The tests must be isolated and independent :<\/strong> each test must be able to be executed, alone, and after or before any other test. Its proper functioning should not depend on the context provided by another test.\u00a0<\/li>\n<\/ul>\nOther practices, however, should be avoided<\/strong>:\u00a0<\/p>\n\nValidating implementation details in a unit test:<\/strong> make sure that a method called with this parameter does not bring anything if it is not part of the behaviour of the code under test (for example, in the case of an orchestrator). This link with the implementation will make the test useless in case of refactoring the internal structure of the code: we will simply know that the implementation has been modified without knowing if the expected overall behaviour has been kept or not.\u00a0<\/li>\nLoading \u201ctoo much\u201d code for a unit test:<\/strong> this often depends on the design of the code, loading too much code is often a sign of too complex code or too little decoupled structures.\u00a0<\/li>\nDepend on external services: <\/strong>dependence on an external service will lead to a test sometimes in error, sometimes not, depending on the availability of this dependency. The use of\u00a0mocks (stub, spy, fake, mock)\u00a0is preferable to avoid this.\u00a0<\/li>\nUse try\u2026catch:<\/strong> in unit tests, we expect this or that specific error. We prefer (when the framework allows it) the assertion on the good triggering of an error.\u00a0<\/li>\n<\/ul>\nI could list other good and bad practices, but I think it is already very effective to follow all these.\u00a0<\/p>\n
What is the coverage? Where do you stop? <\/h2>\n Depending on the context (legal obligations, company strategies, team guidelines\u2026), you can set objectives for code coverage by unit tests (lines, branches, conditions). But watch out! It is easy to achieve very good coverage without ensuring business rules are really covered by tests. <\/p>\n
I recently came across a file that seemed \u201cvery well tested\u201d: <\/p>\n
\n
This same project, after a simple refactoring (method extraction) and a more extensive use of mocks in the tests fell to this level of coverage: <\/p>\n
\n
The tests in place had not evolved and the functional had not been extended: a simple reorganisation of the code and limitation of the code loaded by the tests showed that the job in this code was not as well tested as we thought! <\/p>\n
So how do we proceed? How far do we go? <\/h3>\n I quite like the point of view of Martin Fowler who advises (in his book \u201cRefactoring\u201d) to stop \u201cas soon as you are confident that, if someone comes to introduce a regression in the code you have just produced, one of the tests in place will fall into error\u201d. <\/p>\n
Beyond this mindset, the extent of the unit tests that we choose to implement may depend, in my opinion, on different parameters:<\/p>\n
\nThe risks incurred in the event of failure of the code concerned:<\/strong> a critical project for our customers recently saw the implementation of more than 500 unit tests for 699 lines of code (with a coverage of \u201conly\u201d 88.9% of the code).\u00a0<\/li>\nLong-term testing comfort management:<\/strong> a project for which we wanted to avoid costly maintenance operations at all costs (for reasons of internal team organisation) saw its branch coverage approach 100%.\u00a0<\/li>\nPersonal appreciation\/team judgement:<\/strong> during code reviews carried out in teams, unit tests are also reviewed and the decision to have enough or not is a decision that is made by the whole team and may depend on the project, the context, the company\u2026\u00a0<\/li>\n<\/ul>\nThe main thing is to understand why the decision is made to stop (or continue to expand) testing. This decision must be conscious and informed to allow for a healthy life of the project over time.\u00a0<\/p>\n
Design\u00a0<\/h2>\n To avoid writing unnecessary tests or falling into an illusion of good coverage, it is important to design your unit tests well. For this I will distinguish two cases: designing tests on future code and designing tests on existing code (called \u201clegacy\u201d).\u00a0<\/p>\n
Unit tests on new code <\/h3>\n On the new code, we are lucky to have access to the desired behaviours: these are the behaviours to be implemented. We can then more easily define the unit test cases to implement, based on the acceptance criteria of the sprint backlog stories. We will proceed in 3 steps: <\/p>\n
1.<\/strong> List the desired behaviours for a given work element.\u00a0<\/p>\n\nBase them on the stories and their acceptance criteria.\u00a0<\/li>\n Define input data sets and expected results.\u00a0<\/li>\n<\/ul>\nThis can be an opportunity to challenge the product owner to define certain behaviours more precisely, if necessary.\u00a0<\/p>\n
2.<\/strong> Define the set of use cases for each behaviour or \u201cbusiness rule\u201d.\u00a0<\/p>\n\nPassing cases\u00a0<\/li>\n Error cases\u00a0<\/li>\n Borderline cases\u00a0<\/li>\n<\/ul>\nTo do this, we can use different techniques for designing use cases such as decision trees (to go through all the paths in test cases) or equivalence classes (to avoid testing the same thing several times).\u00a0<\/p>\n
3.<\/strong> Design the code to be able to test these behaviours (testable code).\u00a0<\/p>\n\nIdeally in a \u201ctest first\u201d approach.\u00a0<\/li>\n<\/ul>\n\n\u201cTDD is not a testing activity, it is a design activity\u201d (M. Fowler).<\/em>\u00a0<\/p>\n<\/blockquote>\nUnit testing on existing code <\/h3>\n On existing code (or \u201clegacy\u201d), it is often more difficult to access the behaviours initially expected. It is then perilous to add unit tests when they are missing because we don\u2019t have access to the original specification. It is then difficult to get out of a vicious circle that nobody wants to enter: <\/p>\n
\n
