Functional vs Non Functional Software Testing: What's the Difference?

Ever noticed engineers, IT, QA, and other assorted teams referring to tests as either 'functional' or 'non functional' but unsure what this means yourself?

You've come to the right place.

Below is a no-frills guide to what functional and non functional tests are and the differences in what they measure - with a couple of handy, real-world examples to illustrate.

And, because much of the info online is relatively dated, we've also outlined how the latest testing tools supercharge your testing processes and avoid some pretty entrenched black and white thinking about how to run and prioritize each category of test.

What do Functional and Non Functional Tests Measure?

Functional testing tests whether an app's features work as the software engineering team intended. Non-functional testing evaluates the performance of your app whilst running these features.

What does that mean, practically speaking?

Functional tests check that your app meets the requirements your developers and stakeholders identified at the start of the project. When you run a functional test, you're testing whether your app meets one or more of these pre-specified criteria, with a simple pass/fail response.

Non functional tests gauge how well your app performs these actions. Does it complete the action quickly? Is there any latency? How reliable is the feature under load? These aren't, strictly speaking, essential for checking whether your app works or not - but are vital for understanding what sort of experience it offers your users.

Take a look at a few examples to see what this difference looks like in real terms:

Examples of Functional and Non Functional Tests

'Functional' and 'non functional' are broad approaches to testing software. Foreach, there are several different types of tests you can carry out. Examples of functional tests include:

Unit tests
Unit tests evaluate whether a particular section of code is executing the action it's supposed to. Performed frequently across the software testing lifecycle, they give a quick pass/fail response.

Integration tests
During integration testing, you test multiple components of your software as a group, to make sure that different parts of your app can work together without causing issues.

Smoke tests
Smoke tests are preliminary tests performed early in the testing process to make sure key functions work - you might smoke test a login process before continuing with further testing.

End to end tests
Performed once the initial development phase has been completed, end to end tests involve testing the functionality of your product from a user perspective, helping you validate your application and its dependencies across entire user journeys.

Common non functional tests include:

Load tests
Load tests allow you to test your app under a range of conditions to see how it performs and identify potential breaking points that occur in different circumstances.

Security tests
Security tests help you identify weak points in your app that hackers could exploit - this could involve vulnerability scanning, penetration testing, or ethical hacking.

Performance tests
Performance tests check your app's ability to respond to requests quickly, and help you identify any latency issues which could cause frustration for users.

Usability tests
Usability tests gauge how easy users find completing actions on your app, using both quantitative and qualitative approaches.

Functional vs Non Functional Software Testing: Which is More Important?

Some would argue that functional testing takes priority, because it's more important to know that your app can execute a particular action before launch than measuring how well it does so. You can always optimize after the app is live, right?

You will run more functional tests than non functional tests, because they are smaller and faster, and yes - it's vital to check the nuts and bolts of your app. That doesn't mean you should consider non functional tests low priority.

It's now more important than ever to incorporate more non functional software testing into your pre-launch workflows. This is because:

1. Users really dislike janky, slow, difficult-to-navigate software - if you use a 'launch first fix later' approach to performance issues, you'll sacrifice customer trust and loyalty.
2. Software has never been more crowded - unless you've just patented a teleportation app, chances are you have a few competitors out there waiting to steal your customers.
3. Users can now find an alternative app provider in seconds via a quick online search, or by hopping onto the App/Play Store and downloading something else.
4. With many apps provided via subscription model, 'sunk costs' are significantly less, particularly when users first install your software - it's easier for users to part with a few dollars' worth of monthly subscription than a hefty perpetual license fee.

Basically, if your app performance is bad you should feel bad - because your users will find the first opportunity to jump ship and leave you. Good performance isn't just nice-to-have; it's a fundamental requirement for your app's viability, and the only way you can validate it is by upping your nonfunctional testing efforts.

Previously, time and resource limitations have caused businesses to limit nonfunctional testing, but (good news!) advances in AI technology mean you can run plenty of both without having to rely on clunky, expensive external QA.

How Do You Really Run Functional and Non Functional Software Tests in 2025?

A few years back, here's what the answer would have looked like:

If you're using the right tools, in 2025 things could look more to look like this:

In other words, you're saving time across the board thanks to AI - so:

1. Your developers can run tests quickly and seamlessly in house, for faster time to market and full CI/CD integration.
2. As well as speeding up the development lifecycle, you save a couple of million dollars on unnecessary external QA.
3. You can perform more non functional software testing significantly quicker, so that performance is a major selling point for your app.
4. You can redeploy manual testing resources to where they matter most, for example in-depth UX testing or ethical hacking.

How Do Low Code AI Testing Platforms Save You Time?

AI testing platforms like Momentic can shave a huge amount of time off both functional and non functional testing processes. With advances in machine learning and natural language processing tech, AI testing tools can:

• Generate scripts based on plain English prompts, significantly reducing the time needed to prepare complex test cases.
• Self heal or update test cases when you make changes to your app - no more flaky tests or tedious hours of script maintenance.
• Analyze previous code to predict bugs and defects, giving your devs a heads-up on high-risk areas of code.
• Search elements using plain English, so you can pinpoint a code snippet in seconds, rather than spending minutes scrolling.
• Make it easy for you to watch in real time for ultimate insight into how your app behaves from a user perspective - with automatic screenshots and recordings.

Momentic: Your Engineers' Software Testing Cheat Code?

"Momentic makes it 3x faster for our team to write and maintain end to end tests."
Alex Cui, CTO, GPTZero

We'd love to see if Momentic's AI testing tools could help you optimize your software testing life cycle.

If, like Alex and his team, you're keen to save over two thirds of the time you spend on key testing processes, why not schedule a conversation with our founder?