Monthly Archives: May 2019

The three Rs of remote work

I started working remotely in 2012. Since then I’ve worked for big companies and small, organisations with outstanding remote working cultures, and others that probably would have difficulty spelling the word without predictive text. I broadly classify my experiences into three tiers;

Little r remote

The first kind of remote work I call little r remote.

Your company has an office, but it’s not convenient or you don’t want to work from there. It could be the commute is too long, or its in the next town over, or perhaps a short plane flight away. Sometimes you might go into the office for a day or two a week, and should something serious arise you could join your co-workers onsite for an extended period of time.

If you often hear people say they are going to work from home to get some work done, that’s little r remote.

Big R remote

The next category I call Big R remote. Big R remote differs mainly from little r remote by the tyranny of distance. It’s not impossible to visit your co-workers in person, but it is inconvenient. Meeting face to face requires a day’s flying. Passports and boarder crossings are frequently involved. The expense and distance necessitates week long sprints and commensurate periods of jetlag recuperation.

Because of timezone differences meetings must be prearranged and periods of overlap closely guarded. Communication becomes less spontaneous and care must be taken to avoid committing to unsustainable working hours.

Gothic ℜ remote

The final category is basically Big R remote working on hard mode. Everything that was hard about Big R remote, timezone, travel schedules, public holidays, daylight savings, video call latency, cultural and language barriers is multiplied for each remote worker.

In person meetings are so rare that without a focus on written asynchronous communication progress can repeatedly stall for days, if not weeks, as miscommunication leads to disillusionment and loss of trust.

In my experience, for knowledge workers, little r remote work offers many benefits over the open office hell scape du jour. Big R remote takes a serious commitment by all parties and if you are the first employee in that category you will bare most of the cost to making Big R remote work for you.

Gothic ℜ remote working should probably be avoided unless all those involved have many years of working in that style and the employer is committed to restructuring the company as a remote first organisation. It is not possible to succeed in a Gothic ℜ remote role without a culture of written communication and asynchronous decision making mandated, and consistently enforced, by the leaders of the company.

Why bother writing tests at all?

In previous posts and presentations I talked about how to test, and when to test. To conclude this series of I’m going to ask the question, why test at all?

Even if you don’t, someone will test your software

I’m sure no-one reading this post thinks that software should be delivered without being tested first. Even if that were true, your customers are going to test it, or at least use it. If nothing else, it would be good to discover any issues with the code before your customers do. If not for the reputation of your company, at least for your professional pride.

So, if we agree that software should be tested, the question becomes: who should do that testing?

The majority of testing should be performed by development teams

I argue that the majority of the testing should be done by development groups. Moreover, testing should be automated, and thus the majority of these tests should be unit style tests.

To be clear, I am not saying you shouldn’t write integration, functional, or end to end tests. I’m also not saying that you shouldn’t have a QA group, or integration test engineers. However at a recent software conference, in a room of over 1,000 engineers, nobody raised their hand when I asked if they considered themselves in a pure quality assurance role.

You might argue that the audience was self selecting, that QA engineers did not feel a software conference was relevant–or welcoming–to them. However, I think this proves my point, the days of one developer to one test engineer are gone and not coming back.

If development teams aren’t writing the majority of tests, who is?

Manual testing should not be the majority of your testing because manual testing is O(n)

Thus, if individual contributors are expected to test the software they write, why do we need to automate it? Why is a manual testing plan not good enough?

Manual testing of software or manual verification of a defect is not sufficient because it does not scale. As the number of manual tests grows, engineers are tempted to skip them or only execute the scenarios they think are could be affected. Manual testing is expensive in terms of time, thus dollars, and it is boring. 99.9% of the tests that passed last time are expected to pass again. Manual testing is looking for a needle in a haystack, except you don’t stop when you find the first needle.

This means that your first response when given a bug to fix or a feature to implement should be to write a failing test. This doesn’t need to be a unit test, but it should be an automated test. Once you’ve fixed the bug, or added the feature, now have the test case to prove it worked–and you can check them in together.

Tests are the critical component that ensure you can always ship your master branch

As a development team, you are judged on your ability to deliver working software to the business. No, seriously, the business could care less about OOP vs FP, CI/CD, table tennis or limited run La Croix.

Your super power is, at any time, anyone on the team should be confident that the master branch of your code is shippable. This means at any time they can deliver a release of your software to the business and the business can recoup its investment in your development R&D.

I cannot emphasise this enough. If you want the non technical parts of the business to believe you are heros, you must never create a situation where you say “well, we can’t release right now because we’re in the middle of an important refactoring. It’ll be a few weeks. We hope.”

Again, I’m not saying you cannot refactor, but at every stage your product must be shippable. Your tests have to pass. It may not have all the desired features, but the features that are there should work as described on the tin.

