TL;DR

• Code-centric automation continues to slow teams down as UI changes multiply, making stability and evidence hard to maintain.
• AI code generators don’t solve the problem because they still produce brittle test code that requires constant oversight.
• Live LLM-driven execution introduces unpredictability. Regulated teams need deterministic runs, not improvisation
• A clearer path is intent-driven authoring paired with deterministic engines and Visual AI that detects visual drift and preserves audit-ready evidence.

Request our Governance Readiness Checklist

Teams in regulated environments face a familiar strain. Applications grow in complexity, expectations for fast releases keep rising, and every update requires clarity about what changed and whether required elements still appear as intended. Traditional automation wasn’t built for that pace or level of oversight, and the recent wave of AI coding tools hasn’t solved the core challenges.

A better model is emerging—one that uses AI to reduce the workload of authoring and maintaining tests while keeping execution deterministic, reviewable, and aligned with how people evaluate digital experiences.

This post breaks down why the legacy testing model is hitting its limits and how AI can support a more stable, more trustworthy approach.

Why traditional automation keeps slowing teams down

As digital experiences expand across pages, portals, member journeys, and product flows, test code becomes difficult to scale. Even minor UI changes break locators and assertions, creating unpredictable test runs, delayed reviews, and long maintenance cycles.

Developers are often asked to take on more of the testing responsibility. While this can improve feedback loops, it does not reduce the burden of maintaining code that reacts poorly to UI changes. And when teams already lack time, context switching between product development and test diagnostics becomes expensive.

The result is a predictable bottleneck: too many tests tied directly to implementation details and not enough stability across releases.

Why AI-generated test code hasn’t fixed the problem

The last few years have produced a surge of tools that promise to generate automation code automatically. But teams report the same issues repeating in a new form. LLMs can produce code quickly, yet the resulting output still inherits all the maintenance challenges of coded automation.

AI code generators also excel more at producing new code than updating existing flows. They struggle with assertions, hallucinate element behavior, and require human supervision to validate every step. For regulated teams that must show repeatability and generate evidence for every release, inconsistency becomes a risk rather than a convenience.

If the goal is to escape brittle code, producing more of it is not the answer.

Why live LLM-driven execution creates instability

Another idea gaining attention is allowing an LLM to operate the UI directly during test execution. In theory, this removes the need to write code. In practice, teams quickly run into new risks: undefined steps, inconsistent interactions, slow decision-making, and no reliable way to debug.

Execution in regulated environments must be predictable. It must be reviewable. And it must produce evidence that can be traced, explained, and defended. Live improvisation during a test run undermines each of these requirements.

Determinism matters more than novelty. A testing approach must produce the same result today, tomorrow, and during an audit review.

A clearer path forward: intent-driven authoring with deterministic execution

A more reliable model is emerging that uses AI to simplify authoring without relying on AI to make real-time decisions during execution.

Teams describe test intent in natural language. An AI system translates that intent into structured steps during authoring, where humans can review and adjust. Execution is then handled by deterministic engines and Visual AI that observe the rendered UI and detect visual changes, required-element presence, placement consistency, and contrast.

This separation delivers two advantages:

• People write and maintain far fewer lines of test code
• Test runs become stable, repeatable, and easier to verify

Visual AI provides a complete view of the screen state and compares each run against an approved baseline. When something changes, the system surfaces the difference, captures evidence, and supports reviewer approvals. When the change is expected, one acceptance updates the baseline and applies it across browsers and devices.

The outcome is a testing layer that is easier to maintain and easier to trust.

What this looks like in practice

Teams adopting this approach typically see changes across several parts of their workflow:

• Tests are written in plain language, without selectors or framework setup
• Visual AI validates full screens for layout, presence, placement, and readability
• Changes are highlighted automatically to reduce manual inspection
• Evidence is captured through screenshots, diffs, timestamps, and logs
• Debugging takes place in an environment where runs behave the same every time
• Reusable flows and data-driven steps integrate into the same natural-language format

Instead of managing a growing volume of fragile code, teams maintain intent-level descriptions supported by deterministic execution.

What this means for oversight and compliance

For teams in financial services, healthcare, insurance, or life sciences, the benefits go beyond efficiency.

A visually grounded testing model helps confirm that required notices, disclosures, language-access elements, and other regulated UI content remain present and placed as expected. It documents what changed and preserves evidence for review. It supports consistent experiences across browsers, devices, and PDFs without checking whether values, data, or regulatory text are correct.

Most importantly, it delivers predictable results.

Regulated environments depend on clarity and traceability. When every test run yields reviewable outputs, and every change is captured with context, teams can maintain confidence and release with speed.

If you’re assessing how well your testing workflow supports stability and audit readiness, request our Governance Readiness Checklist. We’ll share the version designed for your stage—whether you’re evaluating Applitools or optimizing an existing deployment.

The post AI Testing in Regulated Environments: Smarter Testing Starts With Stability, Not More Code appeared first on AI-Powered End-to-End Testing | Applitools.

Update & TL;DR

This post was written while Forrester’s research on agentic and autonomous testing was still emerging. Since publication, Applitools has been included in The Forrester Wave: Autonomous Testing Platforms, Q4 2025. The perspective outlined below reflects how this shift has since been validated and formalized by independent industry analysts.

• Agentic automation shifts testing from brittle, script-driven execution to intelligent systems that adapt based on change, risk, and context.
• AI augments human intent rather than replacing QA teams, enabling people to focus on quality strategy, governance, and risk decisions.
• This model is increasingly shaping how autonomous testing platforms are evaluated in the market.

Forrester, a leading global research and advisory firm, identified a major turning point in software testing in its Autonomous Testing Platforms Landscape, Q3 2025. The research describes a shift from traditional scripted automation to AI-augmented systems that can learn, adapt, and act under human guidance. This shift signals the rise of agentic automation: intelligent systems that create, run, and optimize tests within defined boundaries.

As delivery cycles compress and complexity grows, quality and engineering leaders are redefining what effective testing means in practice. Agentic automation bridges human intent with machine-driven precision—transforming testing from a reactive maintenance task into a proactive engine for reliability, speed, and continuous improvement.

