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Informations about the package golden

PHP Golden

A PHP library for snapshot 📸 testing.

A port from

⚠️️ Caution ⚠️: This README is a Work in Progress. We are translating the original readme from Golden to the PHP version. Maybe, we have some typos or references to the Go version.

Cookbook: recipes and how-tos (WIP) 🚧

TL;DR

Snapshot testing is a technique in which the output of the subject under test is compared to a previously generated output, obtained by running the very same subject under test. The basic idea is to ensure that changes made to the code have not affected its behavior.

This is useful for:

Current Status: v0.2.x Mostly stable.

Roadmap/Pending features:

These features are in place:

For future releases:

Usage advice: Usable, stable API. May have some behavior issues.

Installation

You can use standard compose require:

Basic Usage: Verify against an auto-generated snapshot

A snapshot test is pretty easy to write. Capture the output of the subject under test in a variable and pass it to $this->verify(). Golden will take care of all. You can use any type that suits you well.

In PHP Golden is a Trait, so you have to declare that you are using the Golden trait in your PHPUnit test. That's all. Now, you have a verify method.

Sometimes, you could prefer to convert the output to string to have a snapshot more readable for a human.

How it works

The first time you run the test, a snapshot file will be generated at __snapshots/TestSomething/test_speed_of_european_parrot.snap in the same folder of the test. And the test will pass. See How snapshots are named to learn about how names are created in Golden.

The file will contain the output generated by the subject under test, provided that it can be serialized as JSON.

If the snapshot is ok for you, commit it with the code, so it can be used as comparison criteria in future runs and prevent regressions. If not, delete the file, make the changes needed in the code or tests, and run it again.

But, if you are not sure that the current output is correct, you can try the approval mode.

Basic Usage: Approval mode

Approval mode is useful when you are writing new code. In this mode, the snapshot is generated and updated but the test never passes. Why? You will need to inspect the snapshot until you are happy with it, and you, or a domain expert, approve it.

Pass the option waitApproval() to run the test in approval mode.

Once you or the domain expert approves the snapshot, remove the waitApproval() option. That's all. The last generated snapshot will be used as a criterion.

How it works

The first time you run the test, a snapshot file will be generated at __snapshots/TestSomething/test_speed_of_european_parrot.snap in the same package of the test. And the test will not pass. Subsequent runs of the test won't pass until you remove the waitApproval() option, even if there are no differences between the snapshot and the current output.

The file will contain the output generated by the subject under test, provided that it can be serialized as JSON.

If the snapshot is ok for you, remove the option waitApproval(), so it can be used as comparison criteria in future runs. If not, modify the code and run it again until the snapshot is fine.

Basic Usage: Golden Master mode

Golden Master mode is useful when you want to generate a lot of tests combining different values of the subject under test parameters. It will generate all possible combinations, creating a detailed snapshot with all the results.

You will need to create a Wrapper function that exercises the subject under test managing both parameters and all return values, including errors. You will need to manage to return a string representation of the outcome of the SUT.

Here is an example of a test in the GildedRose kata.

How it works

The first time you run the test, a snapshot file will be generated at __snapshots/GildedRoseTest/test_foo.snap.json in the same folder of the test. This will be a JSON file with a description of the inputs and outputs of each generated test. The test itself will pass except if you use the waitApproval() option. Here you have a fragment of the snapshot, in which you can see the params and the generated output.

As a bonus, you can use GoldenMaster tests in Approval mode. In fact, you can pass all the common options.

Known limitations

Inaccurate Path Coverage in Combinatorial tests

Given the way in which Golden creates and executes the combinatorial tests, if you try to use Path Coverage you will see that only one hit per line is showed in coverage reports.

What is Golden?

Golden is a library inspired by projects like Approval Tests. There are some other similar libraries out there, such as Approval Tests, Go-snaps or Cupaloy that offer similar functionality.

So... Why reinvent the wheel?

First of all, why not? I was willing to start a little side project to learn and practice some Golang things for which I didn't find time or opportunity during the daily work. For example, creating a library for distribution, resolving some questions about managing state, creating friendly APIs, managing unknown types, etc.

Second. I found some limitations in the libraries I was using (Approval tests, most of the time) that made my work a bit uncomfortable. So, I started to look for alternatives. I wanted some more flexibility and customization. At the end of the day, I decided to create my library.

Golden for go is working pretty well for me, so I decided to port the library to PHP following similar principles.

Snapshot testing

Snapshot testing is a testing technique that provides an alternative to assertion testing. In assertion testing, you compare the output of executing some unit of code with an expectation you have about it. For example, you could expect that the output would equal some value, or that it contains some text, etc.