Tests lock in behaviour

Your tests are the contract about what your software does and does not do. Unit tests should lock in the behaviour of the package’s API. Integration tests do the same for complex interactions. Tests describe, in code, what the program promises to do.

If there is a unit test for each input permutation, you have defined the contract for what the code will do in code, not documentation. This is a contract anyone on your team can assert by simply running the tests. At any stage you know with a high degree of confidence that the behaviour people relied on before your change continues to function after your change.

Tests give you confidence to change someone else’s code

Lastly, and this is the biggest one, for programmers working on a piece of code that has been through many hands. Tests give you the confidence to make changes.

Even though we’ve never met, something I know about you, the reader, is you will eventually leave your current employer. Maybe you’ll be moving on to a new role, or perhaps a promotion, perhaps you’ll move cities, or follow your partner overseas. Whatever the reason, the succession of the maintenance of programs you write is key.

If people cannot maintain our code then as you and I move from job to job we’ll leave behind programs which cannot be maintained. This goes beyond advocacy for a language or tool. Programs which cannot be changed, programs which are too hard to onboard new developers, or programs which feel like career digression to work on them will reach only one end state–they are a dead end. They represent a balance sheet loss for the business. They will be replaced.

If you worry about who will maintain your code after you’re gone, write good tests.

Prefer table driven tests

I’m a big fan of testing, specifically unit testing and TDD (done correctly, of course). A practice that has grown around Go projects is the idea of a table driven test. This post explores the how and why of writing a table driven test.

Let’s say we have a function that splits strings:

// Split slices s into all substrings separated by sep and
// returns a slice of the substrings between those separators.
func Split(s, sep string) []string {
var result []string
i := strings.Index(s, sep)
for i > -1 {
result = append(result, s[:i])
s = s[i+len(sep):]
i = strings.Index(s, sep)
}
return append(result, s)
}

In Go, unit tests are just regular Go functions (with a few rules) so we write a unit test for this function starting with a file in the same directory, with the same package name, strings.

package split

import (
"reflect"
"testing"
)

func TestSplit(t *testing.T) {
got := Split("a/b/c", "/")
want := []string{"a", "b", "c"}
if !reflect.DeepEqual(want, got) {
t.Fatalf("expected: %v, got: %v", want, got)
}
}

Tests are just regular Go functions with a few rules:

  1. The name of the test function must start with Test.
  2. The test function must take one argument of type *testing.T. A *testing.T is a type injected by the testing package itself, to provide ways to print, skip, and fail the test.

In our test we call Split with some inputs, then compare it to the result we expected.

Code coverage

The next question is, what is the coverage of this package? Luckily the go tool has a built in branch coverage. We can invoke it like this:

% go test -coverprofile=c.out
PASS
coverage: 100.0% of statements
ok split 0.010s

Which tells us we have 100% branch coverage, which isn’t really surprising, there’s only one branch in this code.

If we want to dig in to the coverage report the go tool has several options to print the coverage report. We can use go tool cover -func to break down the coverage per function:

% go tool cover -func=c.out
split/split.go:8: Split 100.0%
total: (statements) 100.0%

Which isn’t that exciting as we only have one function in this package, but I’m sure you’ll find more exciting packages to test.

Spray some .bashrc on that

This pair of commands is so useful for me I have a shell alias which runs the test coverage and the report in one command:

cover () {
local t=$(mktemp -t cover)
go test $COVERFLAGS -coverprofile=$t $@ \
&& go tool cover -func=$t \
&& unlink $t
}

Going beyond 100% coverage

So, we wrote one test case, got 100% coverage, but this isn’t really the end of the story. We have good branch coverage but we probably need to test some of the boundary conditions. For example, what happens if we try to split it on comma?

func TestSplitWrongSep(t *testing.T) {
got := Split("a/b/c", ",")
want := []string{"a/b/c"}
if !reflect.DeepEqual(want, got) {
t.Fatalf("expected: %v, got: %v", want, got)
}
}

Or, what happens if there are no separators in the source string?

func TestSplitNoSep(t *testing.T) {
got := Split("abc", "/")
want := []string{"abc"}
if !reflect.DeepEqual(want, got) {
t.Fatalf("expected: %v, got: %v", want, got)
}
}

We’re starting build a set of test cases that exercise boundary conditions. This is good.

Introducing table driven tests

However the there is a lot of duplication in our tests. For each test case only the input, the expected output, and name of the test case change. Everything else is boilerplate. What we’d like to to set up all the inputs and expected outputs and feel them to a single test harness. This is a great time to introduce table driven testing.

func TestSplit(t *testing.T) {
type test struct {
input string
sep string
want []string
}

tests := []test{
{input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
{input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
{input: "abc", sep: "/", want: []string{"abc"}},
}

for _, tc := range tests {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("expected: %v, got: %v", tc.want, got)
}
}
}

We declare a structure to hold our test inputs and expected outputs. This is our table. The tests structure is usually a local declaration because we want to reuse this name for other tests in this package.