From Automation to Intelligence

Traditional automation accelerated execution but left teams managing brittle scripts and endless maintenance. AI-augmented testing changes that dynamic. These systems:

• Learn continuously from results and application change.
• Adapt test scope and prioritization based on business risk.
• Optimize coverage while maintaining human oversight.

The result is testing that behaves less like a checklist and more like a self-improving quality partner, one that scales reliability across every release.

The Three Business Values Driving This Shift

Forrester highlights three outcomes motivating investment in more intelligent testing systems:

1. Accelerate Time to Value – AI-driven generation and self-healing shorten feedback loops and reduce maintenance.
2. Reduce Strategic Risk – Risk-based orchestration and built-in governance connect quality metrics directly to business priorities.
3. Democratize Testing – Low-code authoring and natural-language interaction let non-developers participate in quality, closing skill gaps.

Agentic automation brings these together: human-directed intent, machine-driven efficiency, and transparent oversight.

How AI-Augmented Systems Complement Human Expertise

AI in testing works best as augmentation, not replacement. By handling repetitive execution and maintenance, intelligent systems free QA professionals to focus on:

• Defining risk and coverage strategy.
• Establishing governance frameworks that maintain trust.
• Collaborating earlier with product and development teams.

Agentic automation shifts QA leadership from running tests to steering quality outcomes.

The Role of Visual and Experience Validation

Intelligent automation depends on reliable validation signals. Traditional assertions can’t always capture what matters to real users: layout, accessibility, and experience consistency. 

Visual and experience validation fill that gap, giving AI-augmented systems context they can trust. When machines validate what users actually experience, teams gain both speed and confidence—without rigid pixel-level comparison.

Building Toward AI-Augmented Readiness

Forrester describes this as a maturing market: organizations are blending traditional automation with AI capabilities to move toward greater autonomy over time. QA leaders can start by:

1. Stabilizing automation foundations and addressing flakiness.
2. Adopting AI-assisted detection of UI and data changes.
3. Integrating experience-level validation for richer feedback.
4. Connecting quality analytics to business metrics for continuous improvement.

Each step builds the trust and data maturity required for agentic automation to succeed under human orchestration. As adoption increases, these maturity steps align with how leaders in the market are being evaluated on autonomous capabilities.

What QA Leaders Can Do Next

Forward-looking teams are already experimenting with:

• Adaptive execution that prioritizes tests dynamically.
• Governance dashboards linking coverage, risk, and compliance.
• Visual AI that helps systems understand real user impact.

The goal isn’t full autonomy—it’s AI-augmented confidence: testing that’s faster, smarter, and more inclusive across roles. Read the full report now.

The post Agentic Automation: Preparing QA Leaders for the Next Leap in Testing appeared first on AI-Powered End-to-End Testing | Applitools.

When testing needs to keep up with faster releases and growing complexity, the challenge isn’t just what to automate—it’s how fast you can create and validate reliable tests.

Code and no-code testing now work together to accelerate test creation, expand coverage, and deliver faster feedback across browsers and devices. By combining AI-assisted test creation with visual validation, you can go from setup to scale in hours instead of weeks.

A Smarter Way to Split Your Effort

High-performing teams balance two types of coverage:

• 20% custom flow tests: Focused, AI-assisted checks for your most critical user journeys
• 80% visual coverage: Full-page validation across browsers and devices with Visual AI

This approach ensures your key flows are verified with precision while everything else is continuously validated for layout, content, and visual consistency.

Full-Site Testing in Minutes

With Autonomous testing, you can point to any URL—or even a subfolder—and let AI do the rest. It crawls your sitemap, creates baselines, and runs cross-browser and cross-device tests automatically.

Setup takes minutes. You can schedule recurring tests daily or weekly, and catch both visual regressions and new pages as they appear.

During one large-scale migration, this approach tested more than 1,500 pages across five browsers and devices. Visual AI caught thousands of small layout changes, grouped them by pattern, and reduced the workload to just 10 unique issues after a single fix acceptance.

Depth Where It Matters

For the 20% that need fine-grained control, AI-assisted test authoring speeds up creation. You can describe each action in plain English—“add item to cart,” “verify success message,” or “fill out this form”—and the system turns those steps into repeatable tests.

AI assists by:

• Generating realistic test data
• Creating textual and visual assertions
• Masking sensitive fields automatically

The result: fast, accurate flows that non-coders and engineers can both maintain.

Reliable Execution, Every Time

Applitools’ deterministic LLM executes steps based on visual descriptions, not fragile locators or XPath. That means if a class name or element ID changes, the test still runs correctly.

It also eliminates token costs and flaky reruns common with external LLM agents, since all logic runs natively inside the platform.

Data Validation Included

End-to-end validation doesn’t stop at the UI. Within the same test, you can call APIs, capture responses, and assert that backend data matches what appears on screen.

Visual results, API responses, and data integrity checks all happen within a single low-code environment.

Reuse More, Maintain Less

Reusable test flows—like login, cleanup, or environment switching—save time and cut duplication. You can parameterize roles or URLs, then reuse those flows across staging, integration, and production.

That modular structure lets QA, developers, and product teams collaborate without reinventing the same tests for each environment.

The Fast Track to Full Coverage

By combining AI-assisted test creation with Visual AI validation, teams achieve:

• Broader coverage with less maintenance
• Faster release confidence
• Consistent, human-readable results

Whether you write code daily or prefer a visual test builder, this blended approach keeps quality high and bottlenecks low.

Try It Yourself

See how AI-assisted testing speeds up coverage for your own apps with Applitools Autonomous, or explore how Visual AI helps teams validate every page and device in minutes.

The post Accelerate Test Creation and Coverage with Code and No-Code Speed Runs appeared first on AI-Powered End-to-End Testing | Applitools.

Software leaders often face a false choice: should testing be code-driven or no-code? The truth is, the strongest strategies use code and no-code test automation together. By letting each approach play to its strengths, teams cut bottlenecks, empower more contributors, and deliver quality software faster.

