Test automation has traditionally lived in the hands of a few specialists—those with the right coding skills, framework knowledge, and time to maintain complex test suites. But software quality touches every part of the delivery process, from product to engineering to QA.

Modern no-code test automation tools are shifting that dynamic. These AI-powered platforms enable teams across roles to create, run, and maintain automated tests—without writing code. And they’re doing it without sacrificing speed, accuracy, or scale.

Here’s how these tools work, what they solve, and why they’re reshaping the way teams approach software quality.

Breaking the Bottlenecks of Traditional Automation

Traditional test automation frameworks come with steep requirements: deep technical skills, time-consuming setup, and scripts that only a few team members can decipher. This creates bottlenecks. When product owners or manual testers can’t contribute, test coverage shrinks—and feedback loops slow down.

No-code test automation tools address this challenge by allowing users to write tests in plain language. Instead of scripting every action, they can describe intent:

“Enter email in login form.”
“Click the primary button.”

This approach makes test cases easier to read, faster to debug, and simpler to hand off between roles.

From Recorded Actions to Readable Test Steps

Most no-code platforms offer more than just simplified language—they streamline how tests are created in the first place. With action recording, testers interact with the app as a user would. Behind the scenes, the tool converts those actions into plain-English test steps using AI and natural language processing.

This drastically reduces authoring time. And since the resulting steps are readable by anyone on the team, debugging and collaboration get a lot easier.

Compared to traditional scripting, this is a faster, clearer, and more inclusive way to build test coverage.

Expanding Who Can Contribute to Test Automation

When test authoring isn’t limited to engineers, more of the team can contribute to quality. That doesn’t just speed things up—it also improves collaboration and visibility.

• Manual testers move from documentation to execution without needing to code.
• QA engineers delegate simpler test flows and focus on complex or edge cases.
• Product owners and business analysts define expected behaviors directly in test interfaces.
• Developers get fast, readable test results that don’t require decoding selectors or scanning logs.

This shift improves velocity while reducing dependencies on any one person or team.

AI Behind the Simplicity: Powering Stability at Scale

The best no-code test automation tools go beyond accessibility—they’re backed by intelligent automation that’s production-ready.

• Self-healing fixes broken locators automatically, even when UI structure changes.
• Visual AI ensures the UI looks right—not just that elements exist in the DOM.
• Root cause analysis explains test failures clearly, saving hours of manual debugging.

These capabilities give teams confidence that their tests will work reliably across browsers, devices, and environments. And when the platform is powered by in-house AI (not third-party APIs), it ensures greater speed, privacy, and control.

Scaling Quality, Not Just Test Automation

No-code test automation tools don’t eliminate testers—they empower them. When everyone can contribute to testing, teams increase their coverage, accelerate release cycles, and reduce time spent chasing down brittle scripts.

What used to take hours of setup or deep technical expertise can now be achieved through a browser session and plain-English instructions. That’s the power of no-code—and the intelligence of modern AI testing tools.

Want to see how no-code test automation works in practice? Watch the full session on-demand and explore how teams are scaling test coverage with AI-powered tools designed for speed, stability, and collaboration.

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From facial recognition to self-driving cars, Artificial Intelligence (AI) and machine learning (ML) have become commonplace for many industries in recent years. In parallel, the software development industry has undergone a transformation of its own.

As customers look to engage more through digital experiences, businesses have been forced to evolve faster than ever before. Enticing and delighting customers in every aspect of product delivery has become “business critical,” determining if the customer chooses, and continues, to do business with you over a competitor. Although the discipline of Quality Engineering has remained unchanged, every aspect of how quality is delivered has evolved. Businesses can no longer trade off quality vs. speed, as both quality and speed must be achieved for modern digital-first businesses.

Recently, two reports came out that speak directly to the intersection of these two trends and discuss how industry leaders are leveraging AI to modernize their approach to Quality Engineering in 2021 and beyond.

The first, from EMA (Enterprise Management Associates), is titled Disrupting the Economics of Software Testing Through AI. In this report, author Torsten Volk, Managing Research Director at EMA, discusses the reasons why traditional approaches to software quality cannot scale to meet the needs of modern software delivery. He highlights 5 key categories of AI and 6 critical pain points of test automation that AI addresses.

In addition, over the last couple of months Sogeti has been releasing sections of their State of AI applied to Quality Engineering 2021-22 report (with still more sections to come through February 2022). This comprehensive report is created in partnership with leading technology providers to provide a detailed examination of the current state of artificial intelligence across many use-cases in the field of Quality Engineering and centers around a key question — how can AI make quality validation smarter?

As the application of AI to testing continues to advance, it is important to understand its potential and how it can help improve the quality, velocity, and efficiency of Quality Engineering activities. Below, I’ll discuss some of the highlights from both of these reports and what they define as the future of Quality Engineering.

