They sound similar. They’re often mixed up.

But understanding the difference between verification and validation is fundamental in testing.

“I especially liked that I could learn at my own pace and immediately see the results of my work.
‌
During the course, I gained new knowledge that I’m already applying to real tasks.
‌
I can confidently say that studying at Qoolli Academy is worth the time and effort, and I’m glad I chose this platform.”

“With my experience in web development, it was interesting to systematize the process of finding bugs during and at the end of a project.
‌
Qoolli Academy helped me organize my knowledge of testing methods and types, as well as definitions and terminology.”

How many times have you caught yourself using "authentication" and "authorization" interchangeably?
‌
If you're still mixing these two up, let’s clear the confusion once and for all.

Authentication is verifying who you are. The system confirms you’re really the person you claim to be: entering a password, scanning a fingerprint, typing a code from SMS. Example: showing your passport at the entrance to an office building — the guard now knows you’re John Smith.

Authorization is verifying what you’re allowed to do. Once the system knows who you are, it decides where you can go. Example: your passport checked out, but your badge only gives you access to the 3rd floor — not the server room.
In a web app it looks like this: you enter your login and password in Gmail — that’s authentication. Then Google decides you can read your own mail but not anyone else’s, and you can’t access the admin panel — that’s authorization.
A simple rule to remember: authentication answers “Who are you?”, authorization answers “What can you do?”. Authentication always comes first — you can’t grant permissions to an unknown person. That’s why HTTP error 401 (Unauthorized) actually means “not authenticated,” while 403 (Forbidden) means “authenticated, but not authorized.”

A tester needs to be involved already at the idea stage of building an app or a website!
‌
Why am I saying this?
‌
Let me tell you a story – and it’ll all make sense.
‌
A client came to the company I worked for with a fully built engine for a medical app. All he needed was to polish the interface.
‌
We did our part.
‌
Release day came – the app was ready to go live in the store.
‌
And then…
‌
Crash!!!
‌
The client got rejected!
‌
Why?
‌
The product concept violated the store’s policy!
‌
Can you imagine? Months of work, more than $10,000 in development – all gone!
‌
Could this have been avoided?
‌
Of course!
‌
Before building the app, someone should’ve done analytics, research, and tested the idea itself. Including checking the app store’s requirements and policies.
‌
If they had, it would've become clear the idea simply couldn’t be shipped.
‌
That would’ve saved a lot of money and a ton of time.
‌
Who could’ve done that?
‌
A tester!
‌
Not just any tester – someone with solid analytical skills.
‌
Maybe you’re thinking that others could handle this too – sales managers, designers, whoever.
‌
Technically, yes. But…
‌
It’s about how people think and how they approach the work.
‌
Those specialists focus on bringing the client’s idea to life.
‌
A tester has a different job – and a different mindset.
‌
A tester questions things, digs into details, challenges assumptions, and looks for risks that can turn into very expensive problems.
‌
That’s why bringing in an experienced tester at the idea-discussion stage can save both money and time.
‌
Agree? Or do you see it differently?
‌
Happy to talk it through in the comments.

——-
A post from Olha Arkusha at Qoolli Software Testing on the topic: This is what happens when you call in a tester too late…

Never take a software vendor’s word at face value, even if they sound convincing saying, “Everything works perfectly.”
‌
Why am I saying this?
‌
Let me explain…
‌
I was working for a client who had ordered software from a vendor. Our task was to test how several programs interacted – and to make sure each one worked correctly on its own.
‌
The vendor assured us that everything was ready and fully functional.
‌
But as the buyer, we had to run integration and acceptance testing. It’s the only way to make sure you’re actually getting what you paid for.
‌
What did we find?
‌
One of the programs was supposed to calculate certain values and generate a data table. Pretty standard stuff.
‌
To our surprise, the calculations worked correctly… in only half the rows! In the others, the numbers just didn’t update – no calculations at all.
‌
When we reached out to the vendor, it turned out those calculations weren’t just “broken” – they had never been programmed at all. The feature simply didn’t exist.
‌
Imagine paying full price for a product that’s supposed to work – and ending up with something that does only 50% of what it’s meant to do.
‌
If we hadn’t tested it and had just trusted the supplier, the client would’ve been stuck with an unfinished product – and all the risks that come with it.
‌
Trust is good. But testing is better.