The Pitfalls of Choosing One Approach

When organizations lean too heavily on one side—whether code or no-code—the same challenges show up again and again:

• Skill gaps: Engineers and testers bring different levels of coding expertise, which creates dependencies and slows progress.
• Silos: Developers, QA, and manual testers often work separately, with little shared visibility.
• Maintenance overhead: Purely coded frameworks can be fragile and time-consuming to update, while a no-code-only strategy can limit flexibility for advanced scenarios.

Instead of streamlining releases, testing becomes another obstacle—especially when teams frame it as code versus no-code instead of embracing code and no-code test automation as a unified strategy.

The Strengths of Code-Based Automation

Code-based frameworks like Selenium, Cypress, and Playwright remain essential for complex cases. They provide:

• Flexibility and customization to test virtually any scenario.
• Fine-grained control over selectors, browser behavior, and environments.
• Precision that’s critical when working with complex workflows.

For engineering teams, code is still the best tool for edge cases and advanced automation.

The Strengths of No-Code Automation

No-code testing platforms such as Applitools Autonomous thrive on speed and accessibility. With plain-language test authoring and visual interfaces, they allow non-technical testers to contribute directly. This makes them ideal for:

• Regression and smoke tests that repeat across releases.
• Routine workflows that don’t require custom code.
• Broad participation across QA and business testers.

The benefit: engineers aren’t pulled into repetitive work, freeing them to focus on higher-value challenges.

Code + No-Code in Action

The difference becomes clear when comparing the two side by side. In one demo, a Selenium test for a simple e-commerce checkout flow took nearly an hour to script. Using Autonomous, the same flow—with assertions—was built in just two minutes.

The takeaway isn’t that one should replace the other. No-code handles what’s fast and repeatable; code handles the complex and custom. Together, they balance speed and depth.

Watch Code & No-Code Journeys: The Collaboration Campground now on-demand.

Real-World Proof: EVERSANA

EVERSANA INTOUCH, a global life sciences agency, illustrates what this balance looks like in practice. Faced with strict compliance requirements and fragmented workflows, they needed to unify testing across teams worldwide.

• First step: Adopted Applitools Eyes (code-based visual testing).
• Next step: Expanded to Autonomous, allowing global manual testers to build end-to-end tests in the browser.

Result: A 65%+ reduction in regression testing time, faster validation across browsers and environments, and a new “Autonomous-first” policy before assigning engineering resources.

The biggest change wasn’t only speed—it was collaboration. Developers, testers, and compliance began working from shared results, cutting duplicate effort and improving trust across the organization.

Read more about how EVERSANA INTOUCH cut regression testing time by 65% in the customer case study.

Takeaway for QA and Engineering Leaders

The question isn’t “code or no-code.” It’s how best to integrate both. For many teams, this means adopting code and no-code test automation to scale testing with confidence. By using no-code for regression and repeatable flows, and code for complex scenarios, teams reduce bottlenecks, shorten feedback cycles, and scale their testing with confidence.

For mid-size to enterprise teams, this balanced approach delivers:

• Faster test creation and execution.
• Greater collaboration across roles and skill levels.
• A testing strategy that keeps pace with modern release cycles.

Next Steps

Identify where no-code can relieve your engineers, and where code provides the precision you need. The future of testing isn’t about choosing sides—it’s about working smarter with both. Start your own code and no-code journey with Applitools Autonomous.

The post Why the Future of Test Automation is Code AND No-Code appeared first on AI-Powered End-to-End Testing | Applitools.

Testing has always been about more than just catching bugs. For QA and engineering leaders, it’s about enabling collaboration across teams, keeping pace with rapid release cycles, and maintaining confidence in quality. But traditional approaches often break down when skill gaps, silos, and tool fragmentation get in the way.

Modern testing platforms are changing that—not by replacing testers, but by using AI to bridge technical and non-technical team members, giving everyone a way to contribute to test creation and maintenance.

AI as the “Trail Guide” for Testing

Think of AI as an experienced trail guide: it understands the terrain, spots shortcuts, and helps both experts and first-timers reach their destination faster.

For testing teams, this means:

• Non-technical testers can describe flows in plain language and see them converted into robust test steps.
• Engineers save time on repetitive tasks and focus on complex automation.
• Teams build trust by working from the same results.

Key Capabilities of Modern Testing Tools

AI-powered platforms don’t just make testing easier, they expand what teams can accomplish together. Some of the most impactful capabilities include:

• Plain-language test authoring: Write test steps in English, not code.
• Interactive recording: Capture actions directly in the browser, instantly translating clicks into test steps.
• LLM-assisted authoring: Automatically generate test steps and validations.
• Data-driven testing: Parameterize values, generate contextual test data, and run variations without rewriting scripts.
• JavaScript injections for advanced logic: Give power users the ability to add complexity when needed.
• Self-maintaining suites: Tools can crawl a site, adapt to changes, and keep tests stable over time.

Deterministic LLMs: Reliable Execution at Scale

Not all AI is created equal. General-purpose models can hallucinate or create inconsistent results — exactly what teams don’t want in testing. Purpose-built, deterministic LLMs address this by focusing on consistency, speed, cost, and security:

• Consistency: Predictable execution without variance.
• Speed: Optimized models built specifically for test authoring and execution.
• Cost control: More efficient to run at scale.
• Security: Use of synthetic data ensures sensitive information is never exposed.

Visual AI for Complete Coverage

AI doesn’t just streamline test authoring. Visual AI extends coverage across devices, browsers, and operating systems with far fewer steps to maintain.

• Visual assertions reduce the need for brittle, locator-based checks.
• Multi-device coverage comes with less authoring overhead.
• Group maintenance lets teams accept or reject changes across multiple screens with a single action.

This creates both broader coverage and long-term scalability.

The Impact on Team Collaboration

The real value isn’t just in new features — it’s in how teams work together. AI-powered tools let QA, developers, and business testers all contribute to the same automated workflows. That reduces bottlenecks, speeds up release cycles, and shifts attention to what matters most: quality insights and critical thinking.

Takeaway for QA and Engineering Leaders

AI isn’t here to replace testers — it’s here to elevate them. By bridging skill levels, reducing repetitive work, and maintaining tests automatically, modern platforms create a more collaborative, efficient testing culture.

