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OpenText UFT One Enterprise Release Update: Faster UI Automation, AI Testing, and CI CD Reliability with Merito
OpenTextDecember 26, 2025By Chris Carpenter
OpenText UFT One Enterprise Release Update: Faster UI Automation, AI Testing, and CI CD Reliability with Merito
OpenText UFT One’s latest release strengthens enterprise automation with faster UI testing, smarter AI-based web and mobile validation, improved CI CD integration, and more reliable remote and data-driven execution.
INTRODUCTION: HARDENING ENTERPRISE TEST AUTOMATION AT SCALE This OpenText UFT One release focuses on what matters most to enterprise SDLC leaders: faster and more stable automation at scale, practical AI-based testing for real-world web and mobile applications, and tighter integration with CI CD pipelines and remote execution environments. The goal is not novelty, but resilience. These enhancements help large organizations increase release frequency while controlling risk, reducing automation noise, and strengthening governance across delivery pipelines.
ENHANCED UIA PRO ADD-IN FOR FASTER AND MORE STABLE UI AUTOMATION The enhanced UIA Pro Add-in, available as an opt-in Beta, delivers significant improvements in performance, stability, and defect fixes for desktop and rich-client automation. Once activated alongside the standard UIA Pro Add-in, teams benefit from faster execution and more predictable object handling.
For enterprises running thousands of UI tests, improved execution speed shortens regression cycles and directly impacts release lead time. Reduced flakiness improves the credibility of quality metrics used by engineering leadership. Automation engineers spend less time rerunning failed tests and more time expanding coverage and improving frameworks, while developers experience tighter feedback loops during local test runs.
DEEPER TEST OBJECT TREE NAVIGATION FOR COMPLEX ENTERPRISE UIS The enhanced UIA Pro Add-In introduces advanced navigation of the test object tree, allowing retrieval of parent and child objects across all levels with control over depth and volume. This is especially valuable for enterprise applications with nested grids, composite widgets, and custom controls.
Improved navigation increases automation coverage of complex workflows such as approvals, claims, and order processing. It also enables cleaner page object and component models, lowering long-term maintenance costs. Day to day, SDETs can build more expressive and resilient selectors that adapt better to UI changes without re-recording entire flows.
ASSISTIVE PROPERTIES FOR STRONGER OBJECT IDENTIFICATION Assistive properties enhance how test object descriptions are built, moving beyond basic attributes to improve identification stability. This reduces false failures in environments where UI labels, layouts, and themes evolve frequently.
From a risk perspective, more reliable object identification leads to consistent automation results across teams and applications, strengthening trust in regression outcomes. For practitioners, this simplifies test authoring, shortens onboarding for new engineers, and significantly reduces repetitive maintenance caused by minor UI updates.
CONSISTENT METHODS AND PROPERTIES ACROSS UI OBJECTS UI objects retrieved using methods such as GetCell now expose a consistent set of common methods and properties. This uniform behavior simplifies framework design and supports standardization across large automation portfolios.
Central QA and tools teams benefit from reduced custom branching and easier governance of shared libraries. Engineers gain reusable utilities for logging, waits, and validations, making debugging faster and reducing cognitive load during failure analysis.
HYBRID TEXT RECOGNITION FOR AI-BASED WEB TESTING Hybrid text recognition combines AI OCR results with text extracted directly from web elements, improving accuracy on modern web applications. It can be enabled globally or controlled programmatically using AIRunSettingsOCR.
This enhancement is critical for dynamic, canvas-heavy, or custom-rendered UIs common in banking, trading, and enterprise dashboards. More reliable AI-based testing reduces false negatives and increases leadership confidence in AI testing as part of a risk-based testing strategy. QA teams benefit from fewer workarounds and faster script development, with flexibility to tune behavior per application or run.
IMPROVED AI-BASED SCROLLING FOR MOBILE TESTING The AIUtil.ScrollOnObject method now supports accurate scrolling in all directions on mobile devices. This enables realistic testing of horizontal lists, carousels, and responsive layouts.
Enterprises gain better coverage of mobile-first features without introducing fragile coordinate-based scripts. Mobile testers can script natural gestures across devices and form factors, reducing maintenance and stabilizing mobile regression suites used in release gates.
