Revolutionizing Software Testing with Visual AI

Applitools has been highlighted in a recent Enterprise Management Associates (EMA) research report titled “Disrupting the Economics of Software Testing Through AI.” The study reveals that Visual AI has emerged as the most impactful application of AI technology in the field of software testing, offering transformative benefits for development and quality assurance teams.

The State of Software Testing

Traditional software testing tools and methods are struggling to keep pace with the demands of modern software development. The report identifies key challenges facing software engineering and DevOps teams, including:

• Escalating quality control costs.
• Increased release velocity requiring faster, more efficient testing.
• Growing complexity due to the diversity of smart devices, operating systems, and programming languages.

Organizations reliant on conventional testing approaches are falling behind competitors who adopt AI-driven solutions.

Visual AI’s Transformative Impact

EMA’s research examined six real-world scenarios in traditional test automation. It found that AI and machine learning (ML) technologies—particularly Visual AI—provide significant return on investment (ROI) by addressing common bottlenecks and inefficiencies in software testing.

According to Torsten Volk, EMA’s Managing Research Director:

“The ability to accelerate software delivery at lower costs while maintaining quality has become essential for competitive advantage. AI-based test automation technologies are not only delivering ROI today but also have the potential to resolve critical bottlenecks that hinder modern software delivery.”

Key Insights from the EMA Report

The study highlights several critical trends and data points:

1. Surge in Test Automation Interest: Nearly 100% increase in test automation-related questions on StackOverflow in the past year.
2. Smartphone Proliferation: The annual release of smartphones has grown at a 30% CAGR (Compound Annual Growth Rate) since 2017 for Android alone, intensifying pressure on quality assurance teams.
3. Cloud Complexity: The number of cloud-native and microservices-based applications has increased by 225% since 2015, leading to greater software delivery challenges.
4. Increased Testing Workloads: As technology complexity grows, the number of monthly code releases multiplies the tasks required of test engineers, resulting in exponentially higher testing demands.
5. Fragmented Test Automation Frameworks: Many organizations rely on inconsistent and disjointed testing frameworks, forcing them to choose between increased costs or higher risk in agile development.

The Case for AI-Driven Software Testing

AI disrupts the traditional linear relationship between the number of software releases and the associated testing workload. Key advantages include:

• Reduced testing overhead: AI automates repetitive tasks and scales effortlessly with increased release velocity.
• Enhanced quality assurance: AI-driven tools like Visual AI ensure consistency across platforms and devices.
• Faster innovation: By freeing teams from manual testing burdens, developers can focus on delivering greater customer value.

This approach ultimately breaks the “vicious rectangle” of competing priorities: faster releases, optimized spending, better quality, and more time for innovation.

The Path Forward

The EMA report emphasizes the urgent need for organizations to adopt AI-powered test automation solutions to remain competitive. Visual AI, with its proven ability to streamline software delivery and enhance quality assurance, is leading the charge in transforming the economics of software testing.

For businesses aiming to scale their testing efforts while reducing costs and improving product quality, embracing Visual AI is not just an option—it’s a necessity.