Commit b410f3cd authored by Jeremy Jackson's avatar Jeremy Jackson Committed by Amy Qualls

Add hard wraps to lines

To minimize merge conflicts, I'm capturing the line wraps in a
separate commit, and will start tackling my edits in the next commit.
parent 137f4b5e
---
stage: Growth
group: Activation
info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#assignments
---
# Create an A/B test with `Experimentation Module`
## Implement the experiment
1. Add the experiment to the `Gitlab::Experimentation::EXPERIMENTS` hash in
[`experimentation.rb`](https://gitlab.com/gitlab-org/gitlab/blob/master/lib%2Fgitlab%2Fexperimentation.rb):
```ruby
EXPERIMENTS = {
other_experiment: {
#...
},
# Add your experiment here:
signup_flow: {
tracking_category: 'Growth::Activation::Experiment::SignUpFlow' # Used for providing the category when setting up tracking data
}
}.freeze
```
1. Use the experiment in the code.
Experiments can be performed on a `subject`. The provided `subject` should
respond to `to_global_id` or `to_s`.
The resulting string is bucketed and assigned to either the control or the
experimental group, so you must always provide the same `subject`
for an experiment to have the same experience.
1. Use this standard for the experiment in a controller:
- Experiment run for a user:
```ruby
class ProjectController < ApplicationController
def show
# experiment_enabled?(:experiment_key) is also available in views and helpers
if experiment_enabled?(:signup_flow, subject: current_user)
# render the experiment
else
# render the original version
end
end
end
```
- Experiment run for a namespace:
```ruby
if experiment_enabled?(:signup_flow, subject: namespace)
# experiment code
else
# control code
end
```
When no subject is given, it falls back to a cookie that gets set and is consistent until
the cookie gets deleted.
```ruby
class RegistrationController < ApplicationController
def show
# falls back to a cookie
if experiment_enabled?(:signup_flow)
# render the experiment
else
# render the original version
end
end
end
```
1. Make the experiment available to the frontend in a controller. This example
checks whether the experiment is enabled and pushes the result to the frontend:
```ruby
before_action do
push_frontend_experiment(:signup_flow, subject: current_user)
end
```
You can check the state of the feature flag in JavaScript:
```javascript
import { isExperimentEnabled } from '~/experimentation';
if ( isExperimentEnabled('signupFlow') ) {
// ...
}
```
You can also run an experiment outside of the controller scope, such as in a worker:
```ruby
class SomeWorker
def perform
# Check if the experiment is active at all (the percentage_of_time_value > 0)
return unless Gitlab::Experimentation.active?(:experiment_key)
# Since we cannot access cookies in a worker, we need to bucket models
# based on a unique, unchanging attribute instead.
# It is therefore necessery to always provide the same subject.
if Gitlab::Experimentation.in_experiment_group?(:experiment_key, subject: user)
# execute experimental code
else
# execute control code
end
end
end
```
## Implement tracking events
To determine whether the experiment is a success or not, we must implement tracking events
to acquire data for analyzing. We can send events to Snowplow via either the backend or frontend.
Read the [product intelligence guide](https://about.gitlab.com/handbook/product/product-intelligence-guide/) for more details.
### Track backend events
The framework provides a helper method that is available in controllers:
```ruby
before_action do
track_experiment_event(:signup_flow, 'action', 'value', subject: current_user)
end
```
To test it:
```ruby
context 'when the experiment is active and the user is in the experimental group' do
before do
stub_experiment(signup_flow: true)
stub_experiment_for_subject(signup_flow: true)
end
it 'tracks an event', :snowplow do
subject
expect_snowplow_event(
category: 'Growth::Activation::Experiment::SignUpFlow',
action: 'action',
value: 'value',
label: 'experimentation_subject_id',
property: 'experimental_group'
)
end
end
```
### Track frontend events
The framework provides a helper method that is available in controllers:
```ruby
before_action do
push_frontend_experiment(:signup_flow, subject: current_user)
frontend_experimentation_tracking_data(:signup_flow, 'action', 'value', subject: current_user)
end
```
This pushes tracking data to `gon.experiments` and `gon.tracking_data`.
```ruby
expect(Gon.experiments['signupFlow']).to eq(true)
expect(Gon.tracking_data).to eq(
{
category: 'Growth::Activation::Experiment::SignUpFlow',
action: 'action',
value: 'value',
label: 'experimentation_subject_id',
property: 'experimental_group'
}
)
```
To track it:
```javascript
import { isExperimentEnabled } from '~/lib/utils/experimentation';
import Tracking from '~/tracking';
document.addEventListener('DOMContentLoaded', () => {
const signupFlowExperimentEnabled = isExperimentEnabled('signupFlow');
if (signupFlowExperimentEnabled && gon.tracking_data) {
const { category, action, ...data } = gon.tracking_data;
Tracking.event(category, action, data);
}
}
```
To test it in Jest:
```javascript
import { withGonExperiment } from 'helpers/experimentation_helper';
import Tracking from '~/tracking';
describe('event tracking', () => {
describe('with tracking data', () => {
withGonExperiment('signupFlow');
beforeEach(() => {
jest.spyOn(Tracking, 'event').mockImplementation(() => {});
gon.tracking_data = {
category: 'Growth::Activation::Experiment::SignUpFlow',
action: 'action',
value: 'value',
label: 'experimentation_subject_id',
property: 'experimental_group'
};
});
it('should track data', () => {
performAction()
expect(Tracking.event).toHaveBeenCalledWith(
'Growth::Activation::Experiment::SignUpFlow',
'action',
{
value: 'value',
label: 'experimentation_subject_id',
property: 'experimental_group'
},
);
});
});
});
```
## Record experiment user
In addition to the anonymous tracking of events, we can also record which users
have participated in which experiments, and whether they were given the control
experience or the experimental experience.