Here you have a typical assertion for equality:

This works very well for TDD and for testing simple outputs. However, it is tedious if you need to test complex objects, generated files, and other big outputs. Also, it is not always the best tool for testing code that you don't know well or that was not created with testing in mind.

In snapshot testing, instead, you first obtain and persist the output of the current behavior of the subject under test. This is what we call a snapshot. This provides us with a regression test for that piece of code. So, if you make some changes, you can be sure that the behavior is not affected by those changes.

And this is how you achieve that:

As you can see, the main difference with the assertion is that we don't specify any expectations about the subject. verify will store the subject value in a file the first time it runs and will be used as the expected value to compare in subsequent runs.

Golden automates the snapshot creation process the first time the test is run, and uses that same snapshot as a criterion in subsequent runs. We call this workflow "Verification mode": the goal of the test is to verify that the output is the same as the first snapshot. As you can easily guess, this works as a regression test.

Snapshot testing is a very good first approach to put legacy code under test or to introduce tests in a codebase without tests. You don't need to know how that code works. You only need a way to capture the output of the existing code.

However, testing legacy or unknown code is not the only use case for snapshot testing.

Snapshot testing is a very good choice for testing complex objects or outputs such as generated HTML, XML, JSON, code, etc. Provided that you can create a file with a serialized representation of the response, you can compare the snapshot with subsequent executions of the same unit of code. Suppose you need to generate an API response with lots of data. Instead of trying to figure out how to check every possible field value, you generate a snapshot with the data. After that, you will be using that snapshot to verify the execution.

But this way of testing is weird for developing new features... How can I know that my generated output is correct at a given moment?

Approval testing

Approval testing is a variation of snapshot testing. Usually, in snapshot testing, the first time you execute the test, a new snapshot is created and the test automatically passes. Then, you make changes to the code and use the snapshot to ensure that there are no behavioral changes. This is the default behavior of Golden, as you can see.

But when we are creating new features we don't want that behavior. We build the code and check the output from time to time. The output must be reviewed to ensure that it has the required content. In this situation, is preferable to not pass the test until the generated output is reviewed by an expert.

In Approval Testing, the first test execution is not automatically passed. Instead, the snapshot is created, but you should review and approve it explicitly. This step allows you to be sure that the output is what you want or what was required. You can show it to the business people, to your client, to the user of your API, or to whoever can review it. Once you get the approval of the snapshot and re-run the test, it will pass.

You could make changes and re-run the test all the times you need until you are satisfied with the output and get approval.

I think that Approval Testing was first introduced by Llewellyn Falco. You can learn more about this technique on their website, where you can find out how to approach development with it.

How to perform Approval Testing with Golden

Imagine you are writing some code that generates a complex struct, JSON object, or another long and complex document. You need this object to be reviewed by a domain expert to ensure that it contains what is supposed to contain.

If you work like me, you probably will start by generating an empty object and adding different elements one at a time, running a test on each iteration to be sure how things are going. Using Golden means using:

Don't forget to use the Golden trait in your test.

But if you work in "verification mode" you will have to delete every snapshot that is created when running the test. Instead of that, you can use the approval mode. It is very easy: you simply have to pass the waitApproval() function as an option until the snapshot reflects exactly what you or the domain expert want.

This way, the test will always fail, and it will update the snapshot with the changes that happen in the output. This is because in approval mode you don't want to allow the test to pass. You could consider this as an iterative way to build your snapshot: you make a change, run the test, and add some bit of content to the snapshot, until you are happy with what you get.

So, how can I declare that a snapshot was approved? Easy: simply change the test back to use the verification mode once you confirm that the last snapshot is correct by removing the golden.WaitApproval() option.

Other libraries require you to use some sort of external tool to rename or mark a snapshot as approved. Golden puts this distinction to the test itself. The fact that it fails, even after you've got the approval, allows you to remember that you will need to do something with the test.

Given the nature of the approval flow, the test will always fail even if the snapshot and the current output have no differences. This means that you are not adding more changes that modify the output. Usually, this can work as a rule of thumb to consider the snapshot as approved and to remove de waitApproval() option.

Golden Master

There is another variation of snapshot testing. Golden Master is a technique introduced by Michael Feathers for working with legacy code that you don't understand. Another way of naming this kind of testing is Characterization testing.

With this technique, you can achieve high coverage really fast, so you can be sure that refactoring will be safe because you always will know if the behavior of the code is broken due to a change you introduced. The best thing is that you don't need to understand the code to test it. Once you start refactoring things and abstracting code, it will be easier to introduce classic assertion testing or even TDD, and finally remove the Golden Master tests.

