{"id":75577,"date":"2023-11-15T09:43:44","date_gmt":"2023-11-15T08:43:44","guid":{"rendered":"https:\/\/aton.com\/la-definizione-dei-mille-il-mondo-dei-test\/"},"modified":"2025-08-25T14:34:18","modified_gmt":"2025-08-25T12:34:18","slug":"the-thousand-definitions-world-of-testing","status":"publish","type":"post","link":"https:\/\/aton.com\/en\/the-thousand-definitions-world-of-testing\/","title":{"rendered":"A thousand tests: the world of software testing"},"content":{"rendered":"

Mid-October, an autumn day. It\u2019s raining outside and you\u2019re grateful to be working remotely. Music in your headphones helps you focus. Fingers tapping on the keyboard.<\/p>\n

Suddenly, an email notification breaks your concentration from the code that, strangely enough, wasn\u2019t behaving quite right: another pull request had been merged into the main branch.<\/p>\n

But this one was special. The release pipeline had successfully run 1,000 tests<\/strong>. Message to the team. A tray of croissants to celebrate the milestone \ud83e\udd73.<\/p>\n

That morning at Aton, when we realised we had reached 1,000 tests in the backend codebase, went more or less like this. It was a small but meaningful achievement,<\/strong> made possible by a journey and a shift in mindset<\/strong> that I\u2019ll try to share with you in this article.<\/p>\n

Let\u2019s begin!<\/p>\n

What\u2019s the point of writing a test?<\/strong><\/h2>\n

\"Test<\/p>\n

Tests<\/b> and\u00a0TDD<\/strong> (Test Driven Development<\/strong><\/em><\/a>)\u00a0are widely discussed across the tech community, with a substantial body of literature on the subject. Stripped right back, writing tests means writing code that verifies and locks in the behaviour of other code<\/strong>.
\nVerifying<\/strong> behaviour means checking that, given a certain input, you always get the same output\u2014both in the simplest, most linear cases and in the trickier, more deceptive ones. It also lets you confirm whether what you\u2019ve written actually matches the specifications defined during the analysis phase.
\nLocking things<\/strong> in, on the other hand, matters even more for keeping a codebase maintainable. A codebase evolves over time and is used by multiple people at once\u2014who may change shared or legacy logic. Having tests as a record of what\u2019s been done gives developers confidence that what they implement won\u2019t break compatibility with what already exists.<\/p>\n

None of this comes for free. Writing tests also means stretching development time to include activities like test setup<\/strong> (e.g. mock data, database seeding, configuration parameters), the tests themselves, and fixing business logic<\/strong> when\u2014inevitably\u2014some tests fail.<\/p>\n

At the same time, this extra development effort pays for itself when the code doesn\u2019t blow up in production and saves the developer a few sleepless nights of debugging.<\/p>\n

OK, that\u2019s the theory. But how do we write tests at Aton<\/strong>?<\/p>\n

Testing as part of the development process<\/strong><\/h2>\n

Since we began developing new product lines<\/strong> at Aton using modern technology stacks (Java\/Kotlin<\/strong> and Spring Boot<\/strong> for the backend, and Angular<\/strong>\u00a02 for the frontend), writing tests has become part of our developers\u2019 daily routine.<\/p>\n

Wherever possible, we aim to follow TDD: given a feature, tests are written first, and only then the business logic for that feature. Once development is complete, the tests are run to check whether the behaviour defined at the start is respected by the implementation.<\/p>\n

If all tests pass, the feature can be considered complete.<\/strong><\/p>\n

If some tests fail<\/strong>, there are two possible reasons:
\n1. The implementation does not comply with the specifications<\/strong>\u2014for example, it doesn\u2019t handle certain corner cases or it returns a data set inconsistent with expectations;
\n2. The test definition is wrong<\/strong>\u2014meaning the tests don\u2019t reflect the specifications, or they were created with input data that doesn\u2019t match the expected output.<\/p>\n

\"Test<\/p>\n

Regardless of the reason why tests have failed, the developer must go back into the code<\/strong> to ensure that all the tests relating to the feature under development turn green. Once this happens, the work can be considered complete and the developer can open a pull request<\/strong> with the changes.<\/p>\n