The `record_experiment_user` helper method is available to all controllers, and it
enables you to record these experiment participants (the current user) and which
experience they were given:
```ruby
before_action do
record_experiment_user(:signup_flow)
end
```
Subsequent calls to this method for the same experiment and the same user have no
effect unless the user is then enrolled into a different experience. This happens
when we roll out the experimental experience to a greater percentage of users.
This data is completely separate from the [events tracking data](#implement-tracking-events).
They are not linked together in any way.
### Add context
You can add arbitrary context data in a hash which gets stored as part of the experiment
user record. New calls to the `record_experiment_user` with newer contexts are merged
deeply into the existing context.
This data can then be used by data analytics dashboards.
```ruby
before_action do
record_experiment_user(:signup_flow, foo: 42, bar: { a: 22})
# context is { "foo" => 42, "bar" => { "a" => 22 }}
end
# Additional contexts for newer record calls are merged deeply
record_experiment_user(:signup_flow, foo: 40, bar: { b: 2 }, thor: 3)
# context becomes { "foo" => 40, "bar" => { "a" => 22, "b" => 2 }, "thor" => 3}
```
## Record experiment conversion event
Along with the tracking of backend and frontend events and the
[recording of experiment participants](#record-experiment-user), we can also record
when a user performs the desired conversion event action. For example:
- **Experimental experience:** Show an in-product nudge to test if the change causes more
people to sign up for trials.
- **Conversion event:** The user starts a trial.
The `record_experiment_conversion_event` helper method is available to all controllers.
Use it to record the conversion event for the current user, regardless of whether
the user is in the control or experimental group:
```ruby
before_action do
record_experiment_conversion_event(:signup_flow)
end
```
Note that the use of this method requires that we have first
[recorded the user](#record-experiment-user) as being part of the experiment.
## Enable the experiment
After all merge requests have been merged, use [ChatOps](../../ci/chatops/index.md) in the
[appropriate channel](../feature_flags/controls.md#communicate-the-change) to start the experiment for 10% of the users.
The feature flag should have the name of the experiment with the `_experiment_percentage` suffix appended.
For visibility, share any commands run against production in the `#s_growth` channel:
```shell
/chatops run feature set signup_flow_experiment_percentage 10
```
If you notice issues with the experiment, you can disable the experiment by removing the feature flag:
```shell
/chatops run feature delete signup_flow_experiment_percentage
```
## Add user to experiment group manually
To force the application to add your current user into the experiment group,
add a query string parameter to the path where the experiment runs. If you add the
query string parameter, the experiment works only for this request, and doesn't work
after following links or submitting forms.
For example, to forcibly enable the `EXPERIMENT_KEY` experiment, add `force_experiment=EXPERIMENT_KEY`
to the URL:
```shell
https://gitlab.com/<EXPERIMENT_ENTRY_URL>?force_experiment=<EXPERIMENT_KEY>
```
## Add user to experiment group with a cookie
You can force the current user into the experiment group for `<EXPERIMENT_KEY>`
during the browser session by using your browser's developer tools:
```javascript
document.cookie = "force_experiment=<EXPERIMENT_KEY>; path=/";
```
Use a comma to list more than one experiment to be forced:
```javascript
document.cookie = "force_experiment=<EXPERIMENT_KEY>,<ANOTHER_EXPERIMENT_KEY>; path=/";
```
To clear the experiments, unset the `force_experiment` cookie:
```javascript
document.cookie = "force_experiment=; path=/";
```
## Testing and test helpers
### RSpec
Use the following in RSpec to mock the experiment:
```ruby
context 'when the experiment is active' do
before do
stub_experiment(signup_flow: true)
end
context 'when the user is in the experimental group' do
before do
stub_experiment_for_subject(signup_flow: true)
end
it { is_expected.to do_experimental_thing }
end
context 'when the user is in the control group' do
before do
stub_experiment_for_subject(signup_flow: false)
end
it { is_expected.to do_control_thing }
end
end
```
### Jest
Use the following in Jest to mock the experiment:
```javascript
import { withGonExperiment } from 'helpers/experimentation_helper';
describe('given experiment is enabled', () => {
withGonExperiment('signupFlow');
it('should do the experimental thing', () => {
expect(wrapper.find('.js-some-experiment-triggered-element')).toEqual(expect.any(Element));
});
});
```
---
stage: Growth
group: Adoption
info: To determine the technical writer assigned to the Stage/Group associated with this page, see https://about.gitlab.com/handbook/engineering/ux/technical-writing/#assignments
---
# Implementing an A/B/n experiment using GLEX
## Introduction
`Gitlab::Experiment` (GLEX) is tightly coupled with the concepts provided by
[Feature flags in development of GitLab](../feature_flags/index.md). Here, we refer
to this layer as feature flags, and may also use the term Flipper, because we
built our development and experiment feature flags atop it.