For mid-size to enterprise organizations, the benefits are clear:

• Faster test authoring and maintenance.
• Broader participation across roles.
• Reliable execution with reduced risk.

Next step: Watch Code & No-Code Journeys: The Collaboration Campground now on-demand, or speak with a testing specialist to explore how AI-powered testing can unify your team and simplify your QA strategy.

The post How Modern Testing Tools Use AI to Bridge Teams and Simplify QA appeared first on AI-Powered End-to-End Testing | Applitools.

Test maintenance is one of the most persistent bottlenecks in software quality engineering. From flaky tests and brittle locators to scattered tools and time-consuming debugging, teams often find themselves fixing instead of progressing.

With the right combination of AI-powered automation, no-code tools, and efficient test execution strategies, teams can reduce maintenance effort by up to 75% while improving reliability and accelerating feedback cycles.

Watch the full session now on-demand.

Top Techniques to Cut Maintenance Costs and Improve Test Stability

Use AI-Powered Self-Healing

When UI elements shift, traditional tests often break. AI-powered tools like Applitools Visual AI detect these changes and automatically adjust, reducing dependency on DOM locators.

Create Tests Without Code

With interactive browser recording and LLM-assisted test creation, teams can skip manual scripting entirely. Typing, “Fill out the form as a Disney character,” becomes a self-maintaining test with generated steps and realistic data.

Run Tests in Parallel Across Devices

Applitools’ Ultrafast Grid lets teams execute a test across dozens of browsers and devices in parallel. This helps identify platform-specific issues quickly without slowing down delivery.

Approve Changes in Bulk

AI detects patterns like currency updates or copy changes and groups them for bulk approval. You can accept or reject across multiple screens in a single click.

Consolidate Your Tool Stack

Instead of juggling five tools to cover visual checks, API tests, and accessibility, Applitools offers a unified platform. Less context switching means faster results and fewer points of failure.

Real-World Results: Peloton’s 78% Reduction in Maintenance

Peloton replaced a legacy testing solution with Applitools and saw a 78% drop in test maintenance. That’s over 130 hours saved per month. They automated more than 3,000 tests across web and mobile—without adding headcount.

Where Things Stand Now

Automated test maintenance can help reduce the overall cost of software testing by minimizing the time and resources required to update tests when application changes occur. Whether you’re building new tests or maintaining legacy suites, smart tools can shift the balance from rework to progress.

To see more of how Applitools leverages AI-powered automation, test grouping, and visual intelligence to reduce effort while increasing test coverage and confidence, speak with a testing specialist today.

The post Slash Test Maintenance Time by 75% with These Proven Strategies appeared first on AI-Powered End-to-End Testing | Applitools.

Have you ever encountered a situation where you try to take a screenshot, but instead of the beautifully well-crafted UI, all you’ve got is an image of a spinner/skeleton/loading screen? Handling animations and loading artifacts in visual testing can be daunting.

Don’t worry – it can happen to anyone, and we’re not here to judge you

One of the SDK engineers here at Applitools, Noam, breaks this down into a hands-on tutorial, hoping it will help you get a better understanding of the industry’s best practices around visual testing of rich and dynamic UI experiences.

Let’s dive right in!

Framework Native Solutions

Most frameworks already have different mechanisms to handle animations and loading artifacts. Keeping things simple is often the best way to achieve code stability and maintainability. Using your framework’s built-in tools would most often be the best approach.

For example:

Playwright JS

// Playwright: wait for spinner to be removed
await page.waitForSelector('.spinner', { state: 'detached' });
await eyes.check()

Cypress

// Cypress: wait for spinner to not exist
cy.get('.spinner').should('not.exist'); 
cy.eyesCheck()

Selenium JS

// Selenium: wait for spinner to be invisible
const spinnerElements = await driver.findElements(By.css('.spinner'));
if (spinnerElements.length) {
  await driver.wait(until.elementIsNotVisible(spinnerElements[0]), 5000);
}
await eyes.check()

A Common Pitfall

Even if the UI appears visually unchanged, frontend frameworks like React, Vue, and Angular may re-render elements under the hood. This can lead to stale element references, especially when capturing regions right after a DOM change.

Consider the following example:

cy.get('.main').then($el => {
  cy.get('.spinner').should('not.exist'); // spinner disappears after main was located

  cy.eyesCheckWindow({
    tag: 'main',
    target: 'region',
    element: $el, // stale reference if .main was replaced
  });
});

1. First, Cypress locates .main
2. Then, Cypress waits for the spinner to disappear
3. This example would fail (even if the new element has the exact same properties) if the main element is replaced by another element

How to avoid that?

When possible (e.g., Playwright), it’s preferred that you use locators instead of selectors. If you can’t, it’s better if you use selectors instead of DOM references (element: ‘.main’).

Videos, CSS Animations, GIFs

There are many techniques to eliminate other types of dynamic behaviour in web pages. Playwright, for example, provides a Clock API that allows pausing JavaScript time-related events (including JS-driven animations). It’s also possible to install custom CSS snippets to pause and reset CSS-related animations. Other JS specialized crafted snippets would be required for resetting GIFs, videos, and so on – you get the idea.

This never-ending cat-and-mouse game can be prevented by using Applitools Ultrafast Grid (UFG). Instead of rendering web pages on locally executed browsers, the UFG team maintains specialized logics and fine-tuned commands that ensure a stable and consistent rendering experience. While UFG offers more than just rendering stability, it’s worth noting that classic screenshots can still achieve stable results. UFG just makes it easier!

Algo-Based Solutions

If you intentionally want to capture dynamic content (e.g., animations, changes), a smarter strategy is to embrace that variability and use smart matching algorithms to compare just what you need, like those found in Applitools Eyes.

Any match level can be used for the entire screenshot or specific regions of the screen. Read more in the Match Level Best Practices tutorial. For example, algorithms like the Layout match level can drastically improve your experience with localization testing.

The waitBeforeCapture Setting

Performing wait operations can become more complicated when:

1. Testing with no-code visual testing SDKs (e.g eyes-storybook)
2. Testing with advanced Eyes features like lazyLoading and layoutBreakpoints

The waitBeforeCapture setting was invented for these types of use cases (and a few others).