Reverse engineering <\/h3>\n The most common approach (but also the least comfortable and most time-consuming) is to engage in a brave reverse engineering to try to re-discover the behaviour of the code. This may shed some light on how the code currently works, but it doesn\u2019t give us any information about the behaviour that is actually expected. User documentation can sometimes also help us \u2013 when it exists! <\/p>\n
Taking advantage of developments <\/h3>\n If we are in the presence of business users (product owner, product manager, development team, users\u2026), a request for evolution or the detection of a bug by a user can be an opportunity to review the business in place. <\/p>\n
Use the current characteristics of the system as a basis <\/h3>\n We can request the code with different input data sets and simply record the resulting outputs. These outputs will be used as \u2018expected behaviour\u2019 for further testing to ensure that the current behaviour (whether correct or not) is not altered by the changes we are going to make to the code. <\/p>\n
There is substantial literature on the subject, including Michael Feathers\u2019 \u201cWorking effectively with legacy code\u201d. <\/p>\n
Mutation testing to identify gaps <\/h3>\n When unit tests are already present and you want to extend the coverage of the business by testing, you can use mutation testing. This type of test consists of modifying the behaviour of the code by introducing \u201cmutants\u201d (certain conditions are modified, certain operators replaced by others, the body of certain branches emptied, etc.) and then running the existing unit tests to see if they detect the modification. <\/p>\n
If a unit test fails, the mutant has been \u201ckilled\u201d. Conversely, if no test fails, it means that the behaviour that has been changed is not within the scope of the unit tests in place: the mutant has \u201csurvived\u201d. <\/p>\n
This practice makes it possible, despite sometimes very good code coverage, to have a better view of whether the different behaviours in the code are covered or not. We can then prioritise the implementation of new unit tests on the code where the mutants have survived. Be careful however, the execution of these mutant tests is costly in machine time and can take a very long time: this is why we most often target certain carefully chosen files for these kinds of tests. <\/p>\n
Code Testability <\/h2>\n The question of code testability comes up fairly regularly when we talk about unit tests. The most recurring difficulties are described below. The simplest solution to these various issues is not to let them take hold, through the use of best practices or adapted tools: <\/p>\n
\nMethods that are too complex to test:<\/strong> tools such as SonarQube ensure that \u201cacceptable\u201d complexities are not exceeded. The use of \u201cquality barriers\u201d even allows you to be alerted as soon as the code becomes too complex.\u00a0<\/li>\nMocks that are too numerous and difficult to set up:<\/strong> the need for many mocks can be avoided by a different code structure. Good refactoring can make the code more easily testable and thus lead to tests that are simpler to design and implement.\u00a0<\/li>\nThe need to load too much code to be able to test a behaviour:<\/strong> the TDD approach avoids having the behaviour too widespread in the code, diluted in many methods\/classes. Only what is needed to render a given behaviour is coded, no more and no less. Principles such as KISS (Keep It Stupid Simple) or YAGNI (You\u00a0Ain\u2019t\u00a0Gonna\u00a0Need It) also promote a good match between code structure and product behaviour.\u00a0<\/li>\n<\/ul>\nMany refactoring techniques exist and are often integrated directly into the IDE (Extract method, extract variable, rename function etc\u2026). I recommend Martin Fowler\u2019s book \u201cRefactoring\u201d, which is a superb toolbox to modify\/improve\/upgrade any software code. <\/p>\n
Keep in mind that any code that seems difficult to test will also be difficult to maintain, evolve and be subject to many bugs throughout its life. <\/p>\n
Conclusion <\/h2>\n In conclusion, I would stress that unit tests, if they are well designed and written to validate system behaviour, are an efficient investment, even essential to ensure the comfort and profitability of projects over their entire lifecycle. They form the defence against many potential regressions in production and make maintenance or refactoring actions safe. <\/p>\n
At AT Internet, we have chosen to invest in this level of testing, by providing information, training, and support to the teams on the subject. We also encourage practices such as peer programming and code reviews (including test code) and listen to the teams to evaluate the problems encountered and spend time to find solutions. <\/p>\n
Photo credits: Alex King<\/a> <\/p>\n<\/div>\n\n","protected":false},"excerpt":{"rendered":"What are unit tests? Who is responsible for them? Why write them? Do we have enough of them? How do you design them? And what do you do with this hard-to-test code?\u00a0A wide range of\u00a0questions that I will try to answer below.\u00a0 This article is particularly aimed at developers who have questions or want to […]<\/p>\n","protected":false},"author":1,"featured_media":63373,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[12033],"tags":[11079,12632,4505],"dealstore":[],"offerexpiration":[],"class_list":["post-63372","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-analytics","tag-invest","tag-testing","tag-unit"],"yoast_head":"\n
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<\/figure>\n<\/div>\n![\"Example](\"https:\/\/blog.atinternet.com\/wp-content\/uploads\/2020\/06\/image-12.png\")
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<\/figure>\n<\/div>\n![\"Vicious](\"https:\/\/blog.atinternet.com\/wp-content\/uploads\/2020\/06\/Circle-tests.jpg\")
<\/figure>\n<\/div>\n