You're strongly encouraged to read and understand the
[Feature flags in development of GitLab](../feature_flags/index.md) portion of the
documentation before considering running experiments. Experiments add additional
concepts which may seem confusing or advanced without understanding the underpinnings
of how GitLab uses feature flags in development. One concept: GLEX supports multivariate
experiments, which are sometimes referred to as A/B/n tests.
The [`gitlab-experiment` project](https://gitlab.com/gitlab-org/gitlab-experiment)
exists in a separate repository, so it can be shared across any GitLab property that uses
Ruby. You should feel comfortable reading the documentation on that project as well
if you want to dig into more advanced topics.
## Glossary of terms
To ensure a shared language, you should understand these fundamental terms we use
when communicating about experiments:
- `experiment`: Any deviation of code paths we want to run at some times, but not others.
- `context`: A consistent experience we provide in an experiment.
- `control`: The default, or "original" code path.
- `candidate`: Defines an experiment with only one code path.
- `variant(s)`: Defines an experiment with multiple code paths.
### How it works
Use this decision tree diagram to understand how GLEX works. When an experiment runs,
the following logic is executed to determine what variant should be provided,
given how the experiment has been defined and using the provided context:
```mermaid
graph TD
GP[General Pool/Population] --> Enabled?
Running? -->|Yes| Cached?[Cached? / Pre-segmented?]
Running? -->|No| Excluded[Control / No Tracking]
Cached? -->|No| Excluded?
Cached? -->|Yes| Cached[Cached Value]
Excluded? -->|Yes / Cached| Excluded
Excluded? -->|No| Segmented?
Segmented? -->|Yes / Cached| VariantA
Segmented? -->|No| Included?[Experiment Group?]
Included? -->|Yes| Rollout
Included? -->|No| Control
Rollout -->|Cached| VariantA
Rollout -->|Cached| VariantB
Rollout -->|Cached| VariantC
classDef included fill:#380d75,color:#ffffff,stroke:none
classDef excluded fill:#fca121,stroke:none
classDef cached fill:#2e2e2e,color:#ffffff,stroke:none
classDef default fill:#fff,stroke:#6e49cb
class VariantA,VariantB,VariantC included
class Control,Excluded excluded
class Cached cached
```
## Implement an experiment
Start by generating a feature flag using the `bin/feature-flag` command as you
normally would for a development feature flag, making sure to use `experiment` for
the type. For the sake of documentation let's name our feature flag (and experiment)
"pill_color".
```shell
bin/feature-flag pill_color -t experiment
```
After you generate the desired feature flag, you can immediately implement an
experiment in code. An experiment implementation can be as simple as:
```ruby
experiment(:pill_color, actor: current_user) do |e|
e.use { 'control' }
e.try(:red) { 'red' }
e.try(:blue) { 'blue' }
end
```
When this code executes, the experiment is run, a variant is assigned, and (if within a
controller or view) a `window.gon.experiment.pillColor` object will be available in the
client layer, with details like:
- The assigned variant.
- The context key for client tracking events.
In addition, when an experiment runs, an event is tracked for
the experiment `:assignment`. We cover more about events, tracking, and
the client layer later.
In local development, you can make the experiment active by using the feature flag
interface. You can also target specific cases by providing the relevant experiment
to the call to enable the feature flag:
```ruby
# Enable for everyone
Feature.enable(:pill_color)
# Get the `experiment` method -- already available in controllers, views, and mailers.
include Gitlab::Experiment::Dsl
# Enable for only the first user
Feature.enable(:pill_color, experiment(:pill_color, actor: User.first))
```
To roll out your experiment feature flag on an environment, run
the following command using ChatOps (which is covered in more depth in the
[Feature flags in development of GitLab](../feature_flags/index.md) documentation).
This command creates a scenario where half of everyone who encounters
the experiment would be assigned the _control_, 25% would be assigned the _red_
variant, and 25% would be assigned the _blue_ variant:
```slack_slash_commands
/chatops run feature set pill_color 50 --actors
```
For an even distribution in this example, change the command to set it to 66% instead
of 50.
NOTE:
To immediately stop running an experiment, use the
`/chatops run feature set pill_color false` command.
<div class="panel panel-danger">
**DANGER**
{: .panel-heading}
<div class="panel-body">
We strongly recommend using the `--actors` flag when using the ChatOps commands,
because anything else may give odd behaviors due to:
- How the caching of variant assignment is handled.
- How the default `percentage_of_time` is unpredictable and pseudo-random.
