· Copy Link In modern product delivery, user interfaces evolve rapidly: new components, updated layouts, and refreshed workflows. While these changes drive innovation, they also increase the risk of visual regressions that escape notice and degrade the user experience. Traditional visual testing methods (pixel-based comparisons, region checks, or manual screenshot reviews) struggle to keep pace. They generate false positives, require human interpretation, and often lack consistency.
To address this challenge, Elix is launching a major innovation : AI-Powered A/B Testing for Visual Regression Analysis. This new feature empowers teams to validate UI changes with unprecedented accuracy, automation, and clarity, helping ensure every release is visually stable and user-ready.
The Problem With Traditional Visual Regression Testing
Visual regressions are among the most visible defects in software. A misaligned button, shifted form field, missing icon, or unexpected color change can break the user experience instantly.
Yet most teams still rely on outdated or manual processes:
- Pixel-wise comparisons trigger false alerts due to minor rendering differences.
- Region-based checks overlook small but meaningful UI shifts.
- Manual reviews are slow, subjective, and resource heavy.
➡️ Result : Visual issues are detected late, require costly rework, and damage user trust.
Elix’s Answer: AI-Powered Interface Comparison
The new Elix A/B Testing feature transforms how teams validate their product’s visual health. Instead of relying on rigid pixel comparison, Elix uses object detection, similarity scoring, and graph-based analysis to intelligently understand your UI, just like a human reviewer, but faster and more accurate.
The result is a fully automated workflow that provides:
✅ Higher detection accuracy
✅ Significant reduction in false positives
✅ Clear, structured reporting for decision-makers
✅ A baseline that evolves with your product
AI A/B Testing with Elix : How it Works
1. New Project Creation
The process starts with a simple setup :
- Enter the project name
- Provide your web application URL
- Add the necessary credentials
Elix then prepares the environment to analyze all screen interfaces of your software application.
2. Baseline Creation: Scraping All Interfaces
Once the project is created, Elix performs a complete scan to generate a visual baseline of your application:
- All pages and UI states are captured
- Each interface is processed using AI-based object detection
- UI components (buttons, labels, forms, menus…) are identified
- A structural graph of the interface is created
➡️ This baseline becomes the reference point for all future A/B tests.
3. Running an A/B Test
Whenever you want to compare the current version of your software application with the baseline, you simply trigger an A/B test.
Elix does the rest automatically:
✅ Scrapes the latest version
✅ Detects all UI components
✅ Matches the new version with the baseline using weighted similarity
✅ Classifies changes: added, removed, repositioned, resized, or visually modified
Similarity scoring uses a sophisticated combination of factors :
- 30% Visual similarity (appearance)
- 30% Text similarity (labels, wording)
- 20% Position similarity
- 20% Dimension similarity
- + Class label matching
➡️ Only changes exceeding an 80% significance threshold are reported.
4. Automated PDF Report Generation
At the end of each test cycle, Elix generates a fully structured, human-friendly PDF report that includes:
- Screenshots of each page
- Highlighted differences between baseline and version
- Component-level analysis (added, removed, changed, moved…)
- A summary table of key modifications
- Severity indicators for quick decision-making
➡️ This report can be shared with QA teams, developers, clients, or stakeholders to accelerate feedback cycles.
The Technology Behind the Feature
1. AI Object Detection Engine
The system is powered by a custom-trained YOLO 11s model, selected after evaluating multiple algorithms through:
- Dataset-specific benchmarking
- Performance comparisons
- Class accuracy validation
The model underwent fine-tuning to optimize detection of real-world UI components.
2. Graph-Based Change Detection
Every UI is represented as a graph of components characterized by Nodes and Edges.
This graph is the key to understanding the UI layout holistically rather than pixel-by-pixel, leading to smarter changelog generation.
A Feature Designed for Real Product Teams
Elix’s AI A/B Testing feature is built with real workflows in mind:
- QA teams can validate releases faster
- Product owners receive clear, unbiased reports
- Developers see exactly what changed, without digging manually
- Clients gain transparency and trust in delivery quality
Whether you’re maintaining a fast-moving web app or overseeing a large-scale product, this feature keeps everyone aligned.
The new AI A/B Testing feature marks a significant milestone for the Elix platform. By combining deep-learning models with graph-based comparison, Elix delivers a new level of accuracy, automation, and reliability in visual regression testing. Teams can finally move beyond pixel comparisons & manual reviews, and embrace an intelligent, scalable, and transparent way to validate UI changes.
This is more than a testing tool; it’s a breakthrough in how software teams ensure quality at scale.
Elix NewsWe’re thrilled to announce the official launch of Elix, our all-in-one business and project management suite designed to transform the way teams work and organizations operate. Developed by think tank, a trusted leader in digital transformation across Europe, Africa, and the Middle East, Elix is more than a tool—it’s an ecosystem.
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