One of the most important stuff that should be done before start writing tests is to decide what to test. Most of the enterprise applications for example can contain lots of code. There is no need to test all that code.
When you decide what to test, you need to be focused on what actually matters to your users and your application. External libraries and frameworks 2. Trivial code like getters and setters 3. Code that works only with the UI 4. One of the most important rules when talking about unit testing is to test and cover code which is mostly part of the business logic.
Code used from a lot of modules 2. Repeatedly changed code 3. Code that is expected to generate a lot of bugs. Most of the developers use some tools or IDE plugins to check the code coverage on the components which already are tested. Unfortunately, the current design does not allow us to do that: the TurnOn and TurnOff methods of BackyardLightSwitcher trigger some state changes in the system, or, in other words, produce side effects.
The only way to verify that these methods were called is to check whether their corresponding side effects actually happened or not, which could be painful. In this case, a unit test can make an attempt to receive and analyze that network traffic.
Or, if the hardware components are connected with a wire, the unit test can check whether the voltage was applied to the appropriate electrical circuit. Or, after all, it can check that the light actually turned on or off using an additional light sensor. As we can see, unit testing side-effecting methods could be as hard as unit testing non-deterministic ones, and may even be impossible.
The resulting test will be hard to implement, unreliable, potentially slow, and not-really-unit. And, after all that, the flashing of the light every time we run the test suite will eventually drive us crazy! No matter how exactly light control is implemented, the SmartHomeController API suffers from these already-familiar issues:.
It is tightly coupled to the concrete implementation. It is not possible to reuse the ActuateLights bool motionDetected method to switch any light other than the one in the backyard. It violates the Single Responsibility Principle. The API has two reasons to change: First, changes to the internal logic such as choosing to make the light turn on only at night, but not in the evening and second, if the light-switching mechanism is replaced with another one.
It lies about its dependencies. There is no way for developers to know that SmartHomeController depends on the hard-coded BackyardLightSwitcher component, other than digging into the source code. It is hard to understand and maintain. What if the light refuses to turn on when the conditions are right? We could spend a lot of time trying to fix the SmartHomeController to no avail, only to realize that the problem was caused by a bug in the BackyardLightSwitcher or, even funnier, a burned out lightbulb!
The solution of both testability and low-quality API issues is, not surprisingly, to break tightly coupled components from each other. As with the previous example, employing Dependency Injection would solve these issues; just add an ILightSwitcher dependency to the SmartHomeController , delegate it the responsibility of flipping the light switch, and pass a fake, test-only ILightSwitcher implementation that will record whether the appropriate methods were called under the right conditions.
This approach is an option in any object-oriented language that supports first-class functions. This solution will convert the method into a higher-order function :.
It is no longer necessary to implement a class that conforms to an interface in order to supply SmartHomeController with the required functionality; instead, we can just pass a function definition. Higher-order functions can be thought of as another way of implementing Inversion of Control. Now, to perform an interaction-based unit test of the resulting method, we can pass easily verifiable fake actions into it:.
Finally, we have made the SmartHomeController API fully testable, and we are able to perform both state-based and interaction-based unit tests for it. Again, notice that in addition to improved testability, introducing a seam between the decision-making and action code helped to solve the tight coupling problem, and led to a cleaner, reusable API. Now, in order to achieve full unit test coverage, we can simply implement a bunch of similar-looking tests to validate all possible cases — not a big deal since unit tests are now quite easy to implement.
Uncontrolled non-determinism and side effects are similar in their destructive effects on the codebase. When used carelessly, they lead to deceptive, hard to understand and maintain, tightly coupled, non-reusable, and untestable code. On the other hand, methods that are both deterministic and side-effect-free are much easier to test, reason about, and reuse to build larger programs. In terms of functional programming, such methods are called pure functions. What really makes code untestable is hard-coded, impure factors that cannot be replaced, overridden, or abstracted away in some other way.
Impurity is toxic: if method Foo depends on non-deterministic or side-effecting method Bar , then Foo becomes non-deterministic or side-effecting as well.
Eventually, we may end up poisoning the entire codebase. However, impurity is inevitable; any real-life application must, at some point, read and manipulate state by interacting with the environment, databases, configuration files, web services, or other external systems. Static properties and fields or, simply put, global state, can complicate code comprehension and testability, by hiding the information required for a method to get its job done, by introducing non-determinism, or by promoting extensive usage of side effects.
Functions that read or modify mutable global state are inherently impure. For example, it is hard to reason about the following code, which depends on a globally accessible property:. Now , or Environment. MachineName ; they are read-only, but still non-deterministic. On the other hand, immutable and deterministic global state is totally OK. Constant values like Math. PI do not introduce any non-determinism, and, since their values cannot be changed, do not allow any side effects:.
Essentially, the Singleton pattern is just another form of the global state. Singletons promote obscure APIs that lie about real dependencies and introduce unnecessarily tight coupling between components. They also violate the Single Responsibility Principle because, in addition to their primary duties, they control their own initialization and lifecycle.
