From e77676673a35294d392bee1593d442418c644005 Mon Sep 17 00:00:00 2001 From: jgaffiot Date: Fri, 17 Jul 2026 11:25:52 +0200 Subject: [PATCH] feat: restore the French article, add an English version --- doc/en/article.md | 355 +++++++++++++++++++ doc/fr/article.md | 373 ++++++++++++++++++++ uv.lock | 879 +++++++++++++++++++++++----------------------- 3 files changed, 1172 insertions(+), 435 deletions(-) create mode 100644 doc/en/article.md create mode 100644 doc/fr/article.md diff --git a/doc/en/article.md b/doc/en/article.md new file mode 100644 index 0000000..cbe5d00 --- /dev/null +++ b/doc/en/article.md @@ -0,0 +1,355 @@ +# Getting out of the testing hell + +## Intro + +The need to write automated tests is now well established among developers. It is the +only way to test systematically and completely, to avoid regressions or simply to avoid +moving forward blindly. + +But once that consensus is reached, the trouble starts. Writing tests is seen as a +chore, often pushed back to the end of the project, and one that doesn't attract +volunteers. +Then the tests turn out to be too often slow, brittle, costly to maintain, not obvious +to run, incomplete... and we end up getting used to negative test reports ("the CI is +red all the time, but that's normal"). +This is called normalization of deviance, and it can lead to disaster, such as crashing +[Challenger](https://en.wikipedia.org/wiki/Space_Shuttle_Challenger_disaster) +*and* [Columbia](https://en.wikipedia.org/wiki/Space_Shuttle_Columbia_disaster). + +Granted, when automated tests are useless or missing, it's an app that crashes rather +than a shuttle, but the process is the same. + +So, how do we regain control of our automated tests? + +## Asking the right questions + +More than the tests themselves, the goal is to ensure the quality and mastery of the +code, code that we can therefore ship and evolve with confidence. +Tests form a software system, parallel to the main project, whose goal is to ensure its +quality: reliability, repeatability, maintainability, evolvability. + +Like any software system, it requires development time and expensive skills to acquire. +Thus, the test suite is an investment for the main project, which must therefore reap a +benefit in quality. It is therefore crucial to define what is expected from the tests. +Since it's always possible to add more tests, we also need to define the resources +allocated to testing, which will later allow us to arbitrate between adding tests or +adding features. + +Automated tests, as opposed to manual tests, form all or part of the test suite, and are +run automatically and systematically, typically on every commit. +This protects against most regressions, and this information reaches the developer +quickly. +It is therefore a central part of the continuous integration (CI) process, to the point +that "CI" now refers to automated test pipelines! + +Some tests remain mostly manual, for example user tests (including usability tests, A/B +tests...), load tests, performance tests, penetration tests... + +Automated tests are there to bring a certain level of confidence in code quality, in +order to reach the project's quality goals within the allocated resources. Never +forgotten, they prevent bugs and stop quality from drifting, but are often limited by +the difficulty of automating things. + +## Starting back from the basics + +Tests are part of the toolbox for reaching the quality goal, but they are not alone. +Several successive layers of tools are needed to catch the different quality issues. + +The first layer is a code formatter, which standardizes the code. The benefits are +making the code easier to read (for those who still read code), reducing the developer's +workload (for those who still write it), and cleaning up code comparisons from all +whitespace differences, which reduces the size of Pull Requests / Merge Requests. + +The second layer consists of analysis software, starting with static analysis tools. +These do not execute the code (hence the name "static") but look for bad practices by +following simple text-recognition rules, such as regular expressions. Their big +advantage is detecting bugs very quickly and without risk (the code doesn't run) and +without conditions (no need for a test environment or anything else). + +This category also includes compilers, especially with their warnings enabled, and type +checkers (for interpreted languages), which make sure types are consistent, again +without having executed a single line of code. + +Finally, dynamic analyzers study the code while it's running, often at the cost of a +performance hit (memory and CPU), to look for logic errors that are harder to detect +(typically memory errors). + +The last layer is of course to automate all the chosen tools, as close to the developer +as possible. First, a runner like [`doit`](https://pydoit.org/) or +[`just`](https://just.systems/man/en/introduction.html) lets you run all the chosen +quality tools with a single simple command. Then, we can take advantage of Git hooks to +automatically run these tools on certain commands, and