit 1

BIN_PATH="$(swift build --show-bin-path)"
XCTEST_PATH="$(find ${BIN_PATH} -name '*.xctest')"

if [[ "$OSTYPE" == "darwin"* ]]; then
	COV_BIN="/usr/bin/xcrun llvm-cov"
	MODULE="$(basename $XCTEST_PATH .xctest)"
	COV_BIN=`which llvm-cov || echo "false"`

if [ $# -eq 0 ]; then
	$COV_BIN report -ignore-filename-regex=".build|Tests" \
	-instr-profile=.build/debug/codecov/default.profdata -use-color \
elif [ $# -eq 1 ] && [ $1 = "lcov" ]; then
	$COV_BIN export -ignore-filename-regex=".build|Tests" \
	-instr-profile=.build/debug/codecov/default.profdata \
	--format=lcov "$XCTEST_PATH" 
	echo "Usage:"
	echo "codecov.sh [lcov]"
	echo "use without argument for human readable output"
	echo "use with lcov as argument for lcov format output"
	exit 1

codecov.sh spits something like:

Filename                      Regions    Missed Regions     Cover   Functions  Missed Functions  Executed       Lines      Missed Lines     Cover
SEKRET.swift                       67                20    70.15%          23                 4    82.61%         157                27    82.80%
Extensions.swift                   15                 3    80.00%           2                 0   100.00%          20                 0   100.00%
TOTAL                              82                23    71.95%          25                 4    84.00%         177                27    84.75%

codecov.sh lcov spits the corresponding lcov output.

Hurray for automation!

Introducing FuzzyTests

TL;DR: Grab it here : Github repo

Unit testing is painful amirite?

Writing good tests for your code very often means spending twice as much time coding them than on the things you test themselves.

It is good practice though to verify as much as possible that the code you write is valid, especially if that code is going to be public or included in someone else's work.

In my workflow I insist on the notion of ownership :

The bottomline for me is this: if there are several people on a project, I want clearly defined ownership. It's not that I won't fix a bug in someone else's code, just that they own it and therefore have to have a reliable way of testing that my fix works.
Tests solve part of that problem. My code, my tests. If you fix my code, run my tests, I'm fairly confident that you didn't wreck the whole thing. And that I won't have to spend a couple of hours figuring out what it is that you did.

This a a very very very light constraint when you compare it to methodologies like TDD, but it's a required minimum for me.

Plus, it's not that painful, except...

Testing every case

In my personal opinion, the tests that are hardest to do right are the ones that have a very large input range, with a few failure/continuity points.

If, for instance, and completely randomly, of course, you had an application where the tilt of the phone changes the state of the app (locked/unlocked, depending on whether the phone is lying flat-ish on the table or not:

  • from -20º to 20º the app is locked
  • from 160º to 200º the app is locked
  • the rest of the time it's not locked
  • All of that modulo 360, of course

So you have a function that takes the current pitch angle, and returns if we should lock or not:

func pitchLock(_ angle: Double) -> Bool {
  // ...

Does it work? Does it work modulo 360? What would a unit test for that function even look like? A for loop?

I have been looking for a way to do that kind of test for a while, which is why I published HoledRange (now Domains 😇) a while back, as part of my hacks.

What I wanted is to write my tests kind of like this (invalid code on so many levels):

for x in [-1000.0...1000.0].randomSelection {
  let unitCircleAngle = x%360.0
  if unitCircleAngle >= 340 || unitCircle <= 20 {
  } else if unitCircleAngle >= 160 && unitCircle <= 200 {
  } else {

This way of testing, while vaguely valid, leaves so many things flaky:

  • how many elements in the random selection?
  • how can we make certain values untestable (because we address them somewhere else, for instance)
  • what a lot of boilerplate if I have multiple functions to test on the same range of values
  • I can't reuse the same value for multiple tests to check function chains

Function builders

I have been fascinated with @_functionBuilder every since it was announced. While I don't feel enthusiastic about SwiftUI (in french), that way to build elements out of blocks is something I have wanted for years.

Making them is a harrowing experience the first time, but in the end it works!