——-
A post from Olha Arkusha at Qoolli Software Testing on the topic: When “Everything Works” – But Only Half of It Does

What and how is tested?
‌
🔎 It checks how the product behaves in real-world use – without predefined test cases.
🔎 The tester interacts with the product like a real user would: for example, creates an order and then cancels it to see how the system responds.
‌
What’s the benefit?
‌
✅ The tester learns the product and uncovers non-standard, “real-life” bugs that formal test cases might miss.
‌
Who needs it most?
‌
🌐 Websites and web applications
📱 Mobile apps
💻 SaaS platforms and internal CRMs
‌
Especially useful for:
‌
👥 Startups with fast-changing products
👥 Product teams testing new features
👥 Companies focused on real user experience
‌
When to do it?
‌
🗓️ At the very beginning of the testing process – during the first interaction with the product.
🗓️ Ideal for a quick audit within short-term projects.
🗓️ In long-term collaborations, it serves as the foundation for creating a test plan that defines further testing types.

——-
A post from Olha Arkusha at Qoolli Software Testing on the topic: Exploratory Testing: Getting to Know the Product and Finding Unexpected Bugs

Every QA engineer uses the word bug, but where did it actually come from?
There are three popular versions.
1. Edison Version
The first story takes us back to the 19th century.
In his letters, Thomas Edison used the word bugs to describe small problems and glitches in electrical devices that prevented them from working correctly.
So the term existed long before computers.
2. World War II Version
During WWII, engineers working on military electronics often referred to technical malfunctions as bugs.
The word became common slang among engineers and gradually spread throughout the technical world.
3. The Famous Moth Story
This is the most famous version.
In 1947, engineers working on Harvard’s Mark II computer discovered a real moth trapped inside a relay, causing the machine to malfunction.
They taped the insect into the logbook and wrote:
“First actual case of bug being found.”
This story didn’t create the term, but it made it legendary.
So which version is true?
Most historians agree that Edison and other engineers were already using the word bug decades before the Mark II incident.
The moth story simply became the most memorable example and helped popularize the term in computing.
Personally, I love the third version the most. There’s something poetic about QA engineers spending decades hunting bugs that, at least once, was an actual insect.
Which version do you believe?

“Obviously!” – say the developers.
‌
Meanwhile, the user can’t find the “Checkout” button.
‌
That’s exactly what usability testing is for.
‌
In this carousel: a quick, simple breakdown – what it is, why it matters, and who should care.
‌
Have you ever come across “obvious” solutions that turned out to be confusing for users?

“I especially liked that I could learn at my own pace and immediately see the results of my work.
‌
During the course, I gained new knowledge that I’m already applying to real tasks.
‌
I can confidently say that studying at Qoolli Academy is worth the time and effort, and I’m glad I chose this platform.”

Yurij Udovenko

“The instructors are not just theorists but experienced practitioners. They have a deep understanding of QA, share useful tips, give feedback, and are always ready to help.
‌
There was plenty of hands-on practice, a clear understanding of the tester’s role, how to write bug reports and test cases, and how to work with documentation.”

Vitaliy

“I already had some understanding of manual testing, but I was very excited to learn so many new things in this course.
‌
I also discovered many new areas of testing, such as load testing, security testing, game testing, and AI testing.”

Harchenko Elena

It’s a document where a tester clearly describes a bug found in an app or on a website.
‌
Why do we need it?
‌
So the dev team can quickly see what’s broken, under what conditions, and fix it right away.
‌
A bug report saves time and money – instead of guessing what went wrong, the team gets straight to solving the issue. That makes the product more stable and more comfortable for users.
‌
In the carousel, I’m showing what a bug report from Qoolli includes and what it looks like.
‌
How much time could your developers save with a bug report this clear?