typically on commit. Utilities +like the well-known [`pre-commit`](https://pre-commit.com/) or the more recent +[`prek`](https://prek.j178.dev/) make it easy to configure Git hooks. To avoid losing +your train of thought waiting for long tests to finish at commit time, only use tools +with a short execution time (up to a few seconds) in the pre-commit hook. + +## Cleaning up the tests + +Let's continue by cleaning up the existing tests, since of course some tests can be +deleted. First, unreliable tests must be disabled or removed, because they don't bring +the information the team needs: whether the test passes or not, we don't learn anything +more about the quality of the tested code. These tests can be re-enabled once +stabilized, which requires identifying the cause of the instability. Random number +generation, triggering based on the real clock, the (lack of) synchronization of code +running in parallel (multithreading, async...), and dependencies on external processes +or APIs are the most likely causes. + +For example, this test fails unpredictably, because it depends on the real clock: + +```python +# Fragile: depends on the system clock... and gets stuck for an hour! +def test_reporting_each_hour(): + manager = Manager() # creates a report exactly on the hour + assert manager.report is None # false if the test is run exactly on the hour + time.sleep(3600) # that's long... + assert manager.report is not None +``` + +The solution is to inject the clock so it can be controlled from the test, which makes +it both reliable and instantaneous: + +```python +# Reliable: time is injected, and therefore controlled +def test_reporting_each_hour(): + clock = FakeClock(now=datetime(2025, 1, 1, 12, 1)) + manager = Manager(clock) # refactoring of Manager + assert manager.report is None # starting 1 minute after noon: no report + clock.advance(timedelta(hours=1)) # advance time without waiting + assert manager.report is not None # new report +``` + +Next, tests that take too long don't get the information to developers in time. They +can be deleted, sped up, or split off into a second test suite if the test is really +necessary and the execution time truly can't be reduced. This second test suite will +always be automated, but not run on every commit — at a lower frequency, such as once a +day (often overnight), or only just before merging to the main branch. What matters is +that the developer doesn't need the immediate result of the test to keep working, but +that the information brought by this suite of long tests is only needed, for example, +for a new release. + +Finally, don't hesitate to prune the test suite: some tests aren't relevant anymore, +because they're too old, too specific, too coupled, or they test too little code. + +## Making the code testable + +The most serious testing problems are most often caused by the code under test: too +coupled and not observable enough, it forces the test to set up a whole environment, to +run the entire code at once, to play out a whole complex scenario to reach the initial +state in which the test can finally be run, and to rely on roundabout or fragile means +(log parsing...) to know whether the test succeeded. + +The first thing to do is to isolate side effects as much as possible, especially +input/output, so that the project-specific code and business logic can be tested +independently. For any project that isn't a simple "pass-through", as much code as +possible should be testable without any special setup, just by running it. + +Let's then sort side effects into 3 categories: black holes, white fountains, and the +rest. A black hole is write-only: you can only throw data into it, which is immediately +lost. This category includes logging and telemetry systems. A white fountain, the +theoretical opposite of a black hole, is read-only and can only be initialized, once. +This category includes the configuration system, which reconciles environment +variables, files, command line... and makes the result available to the rest of the +code. + +Black holes and white fountains can have a lifetime equivalent to that of the entire +program and a global scope. They are the only side effects that can be tolerated across +the whole codebase. Once well identified, a setup shared by all tests will allow them to +be handled once and for all. + +All other side effects must be isolated, and if possible injected into the business +code that uses them, rather than owned by the business code. Indeed, an injected +dependency gives the test complete freedom to disconnect the side effect and isolate the +code, whereas tight coupling makes testing difficult, especially with static languages. +With these languages, however, an appropriate type must be planned for class members +that will be replaced by a substitute. Testability must therefore be planned for at +design time. + +```c++ +// Hard to test, always instantiates a real, non-substitutable connection +class MyClassWithInternalConnection { +private: + Client client; +public: + MyClassWithInternalConnection(const char* host, int port) { client = Client(host, port); } +}; +// Easy