</div>
</div>
We can also implement this experiment in a HAML file with HTML wrappings:
```haml
#cta-interface
- experiment(:pill_color, actor: current_user) do |e|
- e.use do
.pill-button control
- e.try(:red) do
.pill-button.red red
- e.try(:blue) do
.pill-button.blue blue
```
### The importance of context
In our previous example experiment, our context (this is an important term) is a hash
that's set to `{ actor: current_user }`. Context must be unique based on how you
want to run your experiment, and should be understood at a lower level.
It's expected, and recommended, that you use some of these
contexts to simplify reporting:
- `{ actor: current_user }`: Assigns a variant and is "sticky" to each user
(or "client" if `current_user` is nil) who enters the experiment.
- `{ project: project }`: Assigns a variant and is "sticky" to the project currently
being viewed. If running your experiment is more useful when viewing a project,
rather than when a specific user is viewing any project, consider this approach.
- `{ group: group }`: Similar to the project example, but applies to a wider
scope of projects and users.
- `{ actor: current_user, project: project }`: Assigns a variant and is "sticky"
to the user who is viewing the given project. This creates a different variant
assignment possibility for every project that `current_user` views. Understand this
can create a large cache size if an experiment like this in a highly trafficked part
of the application.
- `{ wday: Time.current.wday }`: Assigns a variant based on the current day of the
week. In this example, it would consistently assign one variant on Friday, and a
potentially different variant on Saturday.
Context is critical to how you define and report on your experiment. It's usually
the most important aspect of how you choose to implement your experiment, so consider
it carefully, and discuss it with the wider team if needed. Also, take into account
that the context you choose affects our cache size.
After the above examples, we can state the general case: *given a specific
and consistent context, we can provide a consistent experience and track events for
that experience.* To dive a bit deeper into the implementation details: a context key
is generated from the context that's provided. Use this context key to:
- Determine the assigned variant.
- Identify events tracked against that context key.
We can think about this as the experience that we've rendered, which is both dictated
and tracked by the context key. The context key is used to track the interaction and
results of the experience we've rendered to that context key. These concepts are
somewhat abstract and hard to understand initially, but this approach enables us to
communicate about experiments as something that's wider than just user behavior.
NOTE:
Using `actor:` utilizes cookies if the `current_user` is nil. If you don't need
cookies though - meaning that the exposed functionality would only be visible to
signed in users - `{ user: current_user }` would be just as effective.
WARNING:
The caching of variant assignment is done by using this context, and so consider
your impact on the cache size when defining your experiment. If you use
`{ time: Time.current }` you would be inflating the cache size every time the
experiment is run. Not only that, your experiment would not be "sticky" and events
wouldn't be resolvable.
### Advanced experimentation
GLEX allows for two general implementation styles:
1. The simple experiment style described previously.
1. A more advanced style where an experiment class can be provided.
The advanced style is handled by naming convention, and works similar to what you
would expect in Rails.
To generate a custom experiment class that can override the defaults in
`ApplicationExperiment` (our base GLEX implementation), use the rails generator:
```shell
rails generate gitlab:experiment pill_color control red blue
```
This generates an experiment class in `app/experiments/pill_color_experiment.rb`
with the variants (or _behaviors_) we've provided to the generator. Here's an example
of how that class would look after migrating the previous example into it:
```ruby
class PillColorExperiment < ApplicationExperiment
def control_behavior
'control'
end
def red_behavior
'red'
end
def blue_behavior
'blue'
end
end
```
We can now simplify where we run our experiment to the following call, instead of
providing the block we were initially providing, by explicitly calling `run`:
```ruby
experiment(:pill_color, actor: current_user).run
```
The _behavior_ methods we defined in our experiment class represent the default
implementation. You can still use the block syntax to override these _behavior_
methods however, so the following would also be valid:
```ruby
experiment(:pill_color, actor: current_user) do |e|
e.use { '<strong>control</strong>' }
end
```
NOTE:
When passing a block to the `experiment` method, it is implicitly invoked as
if `run` has been called.
#### Segmentation rules
You can use runtime segmentation rules to, for instance, segment contexts into a specific
variant. The `segment` method is a callback (like `before_action`) and so allows providing
a block or method name.
In this example, any user named `'Richard'` would always be assigned the _red_
variant, and any account older than 2 weeks old would be assigned the _blue_ variant:
```ruby
class PillColorExperiment < ApplicationExperiment
segment(variant: :red) { context.actor.first_name == 'Richard' }
segment :old_account?, variant: :blue
# ...behaviors
private
def old_account?
context.actor.created_at < 2.weeks.ago
end
end
```
When an experiment runs, the segmentation rules are executed in the order they're
defined. The first segmentation rule to produce a truthy result assigns the variant.
In our example, any user named `'Richard'`, regardless of account age, will always
be assigned the _red_ variant. If you want the opposite logic, flip the order.
NOTE:
Keep in mind when defining segmentation rules: after a truthy result, the remaining
segmentation rules are skipped to achieve optimal performance.
#### Exclusion rules
Exclusion rules are similar to segmentation rules, but are intended to determine
if a context should even be considered as something we should include in the experiment
and track events toward. Exclusion means we don't care about the events in relation
to the given context.