Automation is key to making unit testing workable and scalable. In addition, software teams need to practice good testing techniques, such as writing and reviewing tests alongside application code, maintaining tests, and ensuring that failed tests are tracked and remediated immediately. Adopting these unit testing best practices can quickly improve your unit testing outcomes. Brian McGlauflin is a software engineer at Parasoft with experience in full stack development using Spring and Android, API testing, and service virtualization.
He is currently focused on automated software testing for Java applications with Parasoft Jtest. What is Unit Testing? Why Unit Test? Here are more than a few great reasons to unit test: Unit testing validates that each piece of your software not only works properly today, but continues to work in the future, providing a solid foundation for future development. Unit testing identifies defects at early stages of the production process, which reduces the costs of fixing them in later stages of the development cycle.
Unit-tested code is generally safer to refactor , since tests can be re-run quickly to validate that behavior has not changed. Writing unit tests forces developers to consider how well the production code is designed in order to make it suitable for unit testing , and makes developers look at their code from a different perspective , encouraging them to consider corner cases and error conditions in their implementation.
Including unit tests in the code review process can reveal how the modified or new code is supposed to work. Plus, reviewers can confirm whether the tests are good ones or not. Unit Tests Should be Trustworthy The test must fail if the code is broken and only if the code is broken. Unit Tests Should be Maintainable and Readable When production code changes, tests often need to be updated, and possibly debugged as well.
Unit Tests Should Verify a Single Use Case Good tests validate one thing and one thing only, which means that typically, they validate a single use-case.
Unit Tests Should be Isolated Tests should be runnable on any machine, in any order, without affecting each other. Unit Tests Should be Automated Make sure tests are being run in an automated process. Download the eBook. Learn how to increase your return from unit testing with Parasoft Jtest. Request a Demo Now. View All Resources. The edge case? How do I know that a function is adequately covered? I always have the terrible feeling that while a test will prove that a function works for a certain case, it's utterly useless to prove that the function works, period.
Among the plethora of answers thusfar no one has touched upon equivalence partitioning and boundary value analysis , vital considerations in the answer to the question at hand. All of the other answers, while useful, are qualitative but it is possible--and preferable--to be quantitative here. In equivalence partitioning , you divide the set of all possible inputs into groups based on expected outcomes. Any input from one group will yield equivalent results, thus such groups are called equivalence classes.
Note that equivalent results does not mean identical results. As a simple example consider a program that should transform lowercase ASCII characters to uppercase characters. Other characters should undergo an identity transformation, i. Here is one possible breakdown into equivalence classes:. The last column reports the number of test cases if you enumerate all of them.
Technically, by fishtoaster's rule 1 you would include 52 test cases--all of those for the first two rows given above fall under the "common case". But with equivalence partitioning testing any one test case in each equivalence class is sufficient.
If you pick "a" or "g" or "w" you are testing the same code path. Boundary value analysis recommends a slight refinement: essentially it suggests that not every member of an equivalence class is, well, equivalent. That is, values at boundaries should also be considered worthy of a test case in their own right.
One easy justification for this is the infamous off-by-one error! Thus, for each equivalence class you could have 3 test inputs.
Looking at the input domain above--and with some knowledge of ASCII values--I might come up with these test case inputs:. As soon as you get more than 3 boundary values that suggests you might want to rethink your original equivalence class delineations, but this was simple enough that I did not go back to revise them. Thus, boundary value analysis brings us up to just 17 test cases -- with a high confidence of complete coverage -- compared to test cases to do exhaustive testing.
Not to mention that combinatorics dictate that exhaustive testing is simply infeasible for any real-world application! Probably my opinion is not too popular. But I suggest that you to be economical with unit tests.
If you have too many unit tests you can easily end up spending half of your time or more with maintaining tests rather than actual coding. IMHO unit tests are not a replacement for good engineering and defensive coding. Currently I work on a project that is more or less unusuable. It is really stable but a pain to refactor. In fact nobody has touched this code in one year and the software stack it's based on is 4 years old.
Because it's cluttered with unit tests, to be precise: Unit tests and automatized integration tests. Ever heard of cucumber and the like? And here is the best part: This yet unusuable piece of software has been developed by a company whose employees are pioneers in the test-driven development scene. Start writing tests after you developed the basic skeleton, otherwise refactoring can be painful.
As a developer who develops for others you never get the requirements right at the start. Make sure your unit tests can be performed quickly. If you have integration tests like cucumber it's ok if they take a bit longer. But long running tests are no fun, believe me. And yes, sometimes you concentrate on the edge cases, sometimes on the common cases, depending where you expect the unexpected.
Though if you always expect the unexpected, you should really rethink you workflow and discipline. If you are adding tests after the fact, then I cannot recommend enough that you get a copy of Working Effectively With Legacy Code by Michael Feathers and take a look at some of the techniques for both adding tests to your code and ways of refactoring your code to make it more testable.
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