This setting can receive three types of arguments:

1. Milliseconds – the simplest approach. While it’s not always the most innovative or sophisticated pattern, in many cases, it “does the trick.” In general, waiting for explicit timeouts during tests is not recommended. However, when compared to clock manipulations and code injections, sometimes the simplicity and stability is worth the longer run-time.
2. Selector – when we’re waiting for something to appear, most SDKs support passing a selector, and Applitools Eyes will automatically wait for an element that matches this selector to appear in the web page.
3. Custom function – see code example

// eyes-storybook
  waitBeforeCapture: async () => {
    while (document.querySelector('.spinner')) {
      await new Promise((resolve) => setTimeout(resolve, 100))
    }
    return true;
  }

// eyes-playwright
await eyes.check({
  name: 'my-step',
  async waitBeforeCapture() {
    await page.locator('.spinner').waitFor({state: 'hidden'})
  },
})

The waitBeforeCapture setting can be defined in your applitools.config file, in your eyes.check settings, using the Target settings builder, and in other similar places. Please refer to the documentation of the specific SDK you’re using for concrete examples.

Storybook Play Functions

A nice eyes-storybook-specific trick to achieve a desired rendering state would be a Storybook Play Function.

Applitools Eyes will run your play functions and wait for them to finish before capturing anything on the screen. Use Play Functions to navigate the story to an interesting state and wait for the story to be stable inside the play function to help Eyes understand what the best time is to capture the screenshot.

Applitools is Here to Help

We hope you’ve found this article interesting, and maybe it solved some of the most common visual testing issues you may have encountered. Go ahead and try these examples out for free with Applitools Eyes.

However, if something isn’t clear or if you’d like advice regarding the best way to incorporate visual testing into your organization, please don’t hesitate to reach out to our experts! Testing is our passion, and we’re here to help.

Quick Answers

The post Handling Animations and Loading Artifacts in Visual Testing appeared first on AI-Powered End-to-End Testing | Applitools.

TL;DR — Applitools brings visual, functional, and autonomous testing together in a single AI-powered platform. Backed by 11+ years of refinement and a dataset of 4 billion real-world images, our Visual AI delivers unmatched accuracy and reliability for enterprise-grade software testing.

Testing today isn’t just about coverage—it’s about confidence, speed, and scaling quality across teams. Whether you’re a developer chasing faster feedback, a QA lead reducing maintenance overhead, or a product owner focused on release velocity, Applitools helps modern teams deliver software that looks right, works right, and evolves with ease.

Here’s how Visual, Functional, and Autonomous Testing all come together in one powerful platform.

Trusted Visual AI with Proven Accuracy

Applitools sets the standard in Visual Testing. Our Visual AI engine delivers 99.9999% accuracy, eliminating false positives and catching bugs others miss.

• 5.8x more efficient than pixel-based tools
• Detect both functional and visual bugs in a single test
• Works with all major frameworks: Selenium, Cypress, Playwright, and more

We didn’t just add AI—we’ve spent 11+ years perfecting it.

A Complete Platform for End-to-End Testing

Applitools goes far beyond screenshots. Our Intelligent Testing Platform includes Autonomous Test Creation, Visual Validation, Cross-Browser + Device Testing, and Accessibility Testing—all in one cloud-based solution.

• Run tests across browsers, devices, and screen sizes in parallel
• Built-in accessibility and compliance testing
• Fully scalable with enterprise-grade performance

Less Test Maintenance with Self-Healing, Smart Grouping & Predictive Analytics

Spend less time fixing broken tests and more time delivering value. Applitools minimizes test upkeep so your team can focus on building.

• Self-healing locators reduce test flakiness and manual updates
• Root cause analysis, smart grouping, and predictive analytics streamline debugging
• Tests automatically adapt as your UI evolves—no babysitting required

Collaborative Testing: How Developers, PMs, Designers & Marketers All Work Smarter with Applitools

Testing shouldn’t be a bottleneck—or limited to just QA. Applitools empowers developers, designers, product managers, and even marketers to collaborate with ease.

• Intuitive UI for reviewing results and managing baselines
• Seamless sharing of results and issue tracking
• Codeless and code-based authoring, no deep technical expertise needed

More than a Decade of AI Leadership

AI isn’t new to us—it’s the foundation of our platform. Unlike newer tools making AI promises, we’ve been building, training, and refining Visual AI to solve real testing challenges at scale for more than a decade.

• Mature, battle-tested AI that improves with scale
• Trusted by engineering teams at Microsoft, Tesla, Salesforce, eBay, and 400+ more
• Over 1 billion visual tests run with our platform

Seamless Integrations & Dev Experience

Great testing fits into your workflow—not the other way around. Our AI-powered test automation works with your tools, languages, and CI/CD pipelines to scale quality without slowing you down. Applitools integrates with:

• Every major framework: Selenium, Cypress, Playwright, Puppeteer, WebdriverIO
• CI/CD tools: GitHub Actions, Jenkins, GitLab, Azure DevOps
• SDKs for Java, JavaScript, Python, C#, and more

Whether you’re in code or no-code workflows, we plug into your stack and scale with you.

24/7 Support That Doesn’t Disappear

Whether you’re mid-sprint or troubleshooting a release, help is always within reach. Get expert guidance anytime—no hoops, no waiting.

• Around-the-clock global technical support
• Extensive documentation, how-tos, and real-time guidance
• Active community forum and dedicated Customer Success Managers (not just for enterprise)

Compare that to competitors with limited support, slow response times, or no dedicated resources unless you’re a top-tier customer.

Smart Investment, Real Value

Our pricing is flexible, predictable, and scales with your needs. You’ll see ROI fast:

• Save hours of test maintenance per sprint
• Eliminate manual bug hunts and false positives
• Deliver faster releases without compromising quality

Explore our current pricing structure, or speak with a testing specialist to build a package that’s right for your team.