These examples exclude all users named `'Richard'`, *and* any account
older than 2 weeks old. Not only are they given the control behavior - which could
be nothing - but no events are tracked in these cases as well.
```ruby
class PillColorExperiment < ApplicationExperiment
exclude :old_account?, ->{ context.actor.first_name == 'Richard' }
# ...behaviors
private
def old_account?
context.actor.created_at < 2.weeks.ago
end
end
```
We can also do exclusion when we run the experiment. For instance,
if we wanted to prevent the inclusion of non-administrators in an experiment, consider
the following experiment. This type of logic enables us to do complex experiments
while preventing us from passing things into our experiments, because
we want to minimize passing things into our experiments:
```ruby
experiment(:pill_color, actor: current_user) do |e|
e.exclude! unless can?(current_user, :admin_project, project)
end
```
You may also need to check exclusion in custom tracking logic by calling `should_track?`:
```ruby
class PillColorExperiment < ApplicationExperiment
# ...behaviors
def expensive_tracking_logic
return unless should_track?
track(:my_event, value: expensive_method_call)
end
end
```
Exclusion rules aren't the best way to determine if an experiment is active. Override
the `enabled?` method for a high-level way of determining if an experiment should
run and track. Make the `enabled?` check as efficient as possible because it's the
first early opt-out path an experiment can implement.
### Tracking events
One of the most important aspects of experiments is gathering data and reporting on
it. GLEX provides an interface that allows tracking events across an experiment.
You can implement it consistently if you provide the same context between
calls to your experiment. If you do not yet understand context, you should read
about contexts now.
We can assume we run the experiment in one or a few places, but
track events potentially in many places. The tracking call remains the same, with
the arguments you would normally use when
[tracking events using snowplow](../snowplow.md). The easiest example
of tracking an event in Ruby would be:
```ruby
experiment(:pill_color, actor: current_user).track(:created)
```
When you run an experiment with any of these examples, an `:assigned` event
is tracked automatically by default. All events that are tracked from an
experiment have a special
[experiment context](https://gitlab.com/gitlab-org/iglu/-/blob/master/public/schemas/com.gitlab/gitlab_experiment/jsonschema/1-0-0)
added to the event. This can be used - typically by the data team - to create a connection
between the events on a given experiment.
If our current user hasn't encountered the experiment yet (meaning where the experiment
is run), and we track an event for them, they are assigned a variant and see
that variant if they ever encountered the experiment later, when an `:assignment`
event would be tracked at that time for them.
NOTE:
GitLab tries to be sensitive and respectful of our customers regarding tracking,
so GLEX allows us to implement an experiment without ever tracking identifying
IDs. It's not always possible, though, based on experiment reporting requirements.
You may be asked from time to time to track a specific record ID in experiments.
The approach is largely up to the PM and engineer creating the implementation.
No recommendations are provided here at this time.
## Test with RSpec
This gem provides some RSpec helpers and custom matchers. These are in flux as of GitLab 13.10.
First, require the RSpec support file to mix in some of the basics:
```ruby
require 'gitlab/experiment/rspec'
```
You still need to include matchers and other aspects, which happens
automatically for files in `spec/experiments`, but for other files and specs
you want to include it in, you can specify the `:experiment` type:
```ruby
it "tests", :experiment do
end
```
### Stub helpers
You can stub experiments using `stub_experiments`. Pass it a hash using experiment
names as the keys, and the variants you want each to resolve to, as the values:
```ruby
# Ensures the experiments named `:example` & `:example2` are both
# "enabled" and that each will resolve to the given variant
# (`:my_variant` & `:control` respectively).
stub_experiments(example: :my_variant, example2: :control)
experiment(:example) do |e|
e.enabled? # => true
e.variant.name # => 'my_variant'
end
experiment(:example2) do |e|
e.enabled? # => true
e.variant.name # => 'control'
end
```
### Exclusion and segmentation matchers
You can also test the exclusion and segmentation matchers.
```ruby
class ExampleExperiment < ApplicationExperiment
exclude { context.actor.first_name == 'Richard' }
segment(variant: :candidate) { context.actor.username == 'jejacks0n' }
end
excluded = double(username: 'rdiggitty', first_name: 'Richard')
segmented = double(username: 'jejacks0n', first_name: 'Jeremy')
# exclude matcher
expect(experiment(:example)).to exclude(actor: excluded)
expect(experiment(:example)).not_to exclude(actor: segmented)
# segment matcher
expect(experiment(:example)).to segment(actor: segmented).into(:candidate)
expect(experiment(:example)).not_to segment(actor: excluded)
```
### Tracking matcher
Tracking events is a major aspect of experimentation. We try
to provide a flexible way to ensure your tracking calls are covered.
