Perforce Software has announced the introduction of AI Validation, a new feature within its Perfecto continuous testing platform for web and mobile applications. The new capability aims to simplify software testing by using AI to validate applications visually and contextually, adapting dynamically to changes without requiring any human intervention.
About Perforce:
Founded in 1995, Perforce Software provides scalable development and DevOps solutions designed for intelligent testing, risk management, dynamic development, and smooth teamwork.Their product package, serves sectors like gaming, automotive, and financial services, includes version control, application lifecycle management, agile project management, and static analysis tools. Among the notable customers are Bank of America, Nintendo, Pixar, Apple, Samsung, and Honda.
AI Validation: A New Approach to Testing
Traditional automated testing often relies on object locators and AI co-pilots that generate multiple scripts. These methods can become unreliable, particularly when minor UI changes occur, leading to frequent maintenance and test failures. Perfecto’s AI Validation eliminates this dependency by using natural language prompts rather than object-based or code-driven approaches. This makes test automation more stable, adaptable, and accessible to a broader range of users, reducing the need for specialized scripting knowledge.
According to Stephen Feloney, Vice President of Product Management at Perforce, many existing AI-driven testing solutions focus on generating more test scripts rather than addressing the core challenge of ensuring applications work as expected. Feloney stated that AI Validation provides testers with a solution that directly verifies what appears on the screen, ensuring accurate test results without the burden of frequent script updates.
The AI Validation feature integrates seamlessly with CI/CD workflows, allowing teams to perform continuous and scalable testing across multiple platforms. This is particularly beneficial for enterprise teams managing complex development environments, where frequent updates and cross-platform consistency are essential.
One of the key advantages of AI Validation is its ability to handle application elements, such as charts, dashboards, and interactive visual components. Unlike traditional OCR-based solutions, which focus on basic text recognition, AI Validation interprets the meaning behind graphical elements to ensure the correct user experience is delivered.
For example, whether validating a bar chart, trending graph, or calendar view, AI Validation ensures that visual elements display accurate information, reducing the risk of overlooked UI discrepancies.
Real-World Results from Early Adopters
Several companies have already begun using AI Validation, reporting tangible improvements in their testing efficiency and software quality.
- Midwest Tape, a media distributor, incorporated AI Validation into its testing strategy and reduced overall testing time by 20%. The company noted that traditional object locators were often unreliable, but AI-driven validation significantly improved test stability.
- Servus Credit Union, a financial institution, highlighted the potential for AI Validation to streamline test case creation. The company anticipates that natural language-driven test prompts could reduce the need for both manual testing and automation script development.
One of the major pain points in automated testing is the continuous need to update scripts and adjust object locators whenever an application undergoes UI changes. Perfecto’s AI Validation addresses this challenge by eliminating reliance on fragile locators and script-based updates.
By making test automation more adaptive and accessible, Perforce aims to expand test coverage beyond technical specialists, allowing broader team participation. With AI Validation, organizations can maintain software quality at scale while reducing time spent on test maintenance.
As AI-driven testing continues to evolve, Perforce’s AI Validation represents a shift toward autonomous testing, reducing manual intervention and enabling faster, more reliable software releases.