to test, can instantiate a substitute as well as a real connection. But more +// complex with a static language, ownership and lifetime of the client need to be defined. +class MyClassWithInvertedDependency { +private: + // New type to hold the real connection or a substitute + AbstractClient& client; // can hold the real connection or the substitute +public: + // In this example, the class instance takes ownership of the client + MyClassWithInvertedDependency(AbstractClient&& client_): client(client_) {} +}; +``` + +## Getting a grip on your test framework + +All major languages have one or more test frameworks, which bring their share of +features on top of making tests easier to write, such as: + +- launching from a single entry point +- organization into test suites, suites of suites... +- managing the environment, the OS, signals +- temporary files and directories +- capturing standard input and standard outputs +- collecting and structuring test results +- setup/teardown: a pair of special functions to respectively create and destroy a + test's initial state +- fixture: a function that "fixes" the initial state for a test (a generalization of + setup) +- assertions to compare results against expected values, or check an expected behavior + (exception, function call...) +- injecting substitutes or mocks +- parallelization, plugins... + +Using a test framework therefore brings a lot, at the cost of an onboarding time that +quickly pays for itself. + +Substitute injection deserves special attention: it consists of selectively replacing +code that is hard to test (often because it has a side effect) with test-specific code, +by reimplementing the interface of the original code. +For example, a database connection object can be replaced with an object that does +nothing, immediately returning a fixed value. + +The substitute can be built specifically to emulate complex or random behavior, but +with an instant and repeatable result. +It can also be completely empty, but let you check that the interface was called, with +which arguments... +Some dynamic-language frameworks even let you create substitutes without declaring the +interface at all, the interface being created on the fly, just to disconnect a side +effect. +Many plugins or libraries offer ready-to-use substitutes for the most common cases: +connections, time, randomness, databases, logs... + +By combining these features, a test stays short and readable. The given/when/then +structure (also known as arrange/act/assert) clearly separates setup, the tested +action, and verification, turning it into executable documentation: + +```python +def test_alert_sent_if_balance_negative(): + # given: an overdrawn account and a substituted mail-sending service + mailer = FakeMailer() + account = Account(balance=-50, mailer=mailer) + + # when: we trigger the overdraft check + account.check_overdraft() + + # then: one, and only one, alert was sent to the account holder + assert mailer.sent == [("overdraft_alert", account.holder)] +``` + +Here the `FakeMailer` substitute doesn't send any real mail: it just records the calls, +which lets us assert that the alert was indeed triggered, and only once, without +depending on a mail server. + +## Speeding up the tests + +Many approaches can help speed up tests: + +- modularize the code and possibly split it into several libraries. That way, there's + less code, fewer inputs, fewer edge cases to test, and each library is validated + independently. +- prepare artifacts (libs, executables, images...) for testing (and for compilation if + needed) so they're ready to use and cached. Using a precompiled lib is faster than + recompiling it along with the whole project. If a test needs a container, the image + must be available in cache and ready to use as soon as the container starts (no + entrypoint that finishes the setup, for example). +- profile your tests, first to get the time per test, and then to know where the long + tests spend their time. + This step is essential to avoid moving forward blindly. +- parallelize tests (but CI workers may only have a single core) +- factor out setup and teardown shared across several tests +- avoid waiting during a test (`sleep(...)`), and instead react as soon as the action is + done. Sometimes the code needs to be changed so it reports its state (return code, + log, internal variable...) or lets the test retrieve it through a new API. +- reduce the number of different values tested, to focus on expected values and edge + cases +- focus tests on a clear given/when/then sequence, avoiding chaining too many actions +- optimize the code whose test is irreducibly slow. Sometimes the problem is in the + code. +- selectively enable certain options that slow down tests, such as coverage measurement + or memory integrity checking +- selectively run tests based on the code that changed, easy when the code is + well-structured and modular + +Replacing a fixed wait with a conditional wait is a good illustration of this gain: +instead of sleeping "long enough" hoping the action is done (slow, and fragile if the +machine is