“We reduced our testing time from days to hours. Applitools changed how we think about QA.”
— QA Lead, Global Retail Brand

Visual, Functional, and Autonomous Testing—The Applitools Advantage

We combine Visual AI, Autonomous Testing, and a developer-friendly platform into one powerful, scalable solution. With Applitools, your team gets:

• Smarter test creation
• Less maintenance
• Better collaboration
• Faster releases
• And trusted results every time

See What’s New with Applitools Autonomous and What’s Coming with Applitools Eyes

Ready to Test Smarter?

In a crowded automation landscape, it’s not enough to have “AI-powered” features. You need real results. With over a billion visual tests run and trusted by leading enterprises across industries, Applitools isn’t experimenting with AI—it’s already delivering.

Whether you’re starting fresh or looking to scale smarter, Applitools gives your team the tools to automate with confidence and speed.

Ready to see it in action? Start your free trial, book a personalized demo, or explore the platform today.

Applitools helps you test like it’s 2025. Join the world’s top teams already doing it.

The post Visual, Functional, and Autonomous Testing—All in One appeared first on AI-Powered End-to-End Testing | Applitools.

The numbers are in, and five Applitools webinars have emerged as the most-watched so far this year. From no-code test creation to AI-driven automation and real-world ROI, these sessions delivered the strategies and insights that top testing teams are putting into practice right now. Whether you missed them live or want a quick refresh, we’ve rounded up the highlights and key takeaways so you can dive straight into the content that’s driving real results.

Building No-Code Autonomous End-to-End Tests

The dream of building fully autonomous tests without writing a single line of code is now a reality. In this session, Adam Carmi, Applitools Co-Founder and CTO, demonstrates how to leverage Applitools Autonomous to create robust, end-to-end tests that execute with speed and precision—no hand-holding required.

Key Takeaways:

• How to set up and run no-code tests in minutes
• Real-world examples of scaling tests across multiple environments
• Reducing maintenance costs by up to 80%

Watch the Webinar: Building No-Code Autonomous End-to-End Tests

AI-Assisted, AI-Augmented & Autonomous Testing: Choosing the Right Approach

Not all AI is created equal. In this session, we break down the differences between Assisted, Augmented, and Autonomous testing models. Learn when to deploy each for maximum impact.

Key Takeaways:

• Clear definitions and use cases for each AI model
• How to integrate AI into existing testing pipelines
• Choosing the right strategy for different application types

Watch the Webinar: AI-Assisted, AI-Augmented & Autonomous Testing

Creating Automated Tests with AI

What if you could create fully automated tests with just a prompt? In this session, Cory House, Playwright, React and JavaScript specialist, explore how tools like GitHub Copilot, ChatGPT, and Applitools Autonomous are changing the speed and reliability of automated test creation.

Key Takeaways:

• Generating test cases from requirements and prompts
• Reducing manual authoring with AI-driven test generation
• Integrating Copilot and Autonomous for seamless test runs

Watch the Webinar: Creating Automated Tests with AI

The ROI of AI-Powered Testing

AI-driven testing is more than just hype—it’s delivering real business impact. This session dives into the hard numbers and real-world examples of how automated visual testing reduces costs and increases release velocity.

Key Takeaways:

• Measuring ROI with data-driven insights
• Reducing the need for manual testing by 70%
• Increasing deployment speed without sacrificing quality

Watch the Webinar: The ROI of AI-Powered Testing

Code or No-Code Tests? Why Top Teams Choose Both

Hybrid testing strategies are becoming the go-to for teams that want the flexibility of no-code with the depth of code-based tests. Eric Terry, Senior Director of Quality Control at EVERSANA INTOUCH, unpacks why top engineering teams are choosing both to maximize coverage and efficiency.

Key Takeaways:

• Combining code and no-code for better test coverage
• Reducing maintenance through smarter orchestration
• Scaling tests across browsers and devices seamlessly

Watch the Webinar: Code or No-Code Tests? Why Top Teams Choose Both

Ready to Elevate Your Testing Strategy?

Don’t miss out on the insights that are transforming how teams build, maintain, and scale tests. Dive into the full sessions and see how Applitools is pushing the boundaries of what’s possible in test automation. See all our webinars.

The post Top 5 Webinars of 2025: AI-Driven Testing, No-Code Strategies, and Real ROI appeared first on AI-Powered End-to-End Testing | Applitools.

As a Salesforce tester, if you’re unfamiliar with visual testing—don’t worry, you’re in the right place! This blog will walk you through everything from the basics of visual testing to practical Salesforce-specific examples. You’ll learn how to catch visual bugs that can break user trust and impact experience. By the end, you’ll be confident in applying visual testing to your Salesforce QA projects.

Let’s begin.

Real Examples of Visual Bugs in Salesforce

Consider this Scenario

You are working on a team and have just deployed a great feature. The developer is happy with how it turned out, and the project manager is satisfied with the delivery. As a QA, you gave the green signal after testing—everything looked perfect. And the best part? The entire process went smoothly, without any escalations or last-minute issues.

That’s what successful teamwork looks like: the developer built it right the first time, the tester confirmed, and the client exclaimed, ‘This is great!’ It’s rare that things go so smoothly every single time.

Is everything really this easy? In practice, the real world likes to surprise us…

A Real World Scenario

The deployment happened on Friday, and on Monday morning, you and your team received an email. In that email, the surprises begin to unfold. One by one, the bug reports start coming in.

Someone points out a couple of issues:

The first issue: the “Add to Cart” and “Configure” buttons overlap in the Salesforce CPQ cart, making it difficult for users to interact with them properly.

The second issue: a user reports that transparent PNG images do not display correctly on mobile devices when the ‘Optimize Images for Mobile’ setting is enabled in Communities.

What looked like a smooth and successful deployment is now showing signs of trouble.

Why Salesforce QA Teams Should Prioritize Visual Testing

As a Salesforce tester, visual testing means checking how the UI looks and behaves—not just whether it works.

You’re not just checking if buttons work or forms get submitted. As a Salesforce tester, you’re also making sure:

• The layout looks good on both desktop and mobile
• Fields show up correctly based on user roles and permissions
• Lightning components display well in different browsers
• Page layouts and themes don’t break the design
• Third-party apps don’t affect the visual appearance
• What you saw in UAT looks exactly the same in Production

This is especially crucial in Salesforce, where small changes in configuration or access can lead to big differences in what users see.