What I wanted to use as syntax is something like this:

func myPlus(_ a: Int, _ b: Int) -> Int

DomainTests<Int> {
    Test { (a: Int) in
        XCTAssert(myPlus(a, 1) == a+1, "Problem with value\(a)")
        XCTAssert(myPlus(1, a) == a+1, "Problem with value\(a)")
    Test { (a: Int) in
        let random = Int.random(in: -10000...10000)
        XCTAssert(myPlus(a, random) == a+random, "Problem with value\(a)")
        XCTAssert(myPlus(random, a) == a+random, "Problem with value\(a)")

This particular DomainTests runs 1000000 times over $$D=[-10000;10000]$$ in a random fashion.

Note the Test builder that takes a function with a parameter that will be in the domain, and the definition that allows to define both the test domain (mandatory) and the number of random iterations (optional).

If you want to test every single value in a domain, the bounding needs to be Strideable, ie usable in a for-loop.

DomainTests<Int> {
    Test { (a: Int) in
        XCTAssert(myPlus(a, 1) == a+1, "Problem with value\(a)")
        XCTAssert(myPlus(1, a) == a+1, "Problem with value\(a)")
    Test { (a: Int) in
        let random = Int.random(in: -10000...10000)
        XCTAssert(myPlus(a, random) == a+random, "Problem with value\(a)")
        XCTAssert(myPlus(random, a) == a+random, "Problem with value\(a)")


A couple of hard working days plus a healthy dose of using that framework personally means this should be ready-ish for production.

If you are a maths-oriented dev and shiver at the idea of untested domains, this is for you 😬

[Dev Diary] Vanilla Is The Best Flavor

I have a weird thing with the multiplication of command-line tools and gizmos: I forget them.

Do I want to run supercool gitlab commands? Hell yea! Do I need to install 12 utilities (or code a new one) to archive every project older than a year? I hope not...

The setup

I am a sucker for well documented fully linted code. But the thing is, all the gizmos that help me do that have to be installed in the system or in my ~/bin and I have to remember to update them, and I have to install them on my CD machine, and on every new environment I setup, and make sure they are still compatible with the toolchain, and it freaks me out, ok?

Plus,watching the students try to do it is painful.

So, given a 100% vanilla swift-capable environment, can I manage to run documentation and linting?

The idea

We have Swift Package Manager, which is now a first-class citizen in XCode, but it can't run shell script phases without some nasty hacks.

What if some targets were (wait for it) built to do the documentation and the linting?


One of the most popular linters out there is swiftlint, and it supports SPM. It can also build a library instead of an executable, which means one of my targets could just run the linting and output it in the terminal.

In the Package.swift file, all I needed to do was add the right dependency, and the right product and voila!

let package = Package(
	name: "WonderfulPackage",
    products: [
    	// ...
         .executable(name: "Lint", targets: ["Lint"])
    dependencies: [
        // Dependencies declare other packages that this package depends on.
        // .package(url: /* package url */, from: "1.0.0"),
		// ... normal dependencies
        .package(url: "https://github.com/realm/SwiftLint", from: "0.39.0")
    targets: [
    	// ... normal targets
            name: "Lint",
            dependencies: ["SwiftLintFramework"]),

Now, SPM is very strict with paths, so I had to put a file named main.swift in the Sources/<target>/ directory, in this case Sources/Lint.

Running the linter is fairly straightforward, and goes in the main.swift file:

// Lint command main
// runs SourceDocs
import Foundation
import SwiftLintFramework

let config = Configuration(path: FileManager.default.currentDirectoryPath+"/.swiftlint.yml",
                           rootPath: FileManager.default.currentDirectoryPath,
                           optional: true,
                           quiet: true,
                           enableAllRules: false,
                           cachePath: nil,
                           customRulesIdentifiers: [])

for lintable in config.lintableFiles(inPath: FileManager.default.currentDirectoryPath, forceExclude: false) {
    let linter = Linter(file: lintable, configuration: config)
    let storage = RuleStorage()
    let collected = linter.collect(into: storage)
    let violations = collected.styleViolations(using: storage)
    if !violations.isEmpty {

print("🎉 All done!")