This is when the second phase<\/strong> of the backend development process comes into play.
\nCreating a pull request triggers a pipeline in Azure DevOps<\/strong> which diligently runs the tests for the new feature<\/strong> as well as for all existing ones. This step ensures crystallisation:<\/strong> it checks that the new logic doesn\u2019t conflict with what is already in place<\/strong>.<\/p>\n

If something breaks, the pipeline stops and prevents the release from being merged into the main branch.<\/p>\n

Final thoughts<\/strong><\/h2>\n

If, at the beginning of this article, you were wondering why we celebrated the arrival of the thousandth test so much, it should now be a little clearer.<\/p>\n

A thousand tests are not just a statistic<\/strong>, but the sign of a journey that has changed the way we work<\/strong>. A journey that is gradually industrialising the development process at Aton and that, thanks to testing, enables us to produce quality software<\/strong>.<\/p>\n

If you are still asking yourself whether writing tests is worth it<\/strong>, at Aton we can answer: yes, it is.<\/p>\n

It\u2019s worth it because it changes the way you work<\/strong>. It\u2019s worth it because it changes the way you think<\/strong>. It\u2019s worth it because it improves the quality of the code you write<\/strong>. It\u2019s worth it because it gives you the certainty that what you\u2019ve written doesn\u2019t break what was already there<\/strong>. And it\u2019s worth it because, in the end, seeing all those green ticks<\/strong> when the tests pass gives you a pretty good dopamine<\/strong> hit!<\/p>\n

Interested in joining us? Explore our current openings<\/div>
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\"linkedin-icon-img\"<\/a> \"instagram-icon-img\"<\/a> \"facebook-icon-img\"<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Mid-October, an autumn day. It\u2019s raining outside and you\u2019re grateful to be working remotely. Music in your headphones helps you focus. Fingers tapping on the keyboard. Suddenly, an email notification breaks your concentration from the code that, strangely enough, wasn\u2019t behaving quite right: another pull request had been merged into the main branch. But this […]<\/p>\n","protected":false},"author":15,"featured_media":70124,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false},"class_list":["post-75577","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","keyword-tech-en"],"acf":[],"yoast_head":"\nA thousand tests: the world of software testing - Aton<\/title>\n<meta name=\"description\" content=\"Still wondering if writing tests is worth it? 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What will testing look like in the future?<\/strong><\/h2>\n

Writing tests has brought great value to Aton<\/strong>, and we intend to continue supporting this practice over time.
\nThat said, the way we write tests may not remain the same forever.<\/p>\n

The topic of generative artificial intelligence<\/strong>\u2014and how it can become a tool to support developers<\/strong> (and beyond) by boosting productivity\u2014is widely discussed and highly relevant within the tech community.<\/p>\n

Among all the tools currently available, GitHub Copilot<\/a> is certainly the most useful for professional coders\u2014even when it comes to writing tests. By providing the right context, GitHub Copilot can generate unit tests<\/strong> (and other types) for a given feature.<\/p>\n

Using this approach comes with a number of pros<\/strong> and cons:<\/strong><\/p>\n

\ud83d\udc4d\ud83c\udffb Shorter development times<\/strong>. Developers spend less time writing tests and more time on business logic.
\n\ud83d\udc4d\ud83c\udffb Unforeseen test cases.<\/strong> AI can generate test cases the developer may not have anticipated, thereby strengthening the codebase.
\n\ud83d\udc4d\ud83c\udffb Unbiased test cases.<\/strong> Developers often feel attached to their own code and may end up writing conservative tests that don\u2019t really challenge it\u2014especially when the same person both implements the code and writes the tests.<\/p>\n

\ud83d\udc4e\ud83c\udffb False sense of security.<\/strong> AI-generated tests must always be reviewed<\/strong> by the developer to correct any misinterpretations or overly vague contexts.<\/p>\n

Whether this will become the definitive path for testing is something we cannot know for now.
\nHowever, at Aton we have already launched research and development activities<\/strong> focused on GitHub Copilot to understand the impact<\/strong> it could have on a developer\u2019s daily life<\/strong>.<\/p>\n