We all know that Salesforce is a robust platform where a lot of emphasis is placed on configuration, customization, personalization, and a user-centric approach. Specifically, when we talk about Lightning Components and Experience Cloud, the UI can vary based on several criteria, such as:

• Profile permissions
• Screen resolution
• Browser type
• And many other factors…

As a Salesforce QA team member, you may write and update many test cases, and everything might seem to be running smoothly in terms of functionality. However, unless you check how it actually renders, you’re missing a huge part of the user experience.

At this point, we can’t afford to ignore visual testing or say we’ll deal with it later. It has become a necessity to ensure usability.

How to Implement Visual Testing in Salesforce Projects

1. It all starts with awareness and education—helping your engineering team build the mindset to treat visual diffs just like code diffs, reviewing and approving them with the same seriousness.
2. Start small by choosing high-impact pages like the CPQ cart, lead detail, and dashboards. These are user-critical areas where even minor visual issues can hurt usability and trust.
3. Focus on consistently monitoring visual test results with every release and pull request. This helps catch unexpected UI changes early and maintain visual consistency.
4. Make visual testing part of your testing culture—ensure your team treats visual bugs with the same importance as functional bugs. It might be a bit challenging initially, but once this habit is established, it will bring strong results in the long run.
5. Take clear screenshots of important pages to set a visual starting point. Later, these baseline screenshots are used to spot any unexpected UI changes during testing.

How Visual Testing Improves UI Consistency in Salesforce

Here are some hard-learned reasons why Salesforce QA teams must take visual testing seriously:

• Brand Image and Customer Confidence: In Experience Cloud or partner portals, maintaining visual consistency is key to your brand’s identity. A broken UI leads to a broken reputation—what users see is what they’ll believe.
• Totally Unpredictable UI: You’re not just testing code. You’re testing flows, field visibility, and permission-based rendering, all of which affect the layout. With three releases every year, each bringing new updates and sometimes removing existing features, the UI can become unpredictable.
• High Deployment Frequency: With frequent changes in CI/CD pipelines, even small style updates can lead to visual mismatches. In real life, you might notice that something looks one way in UAT but appears differently in Production after deployment.
• Functional Tests Don’t Catch UI Issues: All your test scripts have passed—congratulations! But the point is, a poor layout can still disrupt the user experience. That’s exactly when visual testing comes into the picture.
• UI Changes Based on User Roles: Different users may see different screens or fields—visual testing helps make sure everything looks right for everyone.

Valuable Takeaways for Salesforce QA Teams

Whether you’re testing Sales Cloud, Service Cloud, or an extensively customized Experience Cloud portal, these key principles apply:

• Start Small with High-Impact Pages
  Begin with pages that matter most—such as CPQ carts, lead detail views, or dashboards. These are user-critical zones where even minor UI issues can impact adoption.
• Create Visual Baselines
  Take clear screenshots of important pages during UAT. Use them as reference points for future deployments to catch unexpected changes.
• Automate Screenshot Comparisons
  Don’t rely on manual ‘look and feel’ reviews. Use tools like Applitools to automate visual checks, test across browsers and devices, and ensure UI integrity on every release.
• Involve the Whole Team
  Encourage developers, testers, and product owners to review visual diffs during pull requests. It reinforces a culture where UI quality is everyone’s responsibility.

Don’t underestimate visual appeal as part of user experience—in Salesforce, a flawless UI through visual testing can be the difference between closing or losing a deal.

Wrapping Up: The Business Case for Visual Testing in Salesforce

Visual testing is super helpful when it comes to making sure your Salesforce app not only works well but also looks right on different browsers, devices, and for different users. 

In Salesforce, things like profile permissions, Lightning page setups, and screen sizes can change how components appear. That’s why visual testing is important—it helps you catch UI issues early.

It might seem tricky at first, but Salesforce testing with Applitools makes it easier. This blog gave you a basic idea of why visual testing matters to Salesforce QA teams and how you can get started. Now you’re ready to explore more and improve your testing process.

The post Why Visual Testing is Crucial for Salesforce QA Teams appeared first on AI-Powered End-to-End Testing | Applitools.

The excuse “we don’t have time to write tests” doesn’t hold up anymore. AI has reshaped the way teams approach software testing, making it faster, smarter, and more accessible than ever. Tools like GitHub Copilot, ChatGPT, and Applitools Autonomous can generate reliable automated tests without slowing down your development flow.

If you’ve ever struggled with limited testing resources or hesitated to adopt AI-enhanced workflows, now is the perfect time to embrace AI-powered testing.

How GitHub Copilot Helps Accelerate Unit Test Creation

GitHub Copilot can dramatically speed up unit test creation. It can generate unit tests directly in your editor with a single prompt. For example, typing “create unit tests for Hello.tsx” in VS Code can instantly produce functional test cases using React Testing Library.

While Copilot’s first drafts were impressive—correctly using accessible locators and matching key UI elements—it’s important to note that AI-generated tests often require slight refinements.

Expecting a one-shot from AI is probably unrealistic—but in my experience, it gets you pretty darn close.

Copilot typically picks up on your dependencies, infers structure, and outputs readable, executable tests. If the results aren’t perfect, for instance, using fragile selectors or inconsistent naming, you can quickly iterate. Adjusting your prompt often resolves these issues. In many cases, reprompting is faster than manual edits.

Accessible locators and consistent naming can be enforced through clearer prompting or by storing preferences in a centralized configuration file

The key? Good prompts make a big difference. Prompting Copilot to use best practices, like favoring accessible selectors, resulted in much cleaner and more reliable output.

Taking Testing Further with Playwright and Copilot

Beyond unit tests, AI can support end-to-end testing for full user flows. Using Copilot with a framework like Playwright, you can prompt test generation by simply referencing a live URL and desired interactions.

For example, pointing Copilot to a public demo app like TodoMVC and requesting end-to-end tests will often result in tests for adding, completing, deleting, and filtering tasks—all without writing code manually.

To further improve coverage, ChatGPT can help by generating a requirements document for the app. This doc acts as a guide to ensure tests align with expected behaviors.