You can do this on the instance level or at an "any instance" level:
```ruby
subject = experiment(:example)
expect(subject).to track(:my_event)
subject.track(:my_event)
```
You can use the `on_any_instance` chain method to specify that it could happen on
any instance of the experiment. This helps you if you're calling
`experiment(:example).track` downstream:
```ruby
expect(experiment(:example)).to track(:my_event).on_any_instance
experiment(:example).track(:my_event)
```
A full example of the methods you can chain onto the `track` matcher:
```ruby
expect(experiment(:example)).to track(:my_event, value: 1, property: '_property_')
.on_any_instance
.with_context(foo: :bar)
.for(:variant_name)
experiment(:example, :variant_name, foo: :bar).track(:my_event, value: 1, property: '_property_')
```
## Experiments in the client layer
This is in flux as of GitLab 13.10, and can't be documented just yet.
Any experiment that's been run in the request lifecycle surfaces in `window.gon.experiment`,
and matches [this schema](https://gitlab.com/gitlab-org/iglu/-/blob/master/public/schemas/com.gitlab/gitlab_experiment/jsonschema/1-0-0)
so you can use it when resolving some concepts around experimentation in the client layer.
## Notes on feature flags
NOTE:
We use the terms "enabled" and "disabled" here, even though it's against our
[documentation style guide recommendations](../documentation/styleguide/index.md#avoid-ableist-language)
because these are the terms that the feature flag documentation uses.
You may already be familiar with the concept of feature flags in GitLab, but using
feature flags in experiments is a bit different. While in general terms, a feature flag
is viewed as being either `on` or `off`, this isn't accurate for experiments.
Generally, `off` means that when we ask if a feature flag is enabled, it will always
return `false`, and `on` means that it will always return `true`. An interim state,
considered `conditional`, also exists. GLEX takes advantage of this trinary state of
feature flags. To understand this `conditional` aspect: consider that either of these
settings puts a feature flag into this state:
- Setting a `percentage_of_actors` of any percent greater than 0%.
- Enabling it for a single user or group.
Conditional means that it returns `true` in some situations, but not all situations.
When a feature flag is disabled (meaning the state is `off`), the experiment is
considered _inactive_. You can visualize this in the [decision tree diagram](#how-it-works)
as reaching the first [Running?] node, and traversing the negative path.
When a feature flag is rolled out to a `percentage_of_actors` or similar (meaning the
state is `conditional`) the experiment is considered to be _running_
where sometimes the control is assigned, and sometimes the candidate is assigned.
We don't refer to this as being enabled, because that's a confusing and overloaded
term here. In the experiment terms, our experiment is _running_, and the feature flag is
`conditional`.
When a feature flag is enabled (meaning the state is `on`), the candidate will always be
assigned.
We should try to be consistent with our terms, and so for experiments, we have an
_inactive_ experiment until we set the feature flag to `conditional`. After which,
our experiment is then considered _running_. If you choose to "enable" your feature flag,
you should consider the experiment to be _resolved_, because everyone is assigned
the candidate unless they've opted out of experimentation.
As of GitLab 13.10, work is being done to improve this process and how we communicate
about it.
......@@ -36,396 +36,27 @@ and link to the issue that resolves the experiment. If the experiment is
successful and becomes part of the product, any follow up issues should be
addressed.
## Implement an experiment
## Implementing an experiment
There are two options to conduct experiments:
There are currently two options when implementing an experiment.
1. [GitLab Experiment](https://gitlab.com/gitlab-org/gitlab-experiment/) is a gem included in GitLab.
1. [`Experimentation Module`](https://gitlab.com/gitlab-org/gitlab/blob/master/lib%2Fgitlab%2Fexperimentation.rb) is built in the GitLab codebase.
One is built into GitLab directly and has been around for a while (this is called
`Exerimentation Module`), and the other is provided by
[`gitlab-experiment`](https://gitlab.com/gitlab-org/gitlab-experiment) and is referred
to as `Gitlab::Experiment` -- GLEX for short.
Both methods use [experiment](../feature_flags/development.md#experiment-type) feature flags.
Both approaches use [experiment](../feature_flags/development.md#experiment-type)
feature flags, and there is currently no strong suggestion to use one over the other.
Historical Context: `Experimentation Module` was built iteratively with the needs that appeared while implementing Growth sub-department experiments. The `gitlab-experiment` gem was built with the learnings of the `Experimentation Module` and an easier to use API.
Currently both methods for running experiments are included in the codebase. The features are slightly different:
| Feature | `Experiment Module` | `gitlab-experiment` |
| ------ | ------ | ------ |
| Record user grouping | Yes | No (not natively) |
| Feature | `Experimentation Module` | GLEX |
| -------------------- |------------------------- | ---- |
| Record user grouping | Yes | No |
| Uses feature flags | Yes | Yes |
| Multivariate | No | Yes |
However, there is currently no strong suggestion to use one over the other.
### Experiments using `gitlab-experiment` **(FREE SAAS)**
> - [Introduced](https://gitlab.com/gitlab-org/gitlab/-/issues/300383) in GitLab 13.7.
> - It's [deployed behind a feature flag](../../user/feature_flags.md), disabled by default.
> - It's enabled on GitLab.com.
> - It is not yet intended for use in GitLab self-managed instances.
You find out how to conduct experiments using `gitlab-experiment` in the [README](https://gitlab.com/gitlab-org/gitlab-experiment/-/blob/master/README.md).
