How QualityLogic Uses Automated QA Testing to Drive Speed and Cost Efficiency
Every customer we’ve ever spoken with needs to build and release software faster, move test programs along at pace, manage higher volumes, and find cost efficiencies all along the development and test lifecycle. This reality means teams have quickly shifted code development to AI. In fact, some companies are using AI code all or most of the time: Spotify hasn’t written a single line of code since December and Anthropic reportedly uses AI to write 70-90% of their code. This often allows teams to ship much faster than just a few years ago.
But, this shift to AI coding increases the need to make sure it actually did what you wanted it to do and that it meets the needs of your audience. Research shows that 46% of developers distrust AI output and 66% say that “almost right but not quite” is their biggest frustration.
Teams are not able to review this AI-generated code at pace. This means unreviewed code piles up release after release. Some high-adoption teams have seen a 91% increase in code review times, as humans struggle to keep pace with machine-generated output. We call that verification debt, and we think it’s the defining quality problem of the AI era. The velocity that software teams need is ONLY sustainable with the right quality controls.
Automated QA testing, paired with humans guiding the strategy and implementation, helps teams speed up testing and allows more code to be tested, but it only works if you automate the right things with the right tools. Automate the wrong things, and you trade slow manual cycles for tests that break every time someone moves a button. Also, deciding what’s worth testing, and whether a passing suite means the product works, still takes people who know what they’re looking at.
So, here’s where automation makes sense, the tools we use, and how we put them to work for our customers.
Where Automated QA Testing Pays Off
Three questions tell you whether a test is worth automating:
- Is your code stable? A test tied to a screen where elements are redesigned every release will keep breaking, and you may end up spending more time fixing the test than it saves.
- Will the test run often? A test you run on every release earns back the time it took to build. One you run twice a year usually doesn’t.
- How much does a failure matter? Automating payments or login is worth it because a failure there costs real money. A settings page that changes the color of the interface maybe doesn’t.
When a test is stable, runs often, and covers the kind of failure that costs you money or users, automation earns its place and keeps paying off every time it runs.
Regression testing is the clearest win. Confirming that existing features still work after every code change is repetitive, predictable, and high-stakes work—hitting all the criteria we just outlined. Also, running it by hand eats a significant amount of the QA team’s time each release. Smoke tests, load and performance testing, and high-revenue paths like payments and login are strong candidates for the same reasons: they run constantly, and failures there cost real money.
But for jobs that take human judgment, manual testing remains the safest option. A script can confirm a button works, but it can’t tell you the button is in a confusing spot, and unlike a human tester, it won’t wander off the happy path to find the bug nobody predicted. Exploratory testing, usability, and accessibility are best performed by people. AI generates code so quickly that it can potentially introduce quirky user experiences. The user experience, of course, is critical to the success of a software product in the market.
Both have their place in modern testing programs. The strongest teams treat manual and automated testing as a pair. High-performing QA organizations know when to automate for efficiency and when to let a human tester take the wheel.
How QualityLogic Deploys Automation in Test Programs
We don’t start by putting tools in place. We start by sitting down with your development and quality teams to understand what you’re trying to achieve, what your current testing software stack looks like, and what your budget can support. The right approach looks different for a team shipping AI-generated code several times a day than for a platform with ten years of features behind it. So, discovery is where we start.
When your team is building with AI, we run QA as a parallel track to development. While your developers build against the spec, we build test automation against the same functional requirements. By the time the dev work is ready to merge, the tests are ready too. If the two don’t line up, either the implementation missed the requirement or the requirement was wrong. Either way, you find out before release instead of after: crucial for saving money, time, and user trust for your product.
We also write tests from your requirements, not from reading the generated code. Tests derived from code inherit the AI’s assumptions. Tests built from requirements check what you actually asked for. That independence is what makes the results trustworthy.
The Tools We Use for the Job
Testing tools are not all the same and one size does not fit all. Each one is right for certain jobs and wrong for others.
The newest tools target automation’s biggest hidden cost: maintenance. Teams without disciplined upkeep spend about 20% of their time fixing broken test scripts instead of testing new functionality.
Here are some examples of what they can do:
- Self-healing platforms now detect UI changes and update scripts on their own.
- Generative tools turn a plain-language user story into draft test cases in minutes.
- Agentic testing, the newest category, has an AI agent drive a real browser end to end from a test plan, with no hand-written scripts at all.
This class of tools are test automation frameworks, basically a library of assets and resources that can create test automation code. Some include a tie-in with AI tooling or related capabilities. These are the three we reach for most.
Playwright
Playwright has become the most popular tool for new web automation work. It leads framework adoption among QA professionals at roughly 45%, about double Selenium and triple Cypress. It connects to browsers directly rather than through an HTTP layer, which makes tests faster and far less flaky; most teams that migrate from Selenium report a sharp drop in flaky tests within the first month. It supports JavaScript, TypeScript, Python, .NET, and Java, and it fits cleanly into CI/CD pipelines.
When we build an automation program from scratch, Playwright is usually where we start for web. It’s often the engine behind our managed service for teams that want a stable Playwright suite built and maintained for them rather than staffing it in-house.
What about Selenium? It isn’t dead, but it’s a maintenance choice now. Keeping a large existing Selenium suite can make sense, especially if that is the primary tool approved by your organization. But building a new program on it may not make sense.
Cypress
Cypress keeps a loyal following, especially among JavaScript-first frontend teams. It runs in the browser right alongside your app, which gives developers fast feedback and excellent debugging while they code. It supports JavaScript and TypeScript only, so it fits best when your whole team already works in those languages and values quick local iteration over broad cross-browser coverage.
Appium
For native and hybrid mobile apps, Appium remains the open-source standard. It automates iOS and Android testing without recompiling your app or adding an SDK, and testers can work in the languages and tools they already know. It can intimidate beginners but paired with an experienced team it covers mobile testing end to end.
We help our clients select the best-fit tool for their program. While these tools are all popular, we meet clients where they are: we assess what they have, what they want, and provide advice and guidance. We are platform agnostic and have built new automation in just about every framework you can imagine (but we certainly have our favorites and can explain why).
Choosing a QA Automation Partner
Plenty of companies come to us after a tools-first automation push failed to deliver results, and they ask the same question: what should we look for in a testing partner?
We recommend that teams qualify and choose their vendors with these guidelines.
- Find a team that has experience in your industry. It’s like taking your car to a mechanic who specializes in your make and model instead of someone who knows cars in general. The specialist has context that a team who does not know your industry would not.
- Choose a partner who knows a variety of industry-standard QA tools. Be wary of a company that only uses or knows one tool: no one tool is a fit for every QA program.
- Look for flexibility. Whether you run two-week sprints or longer release cycles, your testing partner should adapt to your workflow and scale up or down as your needs change.
- Make sure they keep humans in the process. A tools-only approach can ignore the important aspects of strategic planning, judgment, and the ability to decide and balance priorities.
- Look for evidence of clear communication. You want regular updates and a team that flags roadblocks early, like a copilot who tells you about the storm ahead rather than after you’ve flown into it.
We also recommend treating cost as an investment decision. Cheaper isn’t always better: a bug caught in production costs far more to fix than one caught before release. An experienced QA team plus the right tool may cost more upfront but saves you far more in the end by catching defects before they reach production.
This is a topic we’ll keep returning to as the tools, practices, and pitfalls continue to evolve. If you want our take as things unfold, subscribe to the Quality Trail newsletter.
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