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Fundamental automated testing and design concepts that inform the craft of writing automated tests and testable code.
WORK IN PROGRESS: This material is currently in draft form and under active development. See the GitHub issues page to examine progress.
Illustration by Catherine Laplace, based on my hand-drawn sketch of a slide
from the Google Unit Testing Lecture slides, originally by Nick
Lesiecki.
An automated test can fall into one of three broad categories:
- Small (unit): Very fine-grained, exercises low-level logic at the scope of a function or a class, and uses no external resources (except possibly a small data file or two, but preferably no file system dependencies whatsoever). Very fast execution on the order of seconds.
- Medium (integration): Exercises interaction between discrete components. May have file system dependencies or run multiple processes, including test databases. Runs on the order of minutes.
- Large (system): Exercises the entire system, end-to-end. Used to identify catastrophic errors and performance bottlenecks at scale. May launch or interact with live databases or services in a datacenter, preferably within a staging environment to avoid affecting production (especially user traffic, and most especially advertising traffic!). Can run on the order of minutes or hours.
The idea here is that you don’t want tests of only one size; different sizes of tests serve very different purposes, but all sizes are vital to the health of a project or system. More specifically:
- You want a lot of fast, focused small tests that you can run frequently to ensure that a low-level change is free of negative side-effects, without breaking the “flow” state while developing.
- You want a decent-sized layer of medium tests to ensure that contracts are honored at interface and component or subsystem boundaries.
- You want a few large tests to provide confidence that the end-to-end system is hanging together without any pieces falling off.
- For a production system or framework, you want a balance of all three, not relying on one size of tests alone. A team developing only pure, in-process, library code may get away without large tests, but most other projects should have them. Larger systems might even benefit from some “enormous” tests and/or a staging system mirroring production, possibly requiring manual intervention and monitoring.
The goal is to automate at the lowest level possible, but no lower. Shoot for an appropriate balance, not an ideal one. There will always be a need for manual testing; the point is to get the most out of it, not to eliminate it.
References:
Testability begins with design. Reuse classes via composition rather than inheritance whenever possible.
Composition-based design produces classes that can be tested more efficiently and vigorously, and are easier to reuse across otherwise unrelated classes. Classes composed of smaller objects are also easier to isolate and test (using fakes, mocks, and stubs, if necessary), and can more easily grow and change by adding, removing, or replacing internal objects. The increased modularity and testability of composed classes is often worth the cost of the extra code required to delegate method calls to internal objects.
Inheriting from a class to reuse its implementation often introduces dependencies that are hard to replace, and can hide behavior. Tests for derived classes with non-obvious behaviors and heavyweight dependencies are often complicated, brittle, slow, and/or nondeterministic (i.e. “flaky”).
On the other hand, in statically-typed languages (for example C++ or Java), interface-only inheritance enables an object to be replaced with any other object of the same interface. Classes can then be composed of references to interface classes, rather than instances of concrete classes (i.e. dependency injection). Tests can use these interfaces to define fakes, mocks, or stubs that replace heavyweight or complicated dependencies and provide better control of the code under test. In dynamically-typed languages (for example Python, Ruby, or Javascript), interface inheritance is unnecessary, since the language runtime will raise an exception when the interface contracted is violated.
If you are using a framework that requires you to inherit implementation from classes in order to hook into it (e.g. Jekyll), you can still implement your derived classes in a compositional style. In this way, you can isolate and test most of the behavior specific to your application without having to set up and manage the framework's dependencies and configuration. For an example, see the _plugins and _test directories of the 18F Hub. Though Hub::Generator derives from Jekyll::Generator, all of its behaviors are encapsulated in other classes, which are tested in isolation.
References:
- Max Three Layers Of Inheritance from the c2 wiki
- "When to Use Inheritance or Composition" from Learn Python the Hard Way
Not all objects that stand in for “real” classes are mock objects. Generally known as “test doubles”, stubs, mocks, and fakes all serve to isolate the code under test from a more complex and/or heavyweight collaborator.
Stubs contain no logic, are manually written without the aid of frameworks or other tools, and only return data assigned by the test code. These are useful when interaction with a collaborator is relatively straightforward.