The technique requires the creation of a big batch of tests for the same unit of code, employing combinations of the parameters that you need to pass to such a unit. The original Approval Tests package includes Combinations, a library that helps you to generate those combinatorial tests.

There are several techniques to guess the best values you should use. You could, for example, study the code and look for significant values in conditionals with the help of a graphical coverage tool that shows you what parts of the code are executed or not depending on the values.

Once you complete the collection of possible values for each parameter, you will use the combination tools that will generate a batch of tests for you. The amount of tests is the product of multiplying the number of values per parameter. You can easily achieve tenths and even hundreds of tests for the same code unit.

As you have probably guessed, Golden takes its name from this technique... and because it starts with "Go". Anyway, I've just found that lots of packages are using the very same name, even in the std library.

How to do Golden Master testing with Golden

A combinatorial test is a bit trickier than a plain snapshot. Not too much. The difficult part is to expose a simple API, but maybe Golden has something good to offer.

I will use a test from the GildedRose Refactoring kata as an example. This is the basic API of the class. To exercise this class as subject under tests, we need to pass an array of Item objects in construction, and invoke the updateQuality method. The array of Item object will be updated and that is the outcome we are going to test.

Wrap the subject under test

The first thing we need is a wrapper variadic function that takes any number of parameters of any type and returns something. We are going to pass this function to the master method. We can do this with an anonymous function.

The body of the wrapper must convert the received parameters back to the types required by the SUT. You need to identify the proper data by position. In this particular example we don't need to apply transformations or casting to the $params items.

The wrapper function could return any type with the result. But if you find problems with doing so, try to convert the output to string. It is a safe bet, and it is what we prefer here:

What can I do if the SUT throws an exception? The best thing is to capture the exception with a try/catch and return something that can be informative, like the exception message. It should appear in the snapshot as is, so you will know what input combination generated it.

Let's imagine that the GildedRose class could throw an exception for whatever reason.

We capture a generic exception in the catch block and return the message string. That's all. The message will appear as the output of the input combination. You can catch specific exceptions if you wish, or customize the returned string with more information. It's up to you.

What can I do if the SUT doesn't return an output? A suggested technique for this is to add some kind of logging facility that you can retrieve after exercising the subject under tests and retrieve that logged output. In fact, this is what is happening in this example: the SUT itself doesn't return anything, we capture the outcome in another way.

Prepare lists of values for each parameter

The next thing we need to do is to prepare lists of values for each parameter. You will populate an array with all the values that you want to test. Remember to use valid types for the signature of the SUT.

You will pass the collections of values with the convenience function Combinations::of().

In fact, Combinations::of() if a constructor for an object that will manage and generate all possible values combinations.

How should I choose the values? It is an interesting question. In this example, it doesn't matter the specific values because the code only has one execution flow. In many cases, you can find interesting values in conditionals, that control the execution flow, allowing you to obtain the most code coverage executing all possible branches. The precedent and following values of those are also interesting. If you are unsure, you can even use a batch with several random values. Remember that once you set up the test, adding or removing values is very easy.

Putting it all together

And this is how you run a Golden Master test with Golden:

This example will generate 180 tests: 5 products 6 sellIn 6 qualities.

master method will invoke verify under the hood, using the result of executing all the combinations as the subject to create the snapshot. This is a very special snapshot, by the way. First of all, it is a JSON file containing an array of JSON objects, each of them representing one example. Like this:

I think this will help you to understand the snapshot, identify easily interesting cases, and even post-process the result if you need to.

Customizing the behavior

You can pass any combination of the following options in any of the test modes.

Customize the snapshot name

You can customize the snapshot name, by passing the option snapshot():

This will generate the snapshot in __snapshots/ParrotTest/european_snapshot.snap in the same folder of the test.

This is useful if you need:

Customize the folder to store the snapshot

You can customize the snapshot folder, by passing the option folder():

This will generate the snapshot in __parrots/ParrotTest/test_speed_of_european_parrot.snap in the same folder as the test.

You can use both options at the same time:

This will generate the snapshot in __parrots/ParrotTest/european_snapshot.snap in the same package of the test.

Customize the extension of the snapshot file

You can customize the snapshot name, by passing extension():

This will generate the snapshot in __snapshots/ParrotTest/test_speed_of_european_parrot.data in the same package of the test.