### Experiments using the `Experimentation Module`
1. Add the experiment to the `Gitlab::Experimentation::EXPERIMENTS` hash in the [`Experimentation Module`](https://gitlab.com/gitlab-org/gitlab/blob/master/lib%2Fgitlab%2Fexperimentation.rb):
```ruby
EXPERIMENTS = {
other_experiment: {
#...
},
# Add your experiment here:
signup_flow: {
tracking_category: 'Growth::Activation::Experiment::SignUpFlow' # Used for providing the category when setting up tracking data
}
}.freeze
```
1. Use the experiment in the code.
Experiments can be performed on a `subject`. The `subject` that gets provided needs to respond to `to_global_id` or `to_s`.
The resulting string is bucketed and assigned to either the control or the experimental group. It's therefore necessary to always provide the same `subject` for an experiment to have the same experience.
- Use this standard for the experiment in a controller:
Experiment run for a user:
```ruby
class ProjectController < ApplicationController
def show
# experiment_enabled?(:experiment_key) is also available in views and helpers
if experiment_enabled?(:signup_flow, subject: current_user)
# render the experiment
else
# render the original version
end
end
end
```
or experiment run for a namespace:
```ruby
if experiment_enabled?(:signup_flow, subject: namespace)
# experiment code
else
# control code
end
```
When no subject is given, it falls back to a cookie that gets set and is consistent until
the cookie gets deleted.
```ruby
class RegistrationController < ApplicationController
def show
# falls back to a cookie
if experiment_enabled?(:signup_flow)
# render the experiment
else
# render the original version
end
end
end
```
- Make the experiment available to the frontend in a controller:
```ruby
before_action do
push_frontend_experiment(:signup_flow, subject: current_user)
end
```
The above checks whether the experiment is enabled and pushes the result to the frontend.
The Frontend helpers for this are no longer used in production.
[More details TBD](https://gitlab.com/gitlab-org/gitlab/-/issues/323934).
- It is also possible to run an experiment outside of the controller scope, for example in a worker:
```ruby
class SomeWorker
def perform
# Check if the experiment is active at all (the percentage_of_time_value > 0)
return unless Gitlab::Experimentation.active?(:experiment_key)
# Since we cannot access cookies in a worker, we need to bucket models based on a unique, unchanging attribute instead.
# It is therefore necessery to always provide the same subject.
if Gitlab::Experimentation.in_experiment_group?(:experiment_key, subject: user)
# execute experimental code
else
# execute control code
end
end
end
```
#### Implement the tracking events
To determine whether the experiment is a success or not, we must implement tracking events
to acquire data for analyzing. We can send events to Snowplow via either the backend or frontend.
Read the [product intelligence guide](https://about.gitlab.com/handbook/product/product-intelligence-guide/) for more details.
##### Track backend events
The framework provides the following helper method that is available in controllers:
```ruby
before_action do
track_experiment_event(:signup_flow, 'action', 'value', subject: current_user)
end
```
Which can be tested as follows:
```ruby
context 'when the experiment is active and the user is in the experimental group' do
before do
stub_experiment(signup_flow: true)
stub_experiment_for_subject(signup_flow: true)
end
it 'tracks an event', :snowplow do
subject
expect_snowplow_event(
category: 'Growth::Activation::Experiment::SignUpFlow',
action: 'action',
value: 'value',
label: 'experimentation_subject_id',
property: 'experimental_group'
)
end
end
```
##### Track frontend events
The framework provides the following helper method that is available in controllers:
```ruby
before_action do
push_frontend_experiment(:signup_flow, subject: current_user)
frontend_experimentation_tracking_data(:signup_flow, 'action', 'value', subject: current_user)
end
```
This pushes tracking data to `gon.experiments` and `gon.tracking_data`.
```ruby
expect(Gon.experiments['signupFlow']).to eq(true)
expect(Gon.tracking_data).to eq(
{
category: 'Growth::Activation::Experiment::SignUpFlow',
action: 'action',
value: 'value',
label: 'experimentation_subject_id',
property: 'experimental_group'
}
)
```
Which can then be used for tracking as follows:
```javascript
import Tracking from '~/tracking';
document.addEventListener('DOMContentLoaded', () => {
const signupFlowExperimentEnabled = gon.experiments['signupFlow'];
if (signupFlowExperimentEnabled && gon.tracking_data) {
const { category, action, ...data } = gon.tracking_data;
Tracking.event(category, action, data);
}
}
```
Which can be tested in Jest as follows:
```javascript
import { withGonExperiment } from 'helpers/experimentation_helper';
import Tracking from '~/tracking';
describe('event tracking', () => {
describe('with tracking data', () => {
withGonExperiment('signupFlow');
beforeEach(() => {
jest.spyOn(Tracking, 'event').mockImplementation(() => {});
gon.tracking_data = {
category: 'Growth::Activation::Experiment::SignUpFlow',
action: 'action',
value: 'value',
label: 'experimentation_subject_id',
property: 'experimental_group'
};
});
it('should track data', () => {
performAction()
expect(Tracking.event).toHaveBeenCalledWith(
'Growth::Activation::Experiment::SignUpFlow',
'action',
{
value: 'value',
label: 'experimentation_subject_id',
property: 'experimental_group'
},
);
});
});
});
```
#### Record experiment user
In addition to the anonymous tracking of events, we can also record which users have participated in which experiments and whether they were given the control experience or the experimental experience.
