TL;DR

• Reducing test maintenance and improving result accuracy are becoming core evaluation criteria for autonomous testing platforms
• Visual validation is increasingly used to ensure UI accuracy across web, mobile, and native applications
• These capabilities help teams maintain release confidence and reduce risk in complex and dynamic, user-facing experiences at scale

Modern software teams ship faster than ever, and testing teams need tooling that keeps up. In Q4 2025, Forrester published The Forrester Wave: Autonomous Testing Platforms, Q4 2025, evaluating autonomous testing platform providers.

Applitools is named a Strong Performer in this evaluation.

The momentum behind autonomous testing

Teams now build and ship across more devices, frameworks, and release cadences. That reality pushes quality practices toward higher automation, better maintenance efficiency, and faster feedback loops.

Forrester frames this market shift directly:

“This is why we changed this Forrester Wave category from ‘continuous automation testing platforms’ to ‘autonomous testing platforms.’”

The Forrester Wave: Autonomous Testing Platforms, Q4 2025, Forrester Research, Inc., Q4 2025.

What buyers should look for in autonomous testing platforms

When you evaluate autonomous testing platforms in 2025, three practical questions usually help teams make sense of the space:

• Platform fit: Can the platform support your mix of apps and test types, plus your workflows across engineering and QA?
• AI-infused automation: Does the platform reduce authoring and maintenance effort in a way you can trust and govern?
• Testing AI-enabled experiences: As more teams ship AI-enabled features, can your testing approach keep pace with new failure modes and higher variability?

These questions help teams connect product capabilities to real delivery constraints: speed, coverage, confidence, and operating cost.

How the report characterizes Applitools

This report describes Applitools’ approach through Visual AI and ML-resilience oriented toward UI accuracy and maintenance reduction:

“(Applitools) It features Visual AI to validate UI accuracy across web, mobile, and native apps and support modern digital experiences at scale.”

The Forrester Wave: Autonomous Testing Platforms, Q4 2025, Forrester Research, Inc., Q4 2025.

It also cites a strategy emphasis on reducing maintenance and improving accuracy:

“Applitools stands out for innovation, gaining an above-par score due to its Visual AI and ML-driven resilience that reduce test maintenance and improve accuracy.”

The Forrester Wave: Autonomous Testing Platforms, Q4 2025, Forrester Research, Inc., Q4 2025.

What this can mean for engineering, QA, and design teams in 2025

Engineering teams can treat autonomous testing as a way to protect delivery speed. When teams reduce flaky failures and avoid constant test repairs, they shorten the path from code change to deployable signal.

QA teams can prioritize scalability and governance. As test suites grow, teams need tools and workflows that improve coverage without creating unsustainable maintenance load.

Design teams can connect UI intent to release confidence. When teams validate UI accuracy consistently across browsers, devices, and releases, they reduce risk in UX-heavy, customer-facing journeys.

Across all three groups, teams can get more value when they align on what “quality” means for the product and then choose automation approaches that enforce that definition consistently.

Read the report

While you’re evaluating autonomous testing priorities for 2025, read the full report to understand the evaluation criteria, methodology, and vendor profiles in context.

Forrester does not endorse any company, product, brand, or service included in its research publications and does not advise any person to select the products or services of any company or brand based on the ratings included in such publications. Information is based on the best available resources. Opinions reflect judgment at the time and are subject to change. For more information, read about Forrester’s objectivity here.

The post Applitools Named a Strong Performer in The Forrester Wave™: Autonomous Testing Platforms Report, Q4 2025 appeared first on AI-Powered End-to-End Testing | Applitools.

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.

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.

Two recent episodes from Thoma Bravo’s Behind the Deal video series take you behind the scenes of Applitools—offering both a strategic and technical lens on how we’re transforming test automation with Visual AI and autonomous testing.

One episode focuses on the big-picture vision behind Thoma Bravo’s investment. The other digs into the founding story, engineering mindset, and what it really takes to build a testing platform that scales.