This option is useful if your snapshot can be files of a certain type, like CSV, JSON, HTML, or similar. Most of IDE will automatically apply syntax coloring and other goodies to inspect those files based on extension. Also, opening them in specific applications or passing them around to use as examples will be easier with the right extension.

Set your own defaults

Folder. You can customize a default snapshots folder, by passing the option folder() to defaults():

This will generate all the snapshots of the TestCase into the __parrots/ folder in the same folder as the test.

Extension. You can customize the default snapshot extension, by passing extension() to defaults():

This will generate all the snapshots in the TestCase with the .json extension.

Scoping Defaults. You can scope the defaults for a whole TestCase by adding a call to $this->defaults(extension(".json")); in the setUp.

If you want to set the defaults for several test cases at once (or even to all test cases), the best option seems to create a class that extends from TestCase and include the call in the setUp method. Remember that all of your test cases must extend the newly created TestCase and the setup must call its parent as shown here:

Dealing with Non-Deterministic output

This is not an exclusive problem of snapshot testing. Managing non-deterministic output is always a problem. In assertion testing, you can introduce property-based testing: instead of looking for exact values, you can look for desired properties of the output.

In snapshot testing, things are a bit more complicated. It is difficult to check the properties of a specific part of the output and ignore that specific value. Anyway, one solution is to look for patterns and do something with them: replacing them with a fixed but representative value or replacing them with a placeholder. Maybe it is possible to ignore that part of the output to compare with the snapshot.

In Golden, as it happens in other similar libraries, we can use Scrubbers. A Scrubber encapsulates a regexp match and replace, so you use the regexp to describe the fragment of the subject that should be replaced and provide a sensible substitution.

You can see an example right now. In the following test, the subject has non-deterministic content because it takes the current time when the test is executed, so it will be different on every run. We want to avoid the failure of the test by replacing the time data with something that never changes.

In this example, the scrubber matches any time formatted like "15:04:05.000" and replaces it with "<Current Time>".

You could use any replacement string. In the previous example, we used a placeholder. But you could prefer to use an arbitrary time so when inspecting the snapshot you can see realistic data. This can be useful if you are trying to get approval for the snapshot, as long as it avoids having to explain that the real thing will be showing real times or whatever non-deterministic data that the software generates.

Replacing fields in Json Files with PathScrubbers

If you are testing Json files you probably will want to scrub specific fields in the output. Instead of searching for a pattern in the file, you want to search for a path to a field. We have you covered with PathScrubber.

The PathScrubber allows you to specify a path and replace its contents unconditionally. If the path is not found, no replacement will be performed.

Caveats

Scrubbers are handy, but it is not advisable to use lots of them in the same test. Having to use a lot of scrubbers means that you have a lot of non-deterministic data in the output, so replacing it will make your test pretty useless because the data in the snapshot will be placeholders or replacements for the most part.

Review if you can avoid that situation by checking things like:

Create Custom Scrubbers

If you find that you are using many times the same regexp and replacement, consider creating a custom Scrubber.

For example, in the previous test, we used:

This is totally fine, but if you really need to reuse the logic in several tests, the best way is to create your own specialized scrubber by creating a class that implements Scrubber interface, delegating the behaviour to a RegexpScrubber or a PathScrubber.

When writing Scrubbers, you should support the callable ...$options parameter as in the previous example. This will allow your Scrubber to use scrubber options, so you can modify the replacement or the context.

This will allow you to enforce policies to scrub snapshots, introducing Scrubbers that are useful for your domain needs.

Predefined Scrubbers

CreditCard: obfuscates credit card numbers

ULID: replaces a Universally Unique Lexicographically Sortable Identifier

Options for Scrubbers

Replacement: allows you to customize the replacement of any scrubber supporting options. Some scrubbers will put a placeholder as a replacement, but in some cases, you could prefer a different placeholder.

A fixed example of the value will help to better understand the generated snapshot to non-technical people.

Format: allows you to pass a format string that provides some context for replacements, so they will be only applied in certain parts of the output. In the following example, we only want to apply the obfuscation to the Credit card field, but not to other codes that could be similar.

How snapshots are named

By default, test names are used to auto-generate the snapshot file name. The name of the TestCase will be used to create a folder in which the snapshot for each test will be created. The name of this files will be the _snakecase version of the test name.

This test:

Will generate the snapshot: __snapshots/NonDeterministicTest/should_scrub_non_deterministic_data.snap

You can customize the snapshot file name by passing the option snapshot("new_snapshot_name"). You must do this if you want to have two or more different snapshots in the same test. The first one could use the default name, but the subsequents will reuse it. You can also use this feature for making different tests use the same snapshot.

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