FLEXIBLE ABBYY TRAINED PATTERN CONTROL FOR OCR New TextUtil methods allow teams to enable, disable, or modify ABBYY trained pattern usage during execution. This is especially valuable in document-heavy and regulated environments.
Pattern-based OCR improves recognition of structured documents such as invoices, claims, and policy forms. Programmatic control allows enterprises to support multiple document types and locales without manual reconfiguration. Test teams can incrementally refine patterns and selectively apply them, improving accuracy without disrupting existing suites.
JSON TESTING SUPPORT FOR ANONYMOUS ARRAYS JsonUtil now supports retrieval of anonymous arrays returned at the root of JSON payloads. This simplifies validation of modern APIs and microservices that underpin digital channels.
Stronger JSON handling improves API contract validation and enables tighter linkage between UI actions and backend responses. Testers can iterate over arrays directly without custom parsing, improving clarity, maintainability, and end-to-end traceability across layers.
RELIABLE REMOTE TEST EXECUTION ON LOCKED OR DISCONNECTED MACHINES Remote test execution can now continue under the same user session even if the machine becomes locked or disconnected. This behavior is configurable through options or the automation object model.
For enterprises using shared labs or remote servers, this reduces failures caused by session timeouts and infrastructure quirks. Maintaining a single user context also supports security and audit requirements. Teams see fewer unexplained failures, while operations can enforce lock policies without breaking automation.
CORRECTED GETCURRENTROW BEHAVIOR FOR DATA-DRIVEN TESTING After importing Excel data using DataTable.Import, GetCurrentRow now returns the actual row index instead of always returning one. While some legacy tests may need adjustment, this change improves predictability.
Accurate row indexing strengthens data-driven testing, improves logging and defect traceability, and supports audits where test evidence must map clearly to input data. Engineers gain faster diagnosis of data-specific failures and greater confidence in reusable data-driven frameworks.
CI CD ENHANCEMENTS FOR AZURE DEVOPS INTEGRATION The Azure DevOps extension version 25.2 and later introduces improvements when running tests from the file system. JUnit reports can now be generated even when all tests pass, and ALM Lab Management runs can be explicitly stopped as part of pipeline execution.
These changes improve quality traceability in CI CD dashboards and strengthen governance over shared lab resources. Teams no longer need custom scripts for green builds, and release managers gain cleaner control of test lifecycle stages within automated pipelines.
WHY THIS OPENTEXT UFT ONE RELEASE MATTERS FOR ENTERPRISE PROGRAMS Together, these updates strengthen UFT One as an enterprise-grade automation platform. Organizations benefit from more stable high-volume automation, AI-based testing that behaves predictably, and cleaner CI CD and remote execution integration. Leaders gain higher-quality signals for release decisions, lower automation maintenance overhead, and stronger alignment with compliance and audit expectations.
HOW MERITO HELPS ENTERPRISES TURN FEATURES INTO OUTCOMES Merito works with enterprises to translate these UFT One capabilities into measurable SDLC improvements. We help teams modernize UI and AI automation strategies, stabilize regression pipelines, integrate UFT One with Azure DevOps and ALM Lab Management, and define standards that scale across portfolios. The result is faster releases with lower risk and more credible quality metrics.
FREQUENTLY ASKED QUESTIONS
How should enterprises adopt the enhanced UIA Pro Add-In? Enterprises should roll it out through controlled pilots, refactor fragile object models using assistive properties, and standardize new navigation patterns to reduce flakiness and maintenance.
How do AI testing improvements help real-world web and mobile apps? Hybrid OCR and improved scrolling increase reliability on dynamic interfaces, enabling broader coverage of complex user journeys without brittle scripts.
What is the value of ABBYY trained patterns in enterprise testing? ABBYY patterns improve OCR accuracy on structured documents and regulated forms, supporting reliable validation of business-critical data.
How do JSON and data table updates improve API testing? Anonymous array support and corrected row indexing simplify API validation and strengthen traceability between UI actions and backend responses.
Why are the Azure DevOps CI CD changes important? Consistent JUnit reporting and explicit lab run control improve pipeline governance, reporting accuracy, and operational efficiency.
How does the new remote execution behavior reduce risk? Maintaining the same user session across disconnects stabilizes long-running test suites and supports security and audit requirements.