How Applitools Uses AI to Revolutionize Test Automation

Host: Carl Press (Thoma Bravo) | Guests: Alex Berry (CEO), Adam Carmi (Co-founder & CTO) | Watch on YouTube

• Why this is the inflection point for AI in testing
• How Applitools helps teams increase coverage while reducing maintenance
• The business logic behind Thoma Bravo’s investment

“With Visual AI, we’re dramatically reducing test maintenance while expanding coverage across the digital experience.”
– Alex Berry, Applitools CEO

Beyond Automation: How Applitools Improves Speed, Scalability & Accuracy

Host: Carl Press | Guests: Alex Berry, Adam Carmi | Watch on YouTube

• The origin story behind Applitools’ platform
• Challenges of scaling visual testing across devices and environments
• Insights from Alex and Adam on culture, leadership, and innovation

“Our goal was to solve the test flakiness problem for good—and make it effortless for teams to deliver quality at scale.”

– Adam Carmi, Applitools Co-Founder & CTO

What’s Next for AI in Software Development?

These episodes offer more than just company insight—they highlight the shifting expectations around quality, speed, and AI in modern software development. If you’re exploring how to future-proof your test strategy, or simply want to see what’s possible with Visual AI, these conversations are a great place to start.

Have questions about how this applies to your team? Reach out to start a conversation—we’re here to help you evaluate if the Applitools Intelligent Testing Platform is the right fit for your goals.

The post Behind the Deal: How Applitools is Scaling AI-Driven Testing appeared first on AI-Powered End-to-End Testing | Applitools.

AI is transforming software testing—but without clear context, even the smartest models can fall short. The new Model Context Protocol (MCP) aims to solve that problem, and it’s picking up momentum fast. Here’s what QA and development teams need to know—and why it matters right now. If you have questions about how we’re building for the future or how this fits into your testing strategy, let us know—we’d love to talk.

What Is MCP?

MCP, or Model Context Protocol, is an open standard designed to help applications provide AI models with structured context. Think of it as a standardized way for tools and systems to tell an AI assistant what’s going on—who the user is, what they’re doing, and what resources are available.

Anthropic introduced MCP in late 2024, and it’s already being adopted by major players like OpenAI, Microsoft, and testing leaders building next-generation AI workflows. Addy Osmani, an engineering leader at Google, calls MCP “the USB-C of AI integrations,” highlighting its potential to standardize the connection between tools and intelligent agents.

Why Context Matters in AI-Assisted Testing

Large language models (LLMs) are only as good as the context they receive. Without proper inputs, you get generic outputs—or worse, hallucinations. For QA teams using AI to generate tests, interpret failures, or automate user flows, missing context leads to fragile results and wasted time.

MCP helps solve this by passing structured information to the model: which test framework is in use, what files are open, what code just changed, and more. That means faster, more relevant AI assistance—and more accurate automation.

What MCP Enables in Testing Workflows

MCP makes it easier for tools and AI assistants to share structured context—like which framework is active, what code changed, or what the user is trying to do. That unlocks more accurate test generation, better debugging, and scalable, reusable automation.

It also supports dynamic discovery, so AI systems can find and connect with available tools at runtime—no brittle configs or manual setup required.

As testers ourselves, we take a measured approach to adopting new AI standards like MCP. That means vetting integrations for stability and reliability, so our customers can move fast without sacrificing trust.

Why It’s a Big Deal Now

There are two key reasons to pay attention to MCP today:

First, the standard is taking off. Thought leaders like Angie Jones, Filip Hric, Tariq King, and Addy Osmani are publishing real-world MCP demos and contributing open-source tools. It’s not theoretical anymore—it’s happening.

Second, the stakes are high. As more testing platforms integrate AI (including Applitools Autonomous), the ability to connect tools through open standards like MCP is becoming a competitive differentiator.

How Applitools Fits In

Applitools has long focused on intelligent automation—delivering AI-powered test creation, visual validation, and self-healing across platforms. As open standards like MCP emerge, we’re building on that foundation to extend context-sharing across tools, so teams can:

• Automatically create or update visual and functional tests based on code changes
• Route test context through the pipeline for faster root cause analysis
• Improve AI-generated tests with better accuracy and explainability
  

Security is also critical. As MCP evolves, host-mediated permissions and encrypted communication protocols are being considered by contributors to ensure context is shared safely and responsibly.