under load), we return control as soon as the condition is met. + +```python +# Slow and fragile: we wait an arbitrary duration +start_job() +time.sleep(5) +assert result_file.exists() + +# Fast and reliable: we react as soon as the condition is true +job = start_job() +wait_until(lambda: job.is_done(), timeout=5) # returns as soon as it's ready +assert result_file.exists() +``` + +## Filling the gaps in the tests + +Once the existing tests are back in working order, we need to check that the test suite +achieves its goal, and in particular that there's no big hole left in test coverage. If +it hasn't been done already, coverage measurement needs to be added to the framework. +Beware, measuring coverage tends to slow down tests, sometimes a lot, so an option to +enable it selectively should be planned. + +Test coverage should already be roughly uniform across the codebase, so the first thing +to look for is an untested part of the code (a whole file, class, function...). Then, +coverage should be used to check that the business logic (without side effects) is very +well tested, with coverage approaching 100%. Finally, coverage must be analyzed against +goals and resources: can the untested code easily be tested? Is it reused heavily +throughout the codebase? Is it critical? Is it business logic? + +Once coverage has been analyzed, it's time to come back to input/output tests, for +which coverage is not the relevant metric. Indeed, it's easy to cover them with tests +that disconnect so many side effects that the test no longer provides much of a +guarantee. Instead, we should check that normal cases, edge cases, and error cases are +tested, check that the link with the rest of the system is genuinely tested at some +point (API connection, database...), or consider a communication contract to validate +(OpenAPI, for example). + +There's no precise coverage percentage to reach, especially since it's relatively easy +to game that number by marking lines as excluded from coverage or by overusing +substitutes. Moreover, the last few percent are far harder to reach than the first ones. +A figure of 80% is a good starting point for discussion, with quality projects sitting +above that. It's more important to follow the trend: as the project evolves, coverage +should not go down, but go up (even slowly) as bugs get fixed and therefore tests get +added. Adding a check to your test pipelines that coverage doesn't regress notably +prevents adding features without their tests. + +## Using AI wisely + +For all these tasks, which are rather tedious and seen as a waste of time compared to +making progress on the project, AI is a valuable helper. It can be very tempting to +delegate the whole problem to AI (and the latest models will do a good job). But tests +are also the system that guarantees the code works, and that let the developer take +responsibility for the delivered code. +Does the generated test really check the important points? Or is it just a spaghetti of +substitutes that in fact tests nothing at all? Either way, the responsibility rests on +the developer's shoulders. + +Depending on the developer and the project, several strategies are possible: generate +but review carefully, work step by step from the specifications, write the tests +manually or at least the main ones, use a verifying AI after generation... + +Beyond the developer's ownership of generated tests, AI is above all a golden +opportunity to go further with testing: performance testing and optimization, +interface testing, fuzz testing, mutation testing... So many advanced testing +strategies that become accessible to every project. + +## Conclusion + +Once brought back under control, tests provide the confidence needed to move forward, +and become a development tool that makes it possible to ship fast and well. +At that point, the team has acquired a whole range of new skills, from getting a grip on +the framework to optimization, from refactoring the code to adding missing tests, which +make writing new tests routine. +Having become an everyday tool, tests can be written first, in some cases starting from +the specifications, turning what was a chore pushed to the end of the project into a +preparatory step that signals the end of development and guarantees quality. diff --git a/doc/fr/article.md b/doc/fr/article.md new file mode 100644 index 0000000..c46b6ab --- /dev/null +++ b/doc/fr/article.md @@ -0,0 +1,373 @@ +# Sortir de l'enfer des tests + +## Intro + +La nécessité d'écrire des tests automatisés est maintenant bien établie chez les +développeurs. Il s'agit du seul moyen de tester systématiquement et complètement, pour +éviter les régressions ou tout simplement ne pas avancer à l'aveugle. + +Mais une fois posé ce consensus, les ennuis commencent. Écrire les tests est considéré +comme une corvée, souvent repoussée à la fin du projet, et qui n'attire pas les +volontaires. +Ensuite les tests sont trop souvent lents, fragiles, longs à maintenir, pas évident à +lancer, incomplets... et on finit par s'habituer à des rapports de test +négatifs ("la CI est tout le temps rouge, mais c'est