The better the input we provide the LLM, the better output we’re likely to get. A requirements doc is a really important piece of input.

Once the requirements are defined, you can direct the AI to use them when generating tests, producing more complete and targeted coverage. Just remember to include your preferences for things like locator strategy and naming conventions in your prompt or project config.

The message is clear: Combining ChatGPT and Copilot creates a powerful AI-assisted workflow for test generation. This approach cuts down on manual scripting while improving test depth.

Boosting End-to-End Testing with Applitools Autonomous

Applitools Autonomous handles creating automated tests with AI differently. Instead of writing code or interacting with the DOM, you provide a URL, and the system automatically scans the app. It generates visual and functional tests and organizes results into a centralized dashboard.

Highlights of what Autonomous can do include:

• Crawl an entire application from just a URL and automatically generate visual and functional tests
  
• Use plain English commands to create, edit, and validate tests (no coding needed)
  
• Validate UI, behavior, and API responses in one workflow
  
• Capture dynamic data like confirmation IDs, verify API responses, and support parameterization without code

Unlike traditional recording tools, Autonomous intelligently builds stable, scalable tests while seamlessly validating across browsers. It even flags hidden 404 errors—showcasing the tool’s ability to catch issues early.

Another key point is that anyone, regardless of technical background, can create sophisticated tests using natural language. At the same time, it maintains the depth and flexibility senior developers demand.

Key Takeaways for Modern Testing Workflows

Today’s AI software testing tools are designed for real-world developer needs:

• Copilot accelerates unit and E2E test creation with natural language prompts.
• ChatGPT fills documentation gaps by drafting requirements for better test coverage.
• Applitools Autonomous redefines E2E testing, combining visual validation and functional flows—from UI to visual to API—and plain-English test authoring. It integrates these into a single, no-install SaaS platform.

AI doesn’t replace the tester’s critical thinking — it augments your workflow, helping you focus on improving test quality, not just checking boxes.

In Summary…

The landscape of automated testing is still evolving. With tools like Copilot, ChatGPT, and Applitools Autonomous, building and maintaining high-quality automated tests no longer has to be a slow, painful process. Whether you’re a front-end engineer, QA lead, or tech manager, adopting AI-powered workflows will free up your team’s time. It will increase your confidence in releases and bring better quality to every sprint.

Want to learn more about how to create automated tests with AI? Watch the full session on demand to see in-depth demos.

The post Creating Automated Tests with AI: How to Use Copilot, Playwright, and Applitools Autonomous appeared first on AI-Powered End-to-End Testing | Applitools.

If you’ve already explored how AI-powered, no-code test automation tools can expand who contributes to testing, the next question is: how do you choose the right AI approach for your broader strategy?

Teams today face more pressure than ever to deliver faster without compromising quality. Traditional test automation can’t keep pace—it’s often brittle, siloed, and difficult to scale across teams.

AI-powered testing offers new ways to accelerate coverage, improve stability, and reduce manual effort. But not all AI is created equal. Understanding the differences between AI-assisted, AI-augmented, and autonomous testing models can help you adopt the right tools at the right time—with the right expectations.

Understanding the AI Testing Landscape

AI is showing up everywhere in the testing conversation, but it’s not always clear what type of AI is in play—or how much human involvement is still required. Here’s a breakdown:

AI-assisted testing

These tools support engineers during test creation. Think: autocomplete, code suggestions, or debugging help. They speed up test authoring but still rely on someone writing the test manually.

AI-augmented testing

These systems go further by analyzing existing test repositories, usage data, or logs to identify missing coverage or redundant cases. The AI assists strategically, but the tester still has the final say.

Autonomous testing

This model allows AI to execute test scenarios based on higher-level inputs—like a test goal or an intent. With access to the application, past test data, and usage patterns, it can decide what to test and how. Human oversight is still essential, but the AI drives more of the process.

Each model – assisted, augmented, or autonomous – shapes who can contribute to testing and how much oversight is needed. Choosing the right mix ensures your entire team can move faster without sacrificing quality.

Solving for Coverage, Speed, and Stability

As testing shifts left—and right—teams need solutions that can handle growing complexity without adding manual effort. AI helps in several key areas.

Reducing Flaky Tests

Flaky tests are a drain on time and confidence. They often result from brittle locators, timing issues, or inconsistent environments.

AI-powered self-healing automatically updates broken selectors when the UI changes, helping teams avoid rework and unnecessary test failures.

Authoring Tests Without Code

AI can also simplify how tests are created. NLP-based test creation, for example, allows users to define actions in plain English or record workflows that are translated into readable steps.

This approach has become one of the most accessible and impactful uses of AI in testing, enabling broader participation—from QA to product to manual testers.

Visual Validation for Real-World UI Testing

Functional scripts may confirm that a button exists—but they can’t always tell if it’s visible, clickable, or correctly placed. Visual AI ensures that tests validate what a user actually sees, not just what’s in the DOM.

This level of intelligence is especially critical for responsive design testing and dynamic layouts.

Choosing an Approach That Fits Your Team

The right AI testing strategy depends on where your team is in its automation journey.

• If you’re accelerating test writing with existing frameworks, AI-assisted tools may be the quickest win.
• If you’re optimizing test coverage and reducing redundancy, AI-augmented systems can help prioritize the right areas to test.
• If you’re expanding test ownership across roles, autonomous testing—especially when paired with no-code NLP creation—offers the scale and accessibility to match.

Many teams benefit from a layered approach, combining all three models across workflows.

And behind the technology, delivery matters. Tools powered by in-house AI models offer faster, more consistent results with greater control over privacy and cost—key factors for scaling in enterprise environments.

What’s Next

AI in testing isn’t about replacing people—it’s about enabling them to do more with less. Whether you’re automating UI tests with NLP, analyzing risk with augmented AI, or building autonomous test flows, the goal is the same: faster releases, better coverage, and fewer late-cycle surprises.

Want to explore how different AI models can work together across your test strategy? Watch the full session on demand and see how teams are applying AI-powered testing models to scale quality without increasing complexity.

The post AI-Powered Testing Strategy: Choosing the Right Approach appeared first on AI-Powered End-to-End Testing | Applitools.