At Applitools, we’re building these principles directly into the future of Autonomous and Eyes—and we’d love to walk you through what’s on our roadmap. If you’re already an Applitools customer, reach out to your account team to schedule a preview conversation. If you’re not already using Applitools, schedule time with one of our testing specialists—we’re here to help.

The post MCP: What It Is and Why It Matters for AI in Software Testing appeared first on AI-Powered End-to-End Testing | Applitools.

We’re proud to share that Applitools has been named AI-Powered Test Automation Platform of the Year 2025 by CIO Review.

Selected by a panel of C-level executives, industry thought leaders, and the editorial team at CIO Review, this recognition highlights the meaningful progress we’re making toward truly intelligent, AI-driven testing.

“We see this as validation of our vision—to move testing beyond automation and toward intelligent systems that know what to test, when, and why.” – Alex Berry, Applitools CEO

At Applitools, our mission is to help teams ship high-quality software with greater speed and confidence. From Visual AI to Applitools Autonomous, our Intelligent Testing Platform is designed to reduce test maintenance, streamline workflows, and help teams scale testing without scaling complexity.

Read the full feature article.

As we continue evolving what’s possible in software testing, we’re honored to be recognized by industry leaders who are shaping the future of technology.

The post Applitools Named AI-Powered Test Automation Platform of the Year by CIO Review appeared first on AI-Powered End-to-End Testing | Applitools.

Manual testing remains an integral part of software development but the increasing complexity of applications demands faster and more efficient testing methodologies. This is where Artificial Intelligence (AI) comes in, offering innovative ways to enhance manual testing efforts.

AI is not here to replace manual testers; instead, it acts as a force multiplier, augmenting their capabilities, reducing repetitive work, and improving accuracy. Something that has been proven multiple times is that AI cannot test or tell the look and feel of an application as well as a human.

In this blog, we will explore how AI can augment manual testing, making the process smarter, faster, and more effective.

The Role of Manual Testing

Manual testing involves human testers executing test cases without automation tools. It is essential for:

• Usability testing – Ensuring a seamless user experience.
• Exploratory testing – Identifying edge cases and unpredictable scenarios.
• Ad-hoc testing – Finding defects that automated scripts may miss.
• Accessibility testing – Evaluating how applications accommodate diverse user needs.

While manual testing is indispensable, it also comes with some challenges like the tests being time-consuming and repetitive testing that can take a lot of effort. It is also error-prone and can also miss some defects having a defect leakage in extreme scenarios.

In addition, with all the new and evolving technologies out there manual testing is not scalable. Therefore, AI helps address these challenges by complementing human testers, allowing them to focus on more strategic tasks.

How AI Augments Manual Testing

Test Case Generation and Optimization
Creating test cases manually can be labor-intensive and inefficient. AI-driven tools can:

• Historical defect data analysis to suggest optimal test scenarios.
• Dynamic generation of test cases from application changes.
• Optimizing test coverage by identifying redundant test cases.

Intelligent Bug Detection
AI can improve defect identification by:

• Analyzing log, UI, and user behavior to detect anomalies.
• Detecting potential failure points before they occur.
• Auto-classifying bugs to prioritize critical defects.

Automated Test Execution Suggestions
AI can assist manual testers by:

• Recommending test cases based on failure probabilities.
• Identifying high-risk regions that must be tested more.
• Proposing exploratory test paths based on real user activity.

Self-Healing Test Scripts
One of the biggest pain points in automation is script maintenance. AI-powered automation tools can:

• Automatically modify test scripts when the UI or functionality is changed.
• Reduce false positives via tuning to minor changes.
• Support script learning from previous runs.

Enhanced Exploratory Testing
AI does not replace a tester but rather amplifies them. Exploratory testing still relies on a tester’s experience and intuition while AI enhances this by:

• Providing test suggestions and hints based on application behavior.
• Building real-world usage scenarios for greater testing coverage.
• Identification of probable weak areas from historical trends.

