Functional vs Non functional testing

August 28, 2025

If you’ve been following my earlier posts, you might have noticed these terms sneaking in here and there: Functional and Non-Functional Testing. Interestingly, I’ve seen even seasoned QA managers pause for a moment when asked to classify a test as functional or non-functional. Yet, for any kind of software — whether it’s a small microservice or a large, integrated enterprise application — both are equally important in the test strategy.

So, what do these terms really mean?

Functional testing is about checking whether the application actually works. Does it do what it’s supposed to do? Think of it like trying out a ballpoint pen before buying it — does it write when you put it on paper?
Non-functional testing, on the other hand, looks beyond that. It’s about the experience and reliability. Will the ink smudge on thin paper? Will the pen last through a three-hour exam without drying up?

At the end of the day, most users are happy as long as the pen writes. But imagine it suddenly stops working halfway through your exam — that’s when frustration kicks in. And that’s exactly why both functional and non-functional testing matter.

Functional Testing

As I mentioned earlier, functional testing is all about ensuring that the system performs the intended functions exactly as the requirements describe. Like checking if a button is present, and if the button actually works when clicked. A test should be marked as a failure not only if the button is missing, but also if it’s there and working but labeled incorrectly — say it says “Sumbit” instead of “Submit”. It’s the little details that can make or break user trust once the product is in the hands of real users.

One of my favorite jokes about functional testing goes like this: “The developers built a bar. Before handing it over, they did thorough unit testing. They ordered 1 beer, 0 beer, -1 beer, and even 10,000 beers. Everything worked perfectly. Then the QA walked in. The developers proudly waited for his first test. The QA asked, ‘Where is the washroom?’” That, in a nutshell, is functional testing.

Throughout my career, I’ve met some truly creative functional testers. The best ones aren’t just button-clickers or screenshot takers; they act as a bridge between business and technical stakeholders. Deep knowledge of the domain, application, and requirements — combined with creativity and attention to detail — is what makes a great functional tester. Let’s be honest: translating a business vision into clear requirements is difficult. Sometimes requirements are vague. Sometimes developers misinterpret them. It’s the tester’s job to ensure the final product actually aligns with what the business envisioned.

Most functional tests are also highly automatable with scripting, especially regression and API tests, using both open-source and enterprise-grade tools. AI is starting to play a bigger role here too, from self-healing test scripts to intelligent test case generation, which reduces the maintenance burden. I’ll try to explore these in a separate article later.

Non-Functional Testing

I could go on and on about non-functional testing — after all, most of my career has been spent here. Non-functional testing is about validating the qualities of a system: how fast, how secure, how reliable, and so on. A non-functional tester expects the button to read “Submit,” but honestly may not care if it says “Hello World.” What matters is whether the button still works when it’s clicked a thousand times in parallel, or under varying load conditions.

While a good functional tester needs deep knowledge of the domain, business, and requirements, a non-functional tester also has to understand the application architecture inside out. What systems are involved? Where are they hosted? Are there third-party services? How do they communicate? How are the test and production environments configured? (I warned you I could go on about this… 😅)

The first — and most important — part of non-functional testing is identifying and classifying requirements and stakeholders. Different types of non-functional requirements lead to different categories of testing — performance, security, usability, compatibility, scalability, resilience — each requiring its own specialized skills and tool-sets. This is a deep topic, and I’ll likely dedicate a separate article just to that.

Automation exists here too, but it looks very different. A quarterly report from your functional automation team might proudly list 1,000 Selenium or UFT scripts. Meanwhile, your performance tester might only list 10 — and still be waiting for budget approval on a load generator license. That’s because non-functional testing isn’t measured by the number of scripts, but by the quality of scenarios, simulations, environments, and monitoring. For example, while a functional tester may try to break a form submission using special characters, a performance tester will simulate 10,000 users submitting the same form simultaneously — not to “break” it, but to monitor CPU and memory usage, track response times, and observe behavior under sudden spike loads.

I’m genuinely fascinated by how my functional testing counterparts are already making full use of AI — from self-healing scripts to intelligent test generation. In the non-functional space, especially performance testing where I spend most of my time, we’re only at the early stages of figuring this out. While functional testers use AI mainly to cut down on script maintenance, my own experiments have been more about using offline models to:

interpret messy result sets,
analyze or extrapolate long-term trends,
correlate observations across multiple systems, and
detect anomalies or even predict bottlenecks before they happen.

It’s still very much a work in progress for me, but that’s also what makes it exciting. To be honest, I feel like I’ve just scratched the surface — and every experiment teaches me something new.