In fact, we don’t even need to give the type a name, we can use an anonymous struct literal to reduce the boilerplate like this:

func TestSplit(t *testing.T) {
tests := []struct {
input string
sep string
want []string
}{
{input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
{input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
{input: "abc", sep: "/", want: []string{"abc"}},
}

for _, tc := range tests {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("expected: %v, got: %v", tc.want, got)
}
}
}

Now, adding a new test is a straight forward matter; simply add another line the tests structure. For example, what will happen if our input string has a trailing separator?

{input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
{input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
{input: "abc", sep: "/", want: []string{"abc"}},
{input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}}, // trailing sep

But, when we run go test, we get

% go test
--- FAIL: TestSplit (0.00s)
split_test.go:24: expected: [a b c], got: [a b c ]

Putting aside the test failure, there are a few problems to talk about.

The first is by rewriting each test from a function to a row in a table we’ve lost the name of the failing test. We added a comment in the test file to call out this case, but we don’t have access to that comment in the go test output.

There are a few ways to resolve this. You’ll see a mix of styles in use in Go code bases because the table testing idiom is evolving as people continue to experiment with the form.

Enumerating test cases

As tests are stored in a slice we can print out the index of the test case in the failure message:

func TestSplit(t *testing.T) {
tests := []struct {
input string
sep . string
want []string
}{
{input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
{input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
{input: "abc", sep: "/", want: []string{"abc"}},
{input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},
}

for i, tc := range tests {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("test %d: expected: %v, got: %v", i+1, tc.want, got)
}
}
}

Now when we run go test we get this

% go test
--- FAIL: TestSplit (0.00s)
split_test.go:24: test 4: expected: [a b c], got: [a b c ]

Which is a little better. Now we know that the fourth test is failing, although we have to do a little bit of fudging because slice indexing—​and range iteration—​is zero based. This requires consistency across your test cases; if some use zero base reporting and others use one based, it’s going to be confusing. And, if the list of test cases is long, it could be difficult to count braces to figure out exactly which fixture constitutes test case number four.

Give your test cases names

Another common pattern is to include a name field in the test fixture.

func TestSplit(t *testing.T) {
tests := []struct {
name string
input string
sep string
want []string
}{
{name: "simple", input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
{name: "wrong sep", input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
{name: "no sep", input: "abc", sep: "/", want: []string{"abc"}},
{name: "trailing sep", input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},
}

for _, tc := range tests {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("%s: expected: %v, got: %v", tc.name, tc.want, got)
}
}
}

Now when the test fails we have a descriptive name for what the test was doing. We no longer have to try to figure it out from the output—​also, now have a string we can search on.

% go test
--- FAIL: TestSplit (0.00s)
split_test.go:25: trailing sep: expected: [a b c], got: [a b c ]

We can dry this up even more using a map literal syntax:

func TestSplit(t *testing.T) {
tests := map[string]struct {
input string
sep string
want []string
}
{
"simple": {input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
"wrong sep": {input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
"no sep": {input: "abc", sep: "/", want: []string{"abc"}},
"trailing sep": {input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},
}

for name, tc := range tests {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("%s: expected: %v, got: %v", name, tc.want, got)
}
}
}

Using a map literal syntax we define our test cases not as a slice of structs, but as map of test names to test fixtures. There’s also a side benefit of using a map that is going to potentially improve the utility of our tests.

Map iteration order is undefined 1 This means each time we run go test, our tests are going to be potentially run in a different order.

This is super useful for spotting conditions where test pass when run in statement order, but not otherwise. If you find that happens you probably have some global state that is being mutated by one test with subsequent tests depending on that modification.

Introducing sub tests

Before we fix the failing test there are a few other issues to address in our table driven test harness.

The first is we’re calling t.Fatalf when one of the test cases fails. This means after the first failing test case we stop testing the other cases. Because test cases are run in an undefined order, if there is a test failure, it would be nice to know if it was the only failure or just the first.

The testing package would do this for us if we go to the effort to write out each test case as its own function, but that’s quite verbose. The good news is since Go 1.7 a new feature was added that lets us do this easily for table driven tests. They’re called sub tests.

func TestSplit(t *testing.T) {
tests := map[string]struct {
input string
sep string
want []string
}{
"simple": {input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
"wrong sep": {input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
"no sep": {input: "abc", sep: "/", want: []string{"abc"}},
"trailing sep": {input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},
}

for name, tc := range tests {
t.Run(name, func(t *testing.T) {
got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("expected: %v, got: %v", tc.want, got)
}
})

}
}

As each sub test now has a name we get that name automatically printed out in any test runs.