The Emergence of Modern Quality Engineering and the Need for AI

First, let’s talk about the reason why traditional approaches to software quality and test automation are no longer sufficient. The first section of the Sogeti report gives an overview of the business pressure to release faster and increasingly complex technical environments. As Torsten discusses in his report, modern software development teams are faced with many challenges that have driven up the complexity and cost of quality, such as the explosion of device/browser combinations and application complexity. Multiply this by the number of releases per month and you can quickly see that the traditional test automation tools can no longer scale to the challenges of modern software delivery.

The biggest problem with the traditional approach to test automation is that it scales linearly. The more, or faster, you need to test the more human and non-human resources you need — which only works if you have an infinite amount of resources (do you?).

With this in mind, the EMA and Sogeti reports discuss the ways modern organizations can leverage AI/ML to streamline their test automation practices and scale to meet the increased pace of software delivery.

AI Scales Test Automation to Identify Issues Before They Impact Users

When it comes to Quality Engineering, certain tools are capable of controlling the graphical user interface (GUI) or an application programming interface (API). Others analyze coverage and recommend additional actions, and some analyze log files in search of specific behaviors. These are just a few examples. But to increase developer productivity, there needs to be an understanding of each of these tasks and how they can be optimized.

How does this relate to AI? AI has the ability to apply algorithms and approaches used in tools to perform human-like tasks. For example, a developer can reason by examining an application to determine whether or not it has been properly tested. If the testing cycle has fallen short, they can then determine what additional testing needs to be done. AI has the potential to act in a similar manner. Although AI may require some training, once it has been trained it has the potential to continue to test the function even as the application evolves.

The EMA report details five key AI capabilities that can help organizations streamline and automate parts of their quality and testing workflow:

• Test Creation/Smart Crawling: Automatic discovery of new and changed test requirements through the continuous analysis of changes in the application and natural language process (NLP) of documented requirements
• Self-Healing: Continuous and automated remediation of broken test workflows
• Visual Inspection: Training of deep learning models to inspect the application through the eyes of the end user
• Coverage Detection: Automatic detection of the different paths that end users can take through the application and reporting of gaps in code coverage
• Anomaly Detection: Automatic detection of system behavior that is inconsistent with the predictions of the AI/ML model

The report highlights the key advantage of each capability and then details how the capabilities can bridge the gap between the “ideal scenario” and “in real life” situation for six critical pain points of Test Automation: false positives, test maintenance, insufficient feedback, application complexity, device/use-case coverage and toolchain complexity.

Each capability is assigned a rating, ranking their current impact in 2021 and predicting their future impact in 2024. Visual inspection, implemented with Visual AI, has the highest rating for both current and future impact with the key advantage that it “Provides complete and accurate coverage of the user experience. It learns and adapts to new situations without the need to write and maintain code-based rules.”

The EMA report goes on to add that “Smart crawling, self-healing, anomaly detection, and coverage detection each are point solutions that help organizations lower their risk of blind spots while decreasing human workload. Visual inspection (Visual AI) goes further compared to these point solutions by aiming to understand application workflows and business requirements.”

As discussed in the most recent section of the Sogeti report, Shorten Release Cycles with Visual AI, Visual AI is already a mature technology, currently being adopted by leading brands across industries to accelerate the delivery of their digital experiences. The high levels of accuracy, and ability to handle dynamic and shifting content, ensures teams do not get overwhelmed with false positives. The automated grouping and categorization of regressions, coupled with root cause analysis, accelerates feedback and reduces test maintenance efforts. Visual AI provides test engineers with an additional “pair of eyes,” leaving them to focus on areas that really need human intelligence — the power and impact of this approach is enormous.

From AI assistance to Autonomous Testing

Currently the industry is focused on having AI remove repetitive and mundane tasks, freeing humans to focus on the creative/complex tasks that require human intelligence. And as Torsten mentions in his report, “AI-based test automation technologies can deliver real ROI today and have the potential to address, and ultimately eliminate, today’s critical automation bottlenecks.”

The ROI will further increase as we look to the future and the next big innovation for Quality Engineering, Autonomous Testing. Autonomous Testing will change the role of developers and testers from testing the application to training the AI how to use the application, leaving it to perform the testing activities, and then reviewing the results. This change will deliver a fundamental increase in team efficiency, reducing the overall cost of quality and enabling businesses to establish truly scalable Quality Engineering practices.

Editor’s Note: This post first appeared on devopsdigest.com.

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We’re honored to be co-authors with Sogeti on their “State of AI applied to Quality Engineering 2021-22” report. In the latest chapter, learn how you can use Visual AI today to release software faster and with fewer bugs.

In the world of software development, there is a very clear trend – greater application complexity and a faster release cadence. This presents a massive (and growing) challenge for Quality Engineering teams, who must keep up with the advancing pace of development. We think about this a lot at Applitools, and we were glad to be able to collaborate with Sogeti on the latest chapter of their landmark “State of AI applied to Quality Engineering 2021-22” report, entitled Shorten release cycles with Visual AI. This chapter is focused around this QE challenge and offers a vision for how Visual AI can help organizations that have not yet adopted it – not far in the future but today.