The `record_experiment_user` helper method is available to all controllers, and it enables you to record these experiment participants (the current user) and which experience they were given:
```ruby
before_action do
record_experiment_user(:signup_flow)
end
```
Subsequent calls to this method for the same experiment and the same user have no effect unless the user has gets enrolled into a different experience. This happens when we roll out the experimental experience to a greater percentage of users.
Note that this data is completely separate from the [events tracking data](#implement-the-tracking-events). They are not linked together in any way.
##### Add context
You can add arbitrary context data in a hash which gets stored as part of the experiment user record. New calls to the `record_experiment_user` with newer contexts get merged deeply into the existing context.
This data can then be used by data analytics dashboards.
```ruby
before_action do
record_experiment_user(:signup_flow, foo: 42, bar: { a: 22})
# context is { "foo" => 42, "bar" => { "a" => 22 }}
end
# Additional contexts for newer record calls are merged deeply
record_experiment_user(:signup_flow, foo: 40, bar: { b: 2 }, thor: 3)
# context becomes { "foo" => 40, "bar" => { "a" => 22, "b" => 2 }, "thor" => 3}
```
#### Record experiment conversion event
Along with the tracking of backend and frontend events and the [recording of experiment participants](#record-experiment-user), we can also record when a user performs the desired conversion event action. For example:
- **Experimental experience:** Show an in-product nudge to see if it causes more people to sign up for trials.
- **Conversion event:** The user starts a trial.
The `record_experiment_conversion_event` helper method is available to all controllers. It enables us to record the conversion event for the current user, regardless of whether they are in the control or experimental group:
```ruby
before_action do
record_experiment_conversion_event(:signup_flow)
end
```
Note that the use of this method requires that we have first [recorded the user as being part of the experiment](#record-experiment-user).
#### Enable the experiment
After all merge requests have been merged, use [`chatops`](../../ci/chatops/index.md) in the
[appropriate channel](../feature_flags/controls.md#communicate-the-change) to start the experiment for 10% of the users.
The feature flag should have the name of the experiment with the `_experiment_percentage` suffix appended.
For visibility, please also share any commands run against production in the `#s_growth` channel:
```shell
/chatops run feature set signup_flow_experiment_percentage 10
```
If you notice issues with the experiment, you can disable the experiment by removing the feature flag:
```shell
/chatops run feature delete signup_flow_experiment_percentage
```
#### Manually force the current user to be in the experiment group
You may force the application to put your current user in the experiment group. To do so
add a query string parameter to the path where the experiment runs. If you do so,
the experiment will work only for this request and won't work after following links or submitting forms.
For example, to forcibly enable the `EXPERIMENT_KEY` experiment, add `force_experiment=EXPERIMENT_KEY`
to the URL:
```shell
https://gitlab.com/<EXPERIMENT_ENTRY_URL>?force_experiment=<EXPERIMENT_KEY>
```
#### A cookie-based approach to force an experiment
It's possible to force the current user to be in the experiment group for `<EXPERIMENT_KEY>`
during the browser session by using your browser's developer tools:
```javascript
document.cookie = "force_experiment=<EXPERIMENT_KEY>; path=/";
```
Use a comma to list more than one experiment to be forced:
```javascript
document.cookie = "force_experiment=<EXPERIMENT_KEY>,<ANOTHER_EXPERIMENT_KEY>; path=/";
```
To clear the experiments, unset the `force_experiment` cookie:
```javascript
document.cookie = "force_experiment=; path=/";
```
#### Testing and test helpers
##### RSpec
Use the following in RSpec to mock the experiment:
```ruby
context 'when the experiment is active' do
before do
stub_experiment(signup_flow: true)
end
context 'when the user is in the experimental group' do
before do
stub_experiment_for_subject(signup_flow: true)
end
it { is_expected.to do_experimental_thing }
end
context 'when the user is in the control group' do
before do
stub_experiment_for_subject(signup_flow: false)
end
it { is_expected.to do_control_thing }
end
end
```
##### Jest
Use the following in Jest to mock the experiment:
```javascript
import { withGonExperiment } from 'helpers/experimentation_helper';
| Multivariate (A/B/n) | No | Yes |
describe('given experiment is enabled', () => {
withGonExperiment('signupFlow');
- [Implementing an A/B experiment using `Experimentation Module`](experimentation.md)
- [Implementing an A/B/n experiment using GLEX](gitlab_experiment.md)
it('should do the experimental thing', () => {
expect(wrapper.find('.js-some-experiment-triggered-element')).toEqual(expect.any(Element));
});
});
```
Historical Context: `Experimentation Module` was built iteratively with the needs that
appeared while implementing Growth sub-department experiments, while GLEX was built
with the learnings of the team and an easier to use API.
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