% go test
--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:25: expected: [a b c], got: [a b c ]

Each subtest is its own anonymous function, therefore we can use t.Fatalft.Skipf, and all the other testing.Thelpers, while retaining the compactness of a table driven test.

Individual sub test cases can be executed directly

Because sub tests have a name, you can run a selection of sub tests by name using the go test -run flag.

% go test -run=.*/trailing -v
=== RUN TestSplit
=== RUN TestSplit/trailing_sep
--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:25: expected: [a b c], got: [a b c ]

Comparing what we got with what we wanted

Now we’re ready to fix the test case. Let’s look at the error.

--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:25: expected: [a b c], got: [a b c ]

Can you spot the problem? Clearly the slices are different, that’s what reflect.DeepEqual is upset about. But spotting the actual difference isn’t easy, you have to spot that extra space after c. This might look simple in this simple example, but it is any thing but when you’re comparing two complicated deeply nested gRPC structures.

We can improve the output if we switch to the %#v syntax to view the value as a Go(ish) declaration:

got := Split(tc.input, tc.sep)
if !reflect.DeepEqual(tc.want, got) {
t.Fatalf("expected: %#v, got: %#v", tc.want, got)
}

Now when we run our test it’s clear that the problem is there is an extra blank element in the slice.

% go test
--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:25: expected: []string{"a", "b", "c"}, got: []string{"a", "b", "c", ""}

But before we go to fix our test failure I want to talk a little bit more about choosing the right way to present test failures. Our Split function is simple, it takes a primitive string and returns a slice of strings, but what if it worked with structs, or worse, pointers to structs?

Here is an example where %#v does not work as well:

func main() {
type T struct {
I int
}
x := []*T{{1}, {2}, {3}}
y := []*T{{1}, {2}, {4}}
fmt.Printf("%v %v\n", x, y)
fmt.Printf("%#v %#v\n", x, y)
}

The first fmt.Printfprints the unhelpful, but expected slice of addresses; [0xc000096000 0xc000096008 0xc000096010] [0xc000096018 0xc000096020 0xc000096028]. However our %#v version doesn’t fare any better, printing a slice of addresses cast to *main.T;[]*main.T{(*main.T)(0xc000096000), (*main.T)(0xc000096008), (*main.T)(0xc000096010)} []*main.T{(*main.T)(0xc000096018), (*main.T)(0xc000096020), (*main.T)(0xc000096028)}

Because of the limitations in using any fmt.Printf verb, I want to introduce the go-cmp library from Google.

The goal of the cmp library is it is specifically to compare two values. This is similar to reflect.DeepEqual, but it has more capabilities. Using the cmp pacakge you can, of course, write:

func main() {
type T struct {
I int
}
x := []*T{{1}, {2}, {3}}
y := []*T{{1}, {2}, {4}}
fmt.Println(cmp.Equal(x, y)) // false
}

But far more useful for us with our test function is the cmp.Diff function which will produce a textual description of what is different between the two values, recursively.

func main() {
type T struct {
I int
}
x := []*T{{1}, {2}, {3}}
y := []*T{{1}, {2}, {4}}
diff := cmp.Diff(x, y)
fmt.Printf(diff)
}

Which instead produces:

% go run
{[]*main.T}[2].I:
-: 3
+: 4

Telling us that at element 2 of the slice of Ts the Ifield was expected to be 3, but was actually 4.

Putting this all together we have our table driven go-cmp test

func TestSplit(t *testing.T) {
tests := map[string]struct {
input string
sep string
want []string
}{
"simple": {input: "a/b/c", sep: "/", want: []string{"a", "b", "c"}},
"wrong sep": {input: "a/b/c", sep: ",", want: []string{"a/b/c"}},
"no sep": {input: "abc", sep: "/", want: []string{"abc"}},
"trailing sep": {input: "a/b/c/", sep: "/", want: []string{"a", "b", "c"}},
}

for name, tc := range tests {
t.Run(name, func(t *testing.T) {
got := Split(tc.input, tc.sep)
diff := cmp.Diff(tc.want, got)
if diff != "" {
t.Fatalf(diff)
}

})
}
}

Running this we get

% go test
--- FAIL: TestSplit (0.00s)
--- FAIL: TestSplit/trailing_sep (0.00s)
split_test.go:27: {[]string}[?->3]:
-: <non-existent>
+: ""
FAIL
exit status 1
FAIL split 0.006s

Using cmp.Diff our test harness isn’t just telling us that what we got and what we wanted were different. Our test is telling us that the strings are different lengths, the third index in the fixture shouldn’t exist, but the actual output we got an empty string, “”. From here fixing the test failure is straight forward.