Smarter Test Data Management
AI can streamline test data creation by:

• Synthesizing test data from application requirements.
• Identification of missing test data scenarios for better coverage.
• Masking sensitive data for security and regulatory purposes.

Visual and UI Testing
Ensuring a consistent user experience across multiple devices is challenging. AI-based visual testing tools can:

• Identifies UI anomalies and layout shifts on different screen sizes.
• Identifies color contrast issues for accessibility compliance.
• Baseline screenshot comparison with new builds to highlight differences.

Predictive Analysis for Risk-Based Testing
AI can help teams focus on high-risk areas by:

• Analyzing past test run data to predict probable failure points.
• Recommending test priorities based on defect trends.
• Removing redundant tests with optimal risk coverage.

This allows testers to focus their efforts on the most impactful tests, improving efficiency.

Chatbots for Test Execution and Assistance
AI-driven chatbots can:

• Provide instant visibility into test results and defect patterns.
• Execute test cases on-demand via natural interfaces.
• Assist the author in building and optimizing test scripts.

The Future of AI-Augmented Testing, The Perfect Combination

AI is transforming the way testing is conducted, but human testers remain indispensable. It would be a great challenge for a tester to now start adapting to the new trends, just like in the past we have had many opinions about automation until we actually saw how it helped our testing. 

The future lies in:

• Human-AI Collaboration – AI handles repetitive tasks, while testers focus on critical thinking and user experience.
• More Adaptive AI Models – AI will continue to learn from test results and user behavior, improving over time.
• AI-Driven Test Orchestration – Seamless integration of AI into DevOps for continuous testing and delivery.

Artificial Intelligence (AI) is transforming software testing but it remains a hot debate among testers. While AI enhances manual testing by automating repetitive tasks, improving accuracy, and speeding up defect detection some professionals still hesitate to embrace it.

However, instead of fearing AI testers should embrace it as a powerful ally. AI eliminates tedious tasks, improves efficiency, and allows testers to focus on critical thinking and creative problem-solving.

In Summary

AI is not replacing manual testers—it is empowering them. By automating repetitive tasks, optimizing test execution, enhancing defect detection, and improving exploratory testing, AI allows testers to focus on what truly matters: ensuring a seamless user experience.

As AI continues to evolve, testers who embrace AI-driven tools will be better equipped to deliver high-quality software faster and more efficiently. The key is to strike the right balance between human expertise and AI-powered augmentation, ensuring that software testing remains intelligent, adaptive, and effective.

Are you ready to embrace AI in your testing workflows?

The post How AI Can Augment Manual Testing appeared first on AI-Powered End-to-End Testing | Applitools.

In today’s fast-paced software landscape, teams must balance speed, quality, and cost—a challenge that traditional test automation often fails to meet. Testing bottlenecks slow down releases, defects slip through to production, and maintenance costs spiral out of control.

This is where AI-powered testing delivers significant business value. By automating test creation, execution, and maintenance, AI helps teams reduce costs, lower risk, and increase software reliability—leading to a clear return on investment (ROI). Let’s explore how AI-driven testing transforms software teams and drives measurable business outcomes.

The Growing Challenge of Software Testing

Modern applications introduce significant testing challenges:

• More Code, More Problems – AI-assisted coding tools generate more code, requiring robust testing to keep pace.
• Expanding Device & Browser Matrix – Users expect seamless experiences across devices, browsers, and screen sizes.
• Limited Testing Resources – Teams often lack the bandwidth to maintain comprehensive test coverage manually.

These realities create a gap between what teams should test and what they can test. AI testing solutions close this gap by increasing coverage, reducing human intervention, and making automated tests more resilient.