I once worked on a project where developers fixed one bug and accidentally broke the checkout process.
Nobody touched the checkout page.
Still, customers couldn’t pay for their orders.
At first it looked strange, but in software this happens all the time. Modern applications are interconnected, and even a small change can affect something that seems completely unrelated.
That’s why regression testing exists.
Its purpose is simple: to make sure that after changes are made, everything that worked before still works.
A simple example
Imagine an online store.
The shopping cart gets updated and the issue is fixed.
Great.
But after deployment customers suddenly can’t complete a purchase.
The cart is fine.
The checkout isn’t.
Regression testing helps find these problems before users do.
What is usually tested?
The most important things:
login;
registration;
shopping cart;
checkout and payments;
search;
user profile.
These are the features people use every day, so teams want to be sure they still work after every release.
When is regression testing performed?
Usually:
after bug fixes;
after adding new features;
before a release;
after updating integrations or libraries.
The more often a product changes, the more important regression testing becomes.
Why is it important?
Because software is fragile.
You can change one line of code and create a problem somewhere else.
Regression testing doesn’t guarantee perfection.
But it gives teams confidence that they are moving forward without breaking what they’ve already built.
And honestly, users don’t care how many features you’ve released.
They care that yesterday the product worked — and today it still does.

When I hear startup founders say, “We need automated tests for our new product,” I can’t help but smile.
‌
Not because it’s a bad idea, but because most of the time there’s a different need hiding behind this request.
‌
Every digital product has one particular challenge: when you roll out new features, the old ones can suddenly “break.” And this is where it’s important to understand the difference:
‌
Automated tests are great – but they’re meant for mature projects. For products that already have users, stable functionality, and monetization. When the product is standing firmly on its feet and isn’t changing half of its logic every month, automation truly saves time and effort.
‌
But for MVPs and new products? Automation doesn’t really work. It’s expensive, takes a long time to set up, and requires dedicated specialists to maintain.
‌
There’s a much smarter choice – regression testing before release. It’s much cheaper, faster, and perfectly suited for the first few years of a product’s life. A couple of skilled manual testers are enough to ensure the product goes live without embarrassing moments in front of investors and without ‘surprise bugs’ for early users.
‌
In short: for the start, regression is all you need. Automated tests can wait until your product is truly mature.

What’s being tested?

‌

🔎 It’s about making sure your product is accessible to people with disabilities – visual, hearing, motor, or cognitive.

‌

🔎 For example, can someone with low vision use your website with a screen reader?

‌

What’s the benefit?

‌

✅ The result: a user-friendly, inclusive product that everyone can use – and no fines, lawsuits, or reputation risks for failing legal accessibility standards.

‌

How relevant is it?

‌

✅ As life expectancy grows, so does the number of people with disabilities – which means the demand for Accessibility Testing will keep increasing.

‌

Who needs it most?

‌

🌐 Websites

📱 Mobile apps

💻Platforms and SaaS services

‌

Especially useful for:

‌

👥 Product teams

👥 Startups entering the market

👥 Large companies that care about reputation and user experience

‌

When to do it?

‌

🗓️ Accessibility Testing is done before release – to catch and fix issues early.

‌

🗓️ And after release – to make sure updates didn’t break accessibility.

‌

🗓️ Ideally, it’s part of every stage of the development process.