What is Visual AI

Visual AI is the ability for machine learning and deep learning algorithms to truly mimic a human’s cognitive understanding of what is seen. This may seem fantastical, but it’s far from science fiction. Our own Visual AI has already been trained on over a billion images, providing 99.9999% accuracy, and leading digital brands are already using it today to accelerate their delivery of innovation.

Leverage Visual AI to Shift Left and Deliver Innovation Faster

Visual AI can be used in a number of ways, and it may be tempting to think of it as a tool that can help you conduct your automated end-to-end tests at the end of development cycles more quickly. Yes, it can do that, but its biggest strength lies elsewhere. Visual AI allows you to shift left and begin to conduct testing “in-sprint” as part of an Agile development cycle.

Testing “in-sprint” means conducting visual validation alongside data validation and gaining complete test coverage of UI changes and visual regressions at every check-in. Bottlenecks are removed and releases are both faster and contain fewer errors, delivering an uncompromised user experience without jeopardizing your brand.

Teams that incorporate automated visual testing throughout their development process simply release faster and higher quality software.

How Can You Use Visual AI Today?

Wondering how you can move your organization or your team over to the left side of the bar charts above? Fortunately, it’s not hard to get started, and this chapter from Sogeti is an excellent place to begin. Keep reading to learn more about:

• When you should visually test your UI (and why it’s important)
• How to automate UI validation with Visual AI (including the three biggest challenges and how to overcome them)
• Different Visual AI comparison algorithms
• How Visual AI significantly reduces test creation and maintenance time while increasing coverage
• Streamlining analysis of test results and root cause analysis
• Using Visual AI for end-to-end validation
• Validation at check-in with Visual AI
• How Visual AI is revolutionizing cross browser testing

Deliver Quality Code Faster with Visual AI

Ultimately, what we’re all looking for is to be able to deliver quality code faster, even as complexity grows. Keeping up with the growing pace of change can feel daunting when you’re relying on traditional test automation that only scales linearly with the resources allocated – AI-powered automation is the only way to scale your team’s productivity at the pace today’s software development demands.

Applitools’ Visual AI integrates into your existing test automation practise and is already being used by the world’s leading top companies to greatly accelerate their ability to deliver innovation to their clients, customers and partners, while protecting their brand and ensuring digital initiatives have the right business outcomes. And it’s only getting better. Visual AI continues to progress as it advances the industry towards a future of truly Autonomous Testing, when the collaboration between humans and AI will change. Today, we’re focused on an AI that can handle repetitive/mundane tasks to free up humans for more creative/complex tasks, but we see a future where Visual AI will be able to handle all testing activities, and the role of humans will shift to training the AI and then reviewing the results.

Check out the full chapter, “Shorten release cycles with Visual AI,” below.

The post How Visual AI Accelerates Release Velocity appeared first on AI-Powered End-to-End Testing | Applitools.

Applitools was invited to share our expertise in applying AI to quality engineering, and we’re honored to be co-authors of this comprehensive report by Sogeti.

Sogeti has just released the first section of their State of AI applied to Quality Engineering 2021-22 report, including two chapters co-authored by Applitools. The report is a detailed examination of the current state of artificial intelligence in the field of quality engineering. It centers around a key question – how can AI make our quality validation smarter? In the words of the executive introduction:

This report aims to assist you in understanding the potential of AI and how it can help improve the quality, velocity, and efficiency of your quality engineering activities.

As one of the pioneers in the application of AI to quality engineering through Visual AI, we were honored to be asked to participate in this report and share our expertise. We co-authored several chapters, including two that have been released today in the first section.

Chapter 1: Quality Engineering and recent trends

In this chapter, you’ll get an overview of the business and technical environment which has led us to where we are today and the current need for assistance from AI. It discusses the shortcomings of traditional testing practices and the emergence of modern quality engineering. What does a successful Quality Engineer do today? What are the challenges faced? What is the future of quality engineering, and what role could AI play in that? Check out this opening chapter for a great introduction into the topic of AI in QE. 

Chapter 2: Getting started with AI

This chapter digs a little deeper into how you can get started in your journey with AI. Moshe starts by relating a personal story about a customer service experience that left him frustrated. How can we use AI to eliminate waste from our days and spend more time on quality engineering and address issues before they impact end users? The chapter goes on to cover the difference between routine and error-prone tasks and opens up the discussion of how we can optimize each type. You’ll also get some great info on how to define AI, understand possible use cases, and thoroughly research your options. Head over to the second chapter to read more.

In chapter 3 and chapter 4, you can explore further with technical deep dives into machine learning and deep learning. 

Check out the Full Report

Sogeti has put together a strong report on this important topic and we’re excited to share the opening section with you today. Starting in September, you can expect to find new sections released bi-weekly, including another chapter from Applitools that will be out in the coming months. To learn more, check out the full “State of AI applied to Quality Engineering 2021-22” report. 

The post The “State of AI applied to Quality Engineering 2021-2022” Report Released appeared first on AI-Powered End-to-End Testing | Applitools.