Setup the .swiftlint file as usual, and run the command via swift run Lint

⛔️ Line 15: Variable name should be between 3 and 40 characters long: 'f'
⚠️ Line 13: Arguments can be omitted when matching enums with associated types if they are not used.
⚠️ Line 12: Line should be 120 characters or less: currently 143 characters


Documentation is actually trickier, because most documentation tools out there aren't built in swift, or compatible with SPM. Doxygen and jazzy are great, but they don't fit my needs.

I found a project that was extremely promising called SourceDocs by Eneko Alonso, but it isn't a library, so I had to fork it and make it into one (while providing a second target to generate the executable if needed). One weird issue is that SPM doesn't like subtargets to bear the same name so I had to rename a couple of them to avoid conflict with Swift Argument Parser (long story).

I finally found myself in the same spot than with the linter. All I needed to do was create another target, and Bob's you're uncle. Well actually he was mine. I digress.

let package = Package(
	name: "WonderfulPackage",
    products: [
    	// ...
         .executable(name: "Docs", targets: ["Docs"])
    dependencies: [
        // Dependencies declare other packages that this package depends on.
        // .package(url: /* package url */, from: "1.0.0"),
		// ... normal dependencies
        .package(url: "https://github.com/krugazor/SourceDocs", from: "0.7.0")
    targets: [
    	// ... normal targets
            name: "Docs",
            dependencies: ["sourcedocslib"])

Another well-placed main file:

// Docs command main
// runs SourceDocs
import Foundation
import SourceDocs

do {
    switch try SourceDocs().runOnSPM(moduleName: "WonderfulPackage",
                                     outputDirectory: FileManager.default.currentDirectoryPath+"/Documentation") {
    case .success:
        print("Successful run of the documentation phase")
    case .failure(let failure):
} catch {

Now, the command swift run Docs generates the markdown documentation in the Documentation directory.

Parsing main.swift (1/1)
Removing reference documentation at 'WonderfulPackage/Documentation/KituraStarter'... ✔
Generating Markdown documentation...
  Writing documentation file: WonderfulPackage/Documentation/WonderfulPackage/structs/WonderfulPackage.md ✔
  Writing documentation file: WonderfulPackage/Documentation/WonderfulPackage/README.md ✔
Done 🎉
Successful run of the documentation phase


✅ Vanilla swift environment
✅ No install needed
✅ Works on Linux and MacOS
✅ Integrated into SPM
⚠️ When running in XCode, the current directory is always wonky for packages

[HoledRange] Sequences and Transformations

HoledRange has reached v 0.1.3 with two additions:

  • Iterating over ranges (contributed by Hugo a.k.a. Zyigh)
  • Transforming ranges into other ranges

Simple idea, full of tricks: how do I apply a function f to transform a domain?

  • It has to result to a possible bound value for the domain (ie Hashable & Comparable)
  • We need to check the ordering of the new values in case the transformation flips the sign (eg [0;1] -> [-1;0])
  • We need to optimize the storage as we go in case the transformation collapses holes

DocumentDB vs MongoDB

From AWS gives open source the middle finger:

Bypassing  MongoDB’s licensing by going for API comparability, given that  AWS  knows exactly why MongoDB did that, was always going to be a   controversial move and won’t endear the company to the open-source   community.

MongoDB is hugely popular, although entirely  for the wrong reasons in my mind, and it's kind of hard to scale it up  without infrastructure expertise, which is why it makes sense for a  company to offer some kind of a turnkey solution. Going for  compatibility rather than using the original code also makes a lot of  sense when you're an infrastructure-oriented business, because your own  code tends to be more tailored to your specific resources.

But in  terms of how-it-looks, after having repeatedly been accused of leeching  off open-source, this isn't great. One of the richest services divisions  out there, offloading R&D to the OSS community, then, once the  concept proves to be a potential goldmine, undercutting the original?

The  global trend of big companies is to acknowledge the influence of  open-source in our field and give back. Some do it because they believe  in it, some because they benefit from fresh (or unpaid) eyes, some  because of "optics" (newest trendy term for "public relations"). I'm not  sure that being branded as the only OSS-hostile name in the biz' is a  wise move.