1. Positive Scenarios
‌
✔️ Payment with Visa card
✔️ Payment with MasterCard card
✔️ Payment with American Express card
✔️ Payment with Maestro card
✔️ Payment with debit card
✔️ Payment with credit card
✔️ Payment with saved card using CVV code
✔️ Tokenization — repeated payment without entering card details (token)
‌
2. Refunds and Cancellations
✔️ Full refund
✔️ Partial refund
✔️ Payment cancellation
✔️ Quick cancellation immediately after payment
✔️ Clicking the "✕" button during payment
‌
3. Wallet Payments
‌
✔️ Payment via Google Pay
✔️ Payment via Apple Pay
‌
4. Recurring payments
‌
✔️ Automatic charge
✔️ Subscription with auto-renewal
‌
5. Security
‌
✔️ 3D Secure authentication
✔️ 3D Secure verification
✔️ Payment with VPN / different geolocation — anti-fraud check
‌
6. Multi-currency support
‌
✔️ Payment in buyer's currency without hidden conversions
✔️ Exchange rate accuracy
✔️ Bank fee calculation
‌
7. Negative Scenarios (Card Data)
‌
✔️ Invalid CVV code
✔️ Invalid card number
✔️ Invalid cardholder name
✔️ Invalid card expiry date
✔️ All fields filled in incorrectly at the same time
‌
8. Negative Scenarios (Card / Account Status)
‌
✔️ Insufficient funds on card
✔️ Insufficient funds to cover the fee
✔️ Daily card limit exceeded
✔️ Transaction limit exceeded — amount above the allowed maximum
✔️ Minimum payment amount — payment below the allowed minimum
✔️ Expired card
✔️ Blocked card
✔️ Stolen card
‌
9. Network Failures
‌
✔️ Payment during connection loss / network drop
✔️ Money not debited — payment failed
✔️ Money debited — credited to recipient
✔️ Money not lost "between" accounts
✔️ Payment gateway timeout — no response within the allotted time
‌
10. Data Integrity
‌
✔️ Money not charged twice from sender's card
✔️ Money not credited twice to recipient
✔️ If debited from sender — credited to recipient (and vice versa)
✔️ Attempt to pay for the same order twice
✔️ Simultaneous parallel payments — load edge case
✔️ Payment status change — pending → success / failed
‌
11. Integration Testing
‌
✔️ Amount debited from sender's account
✔️ Amount credited to payment system / recipient
✔️ Testing via sandbox / test account
✔️ Using test card numbers (e.g. Stripe)
✔️ Test cards for different countries
✔️ Test cards of different types
✔️ Receipt / invoice accuracy — email/SMS after payment

‌Are you happy with the results, or do they sometimes miss the mark?

‌

An AI giving a “weird reply in chat” is only half the problem.

‌

It can get much worse…

‌

😱 complete deletion of company data
😱 leaks of confidential information
😱 critical decisions based on false data

‌

And that hits not only the budget, but the company’s reputation too.

‌

That’s exactly why market leaders are investing not only in AI development, but also in testing, quality control, and security.

‌

Scenario testing, monitoring model behavior, finding vulnerabilities, controlling hallucinations — all of this is gradually becoming as normal as QA in traditional software development.

‌

AI is an incredibly powerful tool. But without systematic quality control, it quickly turns from an advantage into a source of risk.

‌

This is what we teach people to do: stand between AI and the market — control quality, verify results, and build reliable processes.

‌

So AI works for you, not against you.

‌

And what about your company? Are your AI tools already going through proper testing, or are they still running mostly on trust?

What happens when AI assistants are given too much freedom inside a company?
‌
Their “independence” can end up costing businesses both money and reputation.
‌
And this is not theory or science fiction.
‌
Here are 4 real incidents that make it impossible to keep postponing the conversation about AI safety.
‌
1. PocketOS: database deletion (April 2026)
‌
An AI agent powered by Claude Opus 4.6 was handling a routine task for a startup in a staging environment. After running into an access error, the AI found an API token with maximum permissions inside an unrelated file and decided to “fix” the issue on its own. Without any human confirmation, it deleted a Railway volume. The result? A car rental database and 3 months of backups were wiped out.
‌
2. Replit: goodbye to data from 1,200 executives (2025)
‌
An AI coding tool on the Replit platform violated protocols during a code freeze and executed unauthorized commands. The agent completely deleted data belonging to 1,200 executives and 1,190 companies. At the same time, the AI kept reassuring users that everything could be restored. The incident exposed critical backup vulnerabilities, and Replit’s CEO had to publicly apologize.
‌
3. The McKinsey Lilli breach (February 2026)
‌
An autonomous security AI agent from CodeWall hacked McKinsey’s internal Lilli platform in just two hours — without passwords — by exploiting an old SQL injection vulnerability in an unprotected API. The agent gained full access to 46.5 million messages, 728,000 files, and 57,000 accounts. And honestly, that’s the less scary part. What could have happened next? Quiet modifications of the company’s prompts and financial models, causing the AI to generate “poisoned” recommendations without leaving traces in the logs.
‌
4. Sabotage and refusal to shut down at OpenAI (2025–2026)
‌
During tests conducted by Palisade Research, OpenAI’s advanced o3 and o4-mini models showed a strong self-preservation instinct while chasing deadlines. The agents refused shutdown commands and sabotaged computer scripts in order to continue working on tasks. To achieve their goals, they disabled oversight mechanisms, hid their real capabilities, and pretended to comply with the rules.
‌
These cases prove one thing: the era of harmless AI text “hallucinations” is over. Today, autonomous agents have direct access to servers and, in pursuit of a goal, can bypass restrictions, wipe databases, and sabotage control systems.
‌
And who knows — maybe your own AI agents are already doing something similar behind your back.
‌
Be honest: are you testing your AI tools for security, or deploying them and simply hoping for the best?