normal"). +On appelle ça la normalisation de la déviance, et ça peut conduire à la catastrophe, +comme faire se crasher +[Challenger](https://fr.wikipedia.org/wiki/Accident_de_la_navette_spatiale_Challenger) +*et* [Columbia](https://fr.wikipedia.org/wiki/Accident_de_la_navette_spatiale_Columbia). + +Bon d'accord quand les tests auto sont inutiles ou absents, plutôt qu'une navette c'est +une app qui se crashe, mais le processus est le même. + +Alors, comment reprendre le contrôle de ses tests auto ? + +## Se poser les bonnes questions + +Plus que les tests eux-mêmes, l'objectif est d'assurer la qualité et la maitrise +du code, un code qu'on peut donc livrer et faire évoluer en confiance. +Les tests constituent un système logiciel, parallèle au projet principal, +dont l'objectif est d'en assurer la qualité : +fiabilité, répétabilité, maintenabilité, évolutivité. + +Comme tout système logiciel, il demande du temps de développement et des compétences +chères à acquérir. Ainsi, la suite de test constitue un investissement pour le projet +principal, qui doit donc en retirer un bénéfice sur la qualité. Il est donc crucial +de définir ce qui est attendu des tests. Comme il est toujours possible d'ajouter des +tests, il faut aussi définir les moyens alloués aux tests, qui permettront d'arbitrer +plus tard entre ajouter des tests, ou ajouter des fonctionnalités. + +Les tests automatisés, par opposition aux tests manuels, forment tout ou partie de +la suite de tests, et sont lancés automatiquement et systématiquement, +typiquement à chaque commit. +Ainsi, on s'assure contre la plupart des régressions, et cette information est apportée +rapidement au développeur. +Il s'agit donc d'une partie centrale du processus d'intégration continue (CI), au point +que "CI" désigne maintenant les pipelines de tests automatisés ! + +Certains tests restent le plus souvent manuels, par exemple +les tests utilisateurs (dont tests d'ergonomie, tests A/B...), les tests de charges, +les tests de performances, les tests de pénétration... + +Les tests automatisés sont là pour apporter un certain niveau de confiance sur la +qualité du code, afin d'atteindre les objectifs de qualité du projet, dans les moyens +alloués. Jamais oubliés, ils préviennent les bugs et empêchent la qualité de dériver, +mais sont souvent limités par la difficulté à automatiser. + +## Repartir de la base + +Les tests font partie de la boite à outil pour atteindre l'objectif de qualité, mais ils +ne sont pas seuls. Plusieurs couches successives d'outils sont nécessaires +pour intercepter les différents problèmes de qualité. + +La première couche est un formatteur de code, qui permet d'uniformiser le code. Les +avantages sont de faciliter la lecture du code (pour ceux qui lisent encore le code), +de diminuer la charge du développeur (pour ceux qui l'écrivent encore), et de nettoyer +les comparaisons de code de toutes les différences de caractères d'espacement, +ce qui diminue la taile des Pull Request / Merge Request. + +La seconde couche est constituée de logiciels d'analyse, et en premier lieu des outils +d'analyse statique. Ces derniers n'exécutent pas le code (d'où le nom de "statique") +mais cherchent des mauvaises pratiques en suivant des règles simples de reconnaissance +de texte, comme des expressions régulières. Leur gros avantage est de détecter des bugs +très rapidement et sans risque (le code ne tourne pas) et sans condition (pas besoin +d'environnement de test ou autre). + +On trouve aussi dans cette catégorie les compilateurs, surtout avec leurs warnings +activés, et les analyseurs de types (pour les codes interprétés), qui permettent +de s'assurer de la cohérence des types, toujours sans avoir exécuté une ligne du code. + +Enfin, les analyseurs dynamiques vont étudier le code pendant son fonctionnement, +souvent au prix d'une perte en performances (mémoire et CPU), pour chercher des erreurs +logiques plus difficiles à détecter (typiquement les erreurs de mémoire). + +La dernière couche est bien sûr d'automatiser tous les outils choisis, au plus près +du développeur. Déjà, un lanceur comme [`doit`](https://pydoit.org/) ou +[`just`](https://just.systems/man/en/introduction.html) permet de lancer tous les outils +de qualité choisis en une commande simple. Ensuite, nous pouvons tirer parti des hooks +de Git pour lancer automatiquement ces outils lors de certains commandes, et typiquement +lors du commit. Des utilitaires comme le fameux [`pre-commit`](https://pre-commit.com/) +ou le plus récent [`prek`](https://prek.j178.dev/) permettent de configurer facilement +les hooks de Git. Pour éviter de perdre le fil à attendre la fin de tests longs au +moment du commit, n'utiliser lors du pre-commit que des outils dont le temps d'exécution +est court (jusqu'à quelques secondes). + +## Nettoyer les tests + +Continuons par nettoyer les tests existants, car on peut bien sûr supprimer des tests. +D'abord, les tests non fiables doivent être désactivés ou supprimées, parce qu'ils +n'apportent pas l'information dont l'équipe a besoin : que le test passe ou pas, on n'en +sait pas plus sur la qualité du code testé. Ces