Revolutionizing UI and Localization Testing with LLMs: Addressing Key Challenges Beyond Classic Automation

At SandLogic, our vision for Generative AI (GenAI) and Foundation Models is centered on empowering businesses to harness the transformative potential of AI-driven insights across multiple domains. We aim to create highly specialized, domain-specific models derived from our foundation models that go beyond general-purpose applications, offering unparalleled accuracy, performance, and adaptability. Our models are designed to seamlessly integrate into enterprise workflows, enabling organizations to scale AI capabilities with security, speed, and precision.

Recently, we’ve successfully harnessed GenAI and our Foundation Models to implement highly efficient testing processes, solving complex challenges in modern UI and localization. Our tailored models have already delivered significant results, helping our customer achieve precise accuracy and consistency across platforms while enhancing global user experiences.

As software applications evolve and expand across global markets, ensuring a consistent, high-quality user experience becomes increasingly complex. Classic test automation frameworks often struggle to keep up with the demands of modern, scalable applications. Factors such as cross-platform compatibility, dynamic web designs, visual validation, AI-driven UI changes, and localization go beyond the capabilities of traditional automation tools.

In this article, we explore how leveraging Large Language Models (LLMs) can address these challenges, driving efficiency, accuracy, and cost savings in UI regression and localization testing.

Key Challenges in Modern UI Testing: Where Classic Automation Falls Short

Cross-Platform Compatibility and Dynamic UI Design

Applications today must operate seamlessly across a wide range of platforms, from mobile devices to web browsers, and desktop environments. Each platform may render UI elements differently, and dynamic or responsive designs further complicate the testing process. Classic automation frameworks often rely on static locators (e.g., XPath) to find elements, which break when UI layouts change across different screen sizes and devices.
The Solution: By integrating LLMs into UI testing, we can create a more flexible approach that adapts to dynamic UI elements. LLMs can intelligently identify and validate elements across different platforms, ensuring that buttons, text fields, and icons are properly displayed, regardless of the device or screen size. This allows for reliable validation of responsive designs and ensures UI consistency across all platforms.

Visual Validation and Aesthetic Consistency

While functional testing is critical, visual consistency is just as important to ensure a cohesive user experience. Classic test automation frameworks tend to overlook visual validation, focusing solely on functional elements without assessing aesthetic aspects such as proper alignment, color accuracy, or font consistency.
The Solution: LLMs can be trained to detect and evaluate visual discrepancies in UI elements. By comparing screenshots from different versions of the app, the LLMs can identify subtle changes in the appearance of elements, flagging issues such as misaligned buttons, incorrect font sizes, or color mismatches. This approach ensures a pixel-perfect UI that adheres to design guidelines and enhances user experience.

AI/ML-Driven UI Changes

Many modern applications now incorporate AI/ML-driven elements, such as personalized recommendations, dynamic content, or predictive UI adjustments. Classic automation frameworks, which rely on deterministic behavior, are often ill-equipped to validate these AI-driven changes. Traditional testing may fail to account for non-deterministic outcomes, leading to incomplete coverage.
The Solution: With LLM integration, test automation can handle AI-driven UI changes more effectively. LLMs can assess the contextual relevance of dynamically generated content, such as personalized text or recommendations, ensuring that these elements are properly displayed and contextually accurate. This capability helps organizations manage the complexity of AI-driven applications while maintaining a high standard of UI testing.

Localization and Internationalization

Beyond basic text translation, localization requires comprehensive validation of various aspects, including text direction (left-to-right or right-to-left), cultural appropriateness, and proper display of elements such as dates, currency symbols, and number formats. Classic test automation tools struggle to validate these non-text localization aspects, often resulting in UI bugs or poorly formatted content for different regions.
The Solution: LLMs can support comprehensive localization testing by validating localized texts and ensuring proper UI display for different languages and regions. Whether testing for right-to-left languages like Arabic or ensuring that currency symbols and date formats are correct for specific geographies, LLMs can intelligently detect and validate these elements. This level of testing ensures that applications provide a consistent, localized experience for users worldwide.

The Impact: Improving Productivity, Reducing Costs, and Ensuring Global Scalability

By addressing these critical areas, the integration of LLMs into the testing process delivers far-reaching benefits for organizations operating on a global scale:

Improved Efficiency: Automating cross-platform testing and visual validation with LLMs significantly reduces the time spent on manual testing, allowing organizations to release updates faster while ensuring that no UI defects slip through the cracks.

Cost Savings: The reduction in manual effort not only increases speed but also lowers QA costs by allowing teams to focus on strategic initiatives instead of repetitive validation tasks.

Enhanced User Experience: With visual validation, AI/ML-aware testing, and comprehensive localization capabilities, companies can deliver a seamless, polished UI experience for users across all platforms and regions.

Global Scalability: As applications expand across geographies, LLM-driven automation ensures that UI and localization testing scales effortlessly, adapting to new languages, platforms, and AI-driven behaviors without additional overhead.

Example Inference Workflow for UI Regression Testing

For the sake of this article, we used WhatsApp screens as its widely used and adopted across globe.

Original Ground Truth Image : with QR code icon

Defected Image : Without QR code icon

Ground Truth Image with Bounding Box and Labels:

Defected Image with Bounding Box and Labels:

The integration of LLMs and GenAI into UI and localization testing is no longer just an enhancement but a necessity for companies aiming to scale globally. By addressing the gaps that traditional automation frameworks can’t cover—like cross-platform consistency, dynamic UI changes, and nuanced localization—businesses can streamline their testing processes and improve overall product quality. At SandLogic, we are committed to driving this shift, empowering companies to meet the demands of today’s digital landscape while ensuring seamless, high-quality user experiences across the globe.