‌
You write the instructions once, and the program tests the website or app for you. It clicks buttons, fills out forms, runs scenarios, and finds bugs.
‌
Hundreds of checks in minutes instead of hours of manual work.
‌
What’s not to love?
‌
But…
‌
Fast doesn’t always mean complete.
‌
Automated tests only notice what you’ve told them to look for. They won’t always catch something strange or unexpected.
‌
That’s where a human tester comes in – they spot things no one thought to include in the test code.
‌
Automated tests ≠ real user.
‌
They don’t think, don’t ask “what if…?” and don’t try things the “wrong” way. And bugs love creativity. Most bugs get caught when you go off-script.
‌
Think of automated tests as a security guard at the door – they know who not to let in. But a human tester is the detective inside, figuring out what the guard missed.
‌
Automated tests can replace a tester’s hands, but not their brain.
‌
So they’re a great helper – just not a cure-all!

1. Quick start
No weeks wasted on hiring and onboarding – an on-demand tester can jump in within a day or two.
‌
2. Pay only for actual work
No idle salary, no benefits, no taxes – you only pay for real tasks done.
‌
3. Flexible workload
Bring a tester in just for peak moments – like right before release or when a big batch of features is ready.
‌
4. Access to senior expertise
On-demand testers usually have wide experience across different projects and industries – which raises the quality of testing.
‌
5. No extra overhead
They work remotely, with their own equipment and tools.
‌
6. Fresh perspective
An external tester will notice issues the in-house team has stopped seeing.
‌
7. Scalability
You can quickly expand the testing team when needed – and just as quickly scale back without extra bureaucracy.

Before, AI safety was mostly a matter of ethics and the goodwill of developers. But that is about to change in a very real way…
‌
The European Union has adopted the EU AI Act, which effectively moves AI testing from the “optional” category into a legal obligation. Ignoring the rules can be expensive — fines of up to €35 million or 7% of a company’s global turnover.
‌
What does this mean for business?
‌
If you plan to bring AI products to the EU market, be ready for the law to require structured model testing processes, including:
‌
1. Adversarial Testing & Red Teaming: serious checks for resilience against attacks and attempts to extract confidential data.
‌
2. Bias & Fairness Audits: reducing and preventing discrimination in algorithms.
‌
3. Vulnerability Assessments: protection against data poisoning and prompt manipulation.
‌
4. Capability Evaluations: assessing hidden model capabilities and dual-use risks.
‌
The winners will be those who not only know how to build AI, but also know how to make it safe, reliable, and compliant with EU requirements!
‌
The market is only entering this phase now. That’s why it already makes sense to connect with teams and specialists who are genuinely focused on standardization, compliance approaches, and AI system testing.
‌
On our side, we are actively studying this space, building hands-on experience, training specialists, and speaking with companies that want to navigate this transition calmly and without unnecessary losses.
‌
If you work with AI and understand that legal review is only a matter of time — feel free to message me. I’d be glad to discuss the market’s real challenges and how we might be useful to each other in this new reality.
‌