The ROI of AI-Powered Testing

Companies that implement AI-powered testing see improvements across four key areas:

1. Faster Release Cycles = Accelerated Time to Market

Traditional testing slows down software development, with teams often spending 30% or more of their time debugging and fixing defects. AI accelerates release cycles by:

• Automating test creation and execution
• Reducing manual intervention with self-healing test scripts
• Eliminating maintenance headaches caused by UI changes

2. Fewer Production Defects = Lower Business Risk

Bugs in production can lead to revenue loss, reputational damage, and compliance risks. AI-powered testing reduces defect leakage by:

• Catching more UI and functional issues with Visual AI
• Reducing false positives and negatives in test execution
• Identifying risks earlier in the development cycle

Try Applitools Autonomous for free and see how AI-driven testing enhances defect detection. Sign Up Now.

3. Reduced Testing Costs = More Efficient Resource Allocation

Hiring, training, and maintaining a robust QA team is costly. AI-powered testing optimizes costs by:

• Reducing test maintenance efforts by up to 40%
• Allowing non-technical team members to contribute to testing
• Increasing test coverage without requiring more human effort

4. Higher-Quality Software = Increased Customer Satisfaction & Revenue

Customers expect flawless digital experiences. AI-powered testing ensures:

• Fewer production issues that impact user satisfaction
• Smoother cross-device and cross-browser experiences
• Increased trust and retention from end users

A better user experience translates to higher customer retention, fewer support tickets, and increased revenue—a direct boost to the bottom line.

Calculating ROI: What’s the Business Impact?

Organizations that implement AI-powered testing can save millions annually by reducing test maintenance, accelerating releases, and minimizing costly defects. With the right tools, teams can quantify:

• Time savings in test creation, execution, and maintenance
• Reduction in defect-related costs (fixing bugs post-release is 30 times more expensive than catching them early)
• Operational efficiency—allowing teams to focus on innovation instead of repetitive testing tasks

Want to calculate your team’s ROI with AI-powered testing? Talk to our experts and see the impact on your bottom line.

The Future of Testing is AI-Driven

AI-powered testing isn’t just a technical advantage—it’s a business imperative. By improving efficiency, reducing risk, and lowering costs, AI helps teams deliver high-quality software faster while maximizing ROI.

Missed the full discussion? Watch the complete webinar replay for a deeper dive into the ROI of AI-driven testing. Watch now.

The post The Business Value of AI-Powered Testing: Maximizing ROI appeared first on AI-Powered End-to-End Testing | Applitools.

In our recent webinar, Building the Ideal CI/CD Pipeline: Achieving Visual Perfection, we explored the transformative power of Visual AI testing for CI/CD pipelines. Aimed at software engineering managers and team leads, the session provided a deep dive into the limitations of traditional functional testing and how Visual AI testing can surpass these to achieve visual perfection in software development.

Technical Customer Success Manager Brandon Murray shared expert strategies and highlighted the benefits of integrating Visual AI testing, offering guidance on constructing the optimal CI/CD pipeline. He explored the intricacies of Visual AI testing, illuminating its critical role in enhancing software quality and performance.

Challenges in Traditional Functional Testing

Murray began by identifying the bottlenecks commonly encountered in traditional functional testing. These include:

• High maintenance efforts
• Slow feedback cycles
• Limited UI coverage
• Tedious manual testing

The Power of Visual AI Testing

Visual AI testing offers a revolutionary approach to overcome these challenges. By capturing screenshots and using AI to compare these snapshots to a baseline ‘golden image’, Visual AI testing ensures:

• Reduced Test Development and Maintenance Time: Automating UI comparisons dramatically decreases the time spent on writing and maintaining tests.
• Complete UI Coverage: Screenshots ensure that every aspect of the UI is tested, eliminating blind spots.
• Enhanced Operational Efficiency: Faster feedback loops lead to quicker identification and resolution of issues, facilitating faster product releases.

Other Strategies to Supplement Visual AI Testing:

• Self-Healing: Automatically corrects flaky tests by adjusting for locator changes, vastly improving test stability
• Lazy Loading: Helps to ensure the entire page content is loaded
• Parallel Test Execution: Enables the execution of multiple tests simultaneously, significantly speeding up the testing process

Integration into the Development Workflow

Integrating Visual AI testing into existing development workflows, particularly with pull request checks, is pivotal for agile environments. The webinar emphasized the importance of instant feedback for swift issue resolution, leading to accelerated development cycles.