Mock objects are written using mock object frameworks, and are programmed to expect certain method calls by the code under test, and to return specific values as a result of such calls. They're more versatile than stubs, and can verify more complex interactions, but can grow needlessly complex without careful tending.
Fakes are fully-functional, yet smaller-scale implementations of real collaborators. They are manually maintained rather than framework-generated. They may be initialized with test data and/or examined for state changes after the code under test has completed execution. Otherwise, specific interactions are not validated, as they often are in the case of mocks.
References:
Mocks should stand in for expensive and/or complex immediate collaborators. Use mock object frameworks when you have to; overuse of mocks fosters brittleness.
When mock objects are programmed to return other mock objects, that is often a design smell, i.e. indicative that there is something amiss with the design.
Prefer using real, stub, or fake objects instead, using data packaged as a hermetic (i.e. version-controlled) dependency of the test program.
For sufficiently complex and/or heavyweight collaborators, fakes can be far preferable to mocks. They can be far easier to setup and maintain, despite the fact that they may need their own automated tests. Despite the extra work involved, a well-tested fake implementation may prove to be an investment that pays off very well over time.
Ideally, the maintainer of the original code or service will provide a fake implementation clients can use to test against, as the maintainer will be in the best position to ensure the relevant feature parity between implementations. When that isn’t the case, as a client of a complex service, you may begin writing your own fake implementation, starting out small and building out the implementation as needed.
Case in point: Pyfakefs, which is now available in the Python Package Index, started as a Google-internal, personal tool to fake out Python’s builtin file system interface for a single set of automated tests. Since its announcement in Testing on the Toilet in 2006, it’s now used in over 2,000 automated tests at Google as of July 2014.
A “data-driven” test is characterized by a single test function that is used to iterate through an array of data structures. Each element of the array contains both the input data and the expected output values.
Sometimes the interface to a method is so stable and uncomplicated that a
data-driven structure is the easiest and most straightforward to understand.
More often than that, however, data-driven tests require the programmer to
remember the structure of the test data and the semantics of individual values
within the structure. For example, how would one interpret a structure such as
{ true, 1, 0, false, “foo” }? What do true and false, 0 and 1 mean
in this context?
Also, when a new test case requires a new piece of data, then all of the existing structures must be updated, increasing the maintenance burden and the mental burden of understanding each value in the structure.
By contrast, a well-defined test fixture can facilitate easy to read, easy to
maintain, easy to extend, self-contained test cases. A common init()
function (or an Xunit-style setUp(), or what-have-you) can encapsulate the
addition of a new test input by setting a default value. Only the tests making
use of the new input will need to be modified, rather than having to add
otherwise meaningless data to many data-driven test fixtures.
A “golden file” is a log of output from a previous run of a test program. It is expected that subsequent runs should produce no differences in output. If there are differences, the programmer must decide if a defect has occurred or if the golden file must be updated. As such, they can be very brittle when exercising broad swaths of program behavior.
That said, there are a few situations in which golden files make sense:
- If there are no other tests for much of a program’s code, a golden file is a way to pin down behavior while refactoring to add new automated tests.
- If a program has good automated test coverage by other means, a golden file can act as a “large” test and/or a smoke test.
- If a program integrates a lot of frequently-updated components not produced by the team itself, a golden file test can serve to notify the team that integrated features have been significantly updated. The team can then notify the component team to determine whether or not the delta is acceptable. If it is, the golden file can be updated. If it’s not, the component team can fix or roll back the change.
A “self-contained” test means that the test case includes (almost) everything necessary to understand the test without having to read any surrounding code.
The first step is picking a good name for the test that clearly signifies its intent. The second step is to use test fixtures and/or helper classes to provide common data and helper functions, greatly simplifying and clarifying the steps taken by each test case.
References:
- Testing on the Toilet: Writing Descriptive Test Names
- Testing on the Toilet: What Makes a Good Test?
While many languages now include testing libraries that follow the xUnit style, it’s possible to approximate the xUnit style very effectively without importing a special framework by following the pseudo xUnit pattern.
When a test fails, you want to know what’s different about it. Repetition in test case structure makes the significant differences between test cases stand out.
By contrast, implementation code that’s been copied-and-pasted is prime breeding ground for catastrophic bugs. Automated testing exerts design pressure that favors common functions thoroughly tested in isolation over copied-and-modified code that may not be adequately covered by tests.