tests pourront être réactivés une fois +stabilisés, ce qui suppose d'identifier la cause de l'instabilité. Le tirage de nombre +aléatoire, le déclenchement à partir de l'heure réelle, la (non-)synchronisation de code +exécuté en parallèle (multi threading, asynchrone...), la dépendance à des processus ou +des API externes sont les causes les plus probables. + +Par exemple, ce test échoue de façon imprévisible, car il dépend de l'heure réelle : + +```python +# Fragile : dépend de l'horloge système... et reste bloqué une heure ! +def test_reporting_each_hour(): + manager = Manager() # crée un rapport à chaque heure pile + assert manager.report is None # faux si le test est lancé à une heure pile + time.sleep(3600) # c'est long... + assert manager.report is not None +``` + +La solution est d'injecter l'horloge pour la contrôler depuis le test, ce qui le rend +à la fois fiable et instantané : + +```python +# Fiable : le temps est injecté, donc maîtrisé +def test_reporting_each_hour(): + clock = FakeClock(now=datetime(2025, 1, 1, 12, 1)) + manager = Manager(clock) # refacto de Manager + assert manager.report is None # démarrage 1 minute après midi : pas de rapport + clock.advance(timedelta(hours=1)) # on avance le temps sans attendre + assert manager.report is not None # nouveau rapport +``` + +Ensuite, les tests trop longs n'apportent pas l'information à temps aux développeurs. +Ils peuvent être supprimés, accélérés, ou séparés dans une seconde suite de +tests si vraiment le test est nécessaire et le temps d'exécution vraiment incompressible. +Cette seconde suite de tests sera toujours automatisée, mais pas lancée à chaque commit, +à une fréquence plus faible, comme une fois par jour (souvent la nuit), +ou seulement juste avant fusion sur la branche principale. L'important est que le +développeur n'ait pas besoin du résultat immédiat du test pour continuer à travailler, +mais que l'information apportée par cette suite de tests longs ne soit nécessaire +par exemple que pour une nouvelle version. + +Enfin, il ne faut pas hésiter à élaguer la suite de tests : certains tests ne sont pas +pertinents, parce que trop vieux, trop spécifiques, trop couplés ou qui testent trop peu +de code. + +## Rendre le code testable + +Les problèmes de test les plus sérieux sont le plus souvent dus au code à tester : +trop couplé et pas assez observable, il oblige le test à mettre en place tout un +environnement, à lancer tout le code d'un coup, à jouer tout un scénario complexe pour +atteindre l'état initial dans lequel le test pourra enfin être lancé, et à passer par +des moyens détournés ou fragiles (analyse des logs...) pour savoir si le test a réussi. + +La première chose est d'isoler au maximum les effets de bord, en particulier les +entrées/sorties, pour pouvoir tester le code spécifique au projet et la logique métier +indépendamment. Pour tous les projets qui ne sont pas de simples "passe-plats", un +maximum de code doit être testable sans préparation particulière, juste en l'exécutant. + +Trions ensuite les effets de bord en 3 catégories : trous noirs, fontaine blanche, et +le reste. Un trou noir est en écriture seule, on ne peut qu'y jeter des données qui sont +immédiatemement perdues. On trouve dans cette catégorie les systèmes de log et de +télémétrie. Une fontaine blanche, l'inverse théorique d'un trou noir, est en lecture +seule et ne peut être qu'initialisée, une seule fois. On trouve dans cette catégorie +le système de configuration, qui réconcilie variables d'environnement, fichiers, +ligne de commande... et met le résultat à disposition du reste du code. + +Trous noirs et fontaines blanches peuvent avoir une durée de vie équivalente à celle +du programme entier et une portée globale. Ce sont les seuls effets de bord qu'on peut +tolérer à travers toute la base de code. Bien identifiés, une mise en place commune à +tous les test permettra de les gérer une fois pour toute. + +Tous les autres effets de bords doivent être isolés, et si possible injectés dans le +code métier propre à leur utilisation, plutôt que détenus par le code métier. En effet, +une dépendance injectée donne toute liberté au test pour déconnecter l'effet de bord +et isoler le code, alors qu'un couplage fort rend le test difficile, surtout avec +les langages statiques. Avec ces langages, il faut par contre prévoir un type approprié +-pour les membres de classes qui seront remplacés par un substitut. La testabilité doit +-alors être prévue à la conception. + +```c++ +// Difficile à tester, instancie toujours une vraie connexion pas substituable +class MyClassWithInternalConnection { +private: + Client client; +public: + MyClassWithInternalConnection(const char* host, int port) { client = Client(host, port); } +}; +// Facile à tester, peut instancier un substitut comme une vraie connexion. Mais plus +// complexe avec un langage statique, propriété et cycle de vie du client à définir. +class MyClassWithInvertedDependency { +private: + // Nouveau type pour contenir la vraie connexion ou un substitut + AbstractClient& client; // peut contenir la vraie connexion ou le substitut +public: + // Dans