Tools and Technologies Highlighted:

• Cypress: Innovative testing framework for both developers and QA engineers
• GitHub Actions: Continuous integration and continuous delivery (CI/CD) platform enabling automation directly in GitHub repositories
• Figma Designs: Useful for collaborative design reviews and direct comparison against implementations

The session underscored the cost-effectiveness of using browsers on cloud infrastructure containers, especially when dealing with cross-browser coverage. Notably, the Filter Fast Grid was mentioned as an effective solution for this purpose.

Comparing Visual AI Testing to Traditional Methods

Attendees were eager to learn how Visual AI testing compares to snapshot tests and other traditional methods. The webinar demonstrated how Visual AI testing offers:

• Greater Accuracy: By leveraging AI for pixel-perfect comparisons
• Higher Efficiency: Through automated and parallel testing routes

In particular, using commodity CI solutions like GitHub Actions or CircleCI was recommended for their affordability and versatility.

Building the Ideal CI/CD Pipeline: Achieving Visual Perfection highlighted the transformative potential of Visual AI testing in optimizing CI/CD pipelines. Software engineering managers and team leads are strongly encouraged to evaluate how AI-powered tools like Applitools can elevate their testing processes, enhance product quality, and expedite delivery timelines. For those interested, a free trial of Applitools is available to experience the benefits firsthand.

The post Recap: Building the Ideal CI/CD Pipeline appeared first on AI-Powered End-to-End Testing | Applitools.

Forrester’s August 2023 report: The State Of TuringBots, 2023 examines the impact of AI on the Software Delivery Lifecycle (SDLC) and how organizations can effectively add this technology to their overall strategy. The findings were compelling and critical for modern businesses to understand so that they can adapt and stay ahead of the curve. Let’s take a look at some of the key points. 

Per the report, many organizations are grappling with the challenge of keeping up with business changes due to sluggish software development processes. The emergence of Generative AI has ushered in a new era of AI-assistive software, impacting industries irrespective of their SDLC maturity or existing AI utilization.

Forrester coined the name for this software as “TuringBots” which they now define as:

“AI-powered software that augments application development and infrastructure and operations (I&O) teams’ automation and semiautonomous capabilities to plan, analyze, design, code, test, deliver, and deploy while providing assistive intelligence on code, development processes, and applications.”

Forward-thinking organizations are embracing cutting-edge technologies like TuringBots to stay ahead. With GenAI revolutionizing numerous AI applications, the anticipated timeline for the development and impact of TuringBots has been accelerated to two to five years instead of a decade. This shift has brought about a deeper comprehension of the immense potential held by TuringBots.

Today, businesses rely on software as the backbone of digital operations, representing their strategies, processes, products, and services. However, many organizations need help in software development to match the rapid pace of business evolution and innovation. The report laid out common challenges like:

• Many developers still rely on manual testing despite automation advancements in the software development lifecycle. The lack of automation in various stages is attributed to tool complexity, skill gaps, and slow organizational modernization adoption.
• Lacking product management skills. The main hurdle in agile proficiency is the absence of business-led product ownership and management.
• IT that is resistant to change.

Per the report, academia and the tech industry have long aimed to streamline software development. With GenAI’s TuringBots, Forrester states that vendors accelerate product delivery by automating tasks and enhancing user experiences. The report recommends that it is time for tech leaders to empower their teams with TuringBots for maximum efficiency and take advantage of these benefits:

• TuringBots assist in development processes, though not fully mature for complete SDLC support.
• Easy access to project information is crucial for teams, covering project status, test completion, code check-ins, and more. Developers can save time by using Coder TuringBot plug-ins in IDEs like Tabnine or GitHub Copilot.
• These tools provide quick access to code snippets and information, helping to generate code efficiently through natural language chat.

The impact of TuringBots is significant across all industries, driving the transformation into digitally competent businesses. While the speed of adaptation varies, understanding and managing TuringBots is crucial for technology leaders. It is essential to swiftly grasp the key governance and best practices to mitigate risks related to insecure code, performance issues, and user experience.

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