cet exemple l'instance de classe prend la propriété du client + MyClassWithInvertedDependency(AbstractClient&& client_): client(client_) {} +}; +``` + +## Prendre en main son framework de test + +Tous les principaux langages ont un ou plusieurs frameworks de test, qui apportent +leur lot de fonctionnalités en plus de faciliter l'écriture des tests, comme : + +- lancement depuis un point unique +- organisation en suite de tests, suite de suites... +- gestion de l'environnement, de l'OS, des signaux +- fichiers et dossiers temporaires +- interception de l'entrée standard et des sorties standards +- rassemblement et structuration des résultats des tests +- setup/teardown : une paire de fonction spéciale pour respectivement créer et détruire + l'état initial d'un test +- fixture : une fonction qui "fixe" l'état initial pour un test (généralisation du + setup) +- assertions pour comparer des résultats à des valeurs attendues, ou vérifier un + comportement attendu (exception, appel de fonction...) +- injection de substitut ou mock +- parallélisation, plugins... + +Utiliser un framework de test apporte donc énormément, au prix du temps vite rentabilisé +de prise en main du framework. + +L'injection de substitut mérite un point d'attention : il s'agit de remplacer +sélectivement du code difficile à tester (souvent avec un effet de bord) par du code +propre au test, en réimplémentant l'interface du code original. +Par exemple, un objet de connexion à une base de données peut être remplacé par un +objet ne faisant rien, en renvoyant immédiatement une valeur fixée. + +Le substitut peut être développé spécifiquement pour émuler un comportement complexe +ou aléatoire, mais avec un retour instantané et répétable. +Il peut aussi être complètement vide, mais permettre de vérifier que l'interface a été +appelée, avec quels arguments... +Certains frameworks de langages dynamiques permettent même de créer des substituts sans +déclarer l'interface du tout, l'interface étant créée à la volée, pour juste débrancher +un effet de bord. +De nombreux plugins ou librairies proposent des substituts clés en main pour les cas +les plus courants : connexion, temps, aléatoire, base de données, logs... + +En combinant ces fonctionnalités, un test reste court et lisible. La structure +given/when/then (« étant donné / quand / alors », ou encore arrange/act/assert) +sépare nettement la préparation, l'action testée et la vérification, ce qui en fait +une documentation exécutable : + +```python +def test_alerte_envoyee_si_solde_negatif(): + # given : un compte à découvert et un service d'envoi de mail substitué + mailer = FakeMailer() + compte = Compte(solde=-50, mailer=mailer) + + # when : on déclenche la vérification du découvert + compte.verifier_decouvert() + + # then : une alerte, et une seule, a été envoyée au titulaire + assert mailer.envois == [("alerte_decouvert", compte.titulaire)] +``` + +Ici le substitut `FakeMailer` n'envoie aucun vrai mail : il se contente d'enregistrer +les appels, ce qui permet d'affirmer que l'alerte a bien été déclenchée, et une seule +fois, sans dépendre d'un serveur de messagerie. + +## Accélérer les tests + +De nombreuses pistes peuvent permettre d'accélérer les tests : + +- modulariser le code et éventuellement le séparer en plusieurs librairies. Ainsi, il y + a moins de code, d'entrées, de cas limites à tester, et chaque librairie est validée + indépendamment. +- préparer les artefacts (libs, exécutables, images...) pour le test (et la compilation + si nécessaire) pour qu'ils soient prêts à l'emploi et en cache. Utiliser une lib + précompilée est plus rapide que la recompiler avec tout le projet. Si un test a besoin + d'un conteneur, l'image doit être disponible en cache et prête à l'emploi dès que le + conteneur est lancé (pas d'entrypoint qui finit la mise en place par exemple). +- profiler ses tests, pour d'abord avoir le temps par test, et ensuite savoir où les + tests longs passent leur temps. + Cette étape est essentielle pour ne pas avancer à l'aveugle. +- paralléliser les tests (mais les workers de CI peuvent n'avoir qu'un seul coeur) +- factoriser la mise en place et le nettoyage entre plusieurs tests +- éviter d'attendre pendant un test (`sleep(...)`), à la place réagir quand l'action est + terminée. Il faut parfois modifier le code pour qu'il reporte son état (code de + retour, log, variable interne...) ou permette au test de l'obtenir avec une nouvelle + API. +- réduire la quantité de valeurs différentes testées, pour se concentrer sur les valeurs + attendues et les cas limites. +- focaliser les tests sur une séquence claire given/when/then, en évitant d'enchainer + trop d'actions +- optimiser le code dont le test est irréductiblement long. Parfois le problème vient du + code. +- activer sélectivement certaines options qui ralentissent les tests, comme la mesure + de la couverture ou la vérification de l'intégrité de la mémoire +- lancer sélectivement les tests selon le code modifié, facile quand le code est bien + structuré et modulaire + +Le remplacement d'une attente fixe par une attente conditionnelle illustre bien ce +gain : au lieu de dormir « assez longtemps » en espérant que l'action soit terminée +(lent, et fragile si la machine est chargée), on rend la main dès que la condition est +remplie. + +```python +# Lent et fragile : on attend une durée arbitraire +start_job() +time.sleep(5) +assert result_file.exists() + +# Rapide et fiable : on réagit dès que la condition est vraie +job = start_job() +wait_until(lambda: job.is_done(), timeout=5) # sort dès que c'est prêt +assert result_file.exists() +``` + +## Compléter les tests + +Une fois les tests existants remis en ordre de marche, il faut vérifier que la suite de +test atteint son objectif, et en particulier qu'il ne reste pas de gros trou dans la +couverture de test. Si ce n'est pas déjà fait, il faut ajouter la mesure de la +couverture à son framework. Attention, mesurer la couverture a tendance à ralentir les +tests, parfois beaucoup, et il faut alors prévoir une option pour l'activer +sélectivement. + +La couverture de test doit déjà être à peu près homogène sur la base de code, et donc la +première chose à rechercher est une partie du code non testée (fichier complet, classe, +fonction...). Ensuite, la couverture doit permettre de vérifier que la logique métier +(sans effets de bord) est très bien testée, avec une couverture tendant vers 100%. +Enfin, la couverture doit être analysée par rapport aux objectifs et ressources : est-ce +que le code non testé peut l'être facilement ? Est-il réutilisé massivement dans toute +la base de code ? Est-il critique ? Est-ce de la logique métier ? + +Une fois la couverture analysée, c'est le moment de revenir sur les tests des +entrées/sorties, dont la couverture de test n'est pas la métrique pertinente. Il est en +effet facile de les couvrir par des tests qui déconnectent tellement d'effets de bord +que le test n'apporte plus beaucoup de garantie. Il faut plutôt vérifier que les cas +normaux, limites et d'erreurs sont testés, vérifier que le lien avec le reste du système +soit réellement testé à un moment ou un autre (connexion à l'API, base de données...), +ou envisager un contrat de communication à valider (OpenAPI par exemple). + +Il n'y a pas de chiffre précis de taux de couverture à atteindre, surtout qu'il est +relativement facile de biaiser ce chiffre en déclarant des lignes hors couverture ou en +abusant des substituts. De plus, les derniers pourcents sont bien plus difficiles à +atteindre que les premiers. Un chiffre de 80% est une bonne base de réflexion, les +projets de qualité étant au-dessus. Il est plus important de suivre la tendance : au fur +et à mesure que le projet évolue, la couverture ne doit pas reculer, mais monter (même +lentement) au fur et à mesure que des bugs sont corrigés et donc des tests ajoutés. +Ajouter à ses pipelines de test la vérification que la couverture ne recule pas +empêche en particulier d'ajouter des fonctionnalités sans leurs tests. + +## Utiliser l'IA à bon escient + +Pour toutes ces tâches, plutôt rébarbatives et vues comme une perte de temps par rapport +à l'avancée du projet, l'IA est un auxiliaire précieux. Il peut être très tentant de +déléguer tout le problème à l'IA (et les derniers modèles feront un bon travail). +Mais les tests sont aussi le système qui garantit le fonctionnement du code, +et qui permettent au développeur d'engager sa responsabilité sur le code livré. +Est-ce que le test généré teste vraiment les points importants ? Ou est-ce qu'il s'agit +juste d'un spaghetti de substituts qui ne teste en fait rien du tout ? Dans tous les cas +la responsabilité est sur les épaules du développeur. + +Selon les développeurs et les projets, plusieurs stratégies sont possibles : générer +mais relire soigneusement, travailler par étape à partir des spécifications, écrire +manuellement les tests ou au moins les tests principaux, utiliser une IA vérificatrice +après la génération... + +Par délà l'appropriation des tests générés par le développeur, l'IA est surtout une +opportunité en or d'aller plus loin dans les tests : test de performances et +optimisation, test d'interface, fuzz testing, mutation testing... Autant de stratégies +de test avancées qui deviennent accessibles à tous les projets. + +## Conclusion + +Une fois repris en main, les tests apportent la confiance indispensable pour avancer, +et deviennent un outil de développement qui permet de livrer vite et bien. +À ce moment, l'équipe a acquis toute une gamme de nouvelles compétences, +de la prise en main du framework à l'optimisation, de la refactorisation du code à +l'ajout des tests manquant, qui rendent l'écriture de nouveaux tests banale. +Devenu un outil du quotidien, le test peut être écrit d'abord, dans certains cas en +partant des spécifications, faisant d'une corvée repoussée en fin de projet une étape +préparatoire qui signalera la fin du développement et garantira la qualité. diff --git a/uv.lock b/uv.lock index d3d0181..fa9c5f1 100644 --- a/uv.lock +++ b/uv.lock @@ -4,16 +4,16 @@ requires-python = ">=3.12" [[package]] name = "alembic" -version = "1.18.4" +version = "1.18.5" source = { registry = 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