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Automation

One Script, Multiple Platforms: Why Cross-Platform Testing Still Fails

Many automation tools promote a simple idea. One script that can run across multiple platforms. In theory, this approach reduces duplication, simplifies maintenance, and improves consistency across environments. In practice, achieving that level of consistency remains a challenge for most teams. The Promise of One Script Across Multiple Platforms Cross-platform testing has become essential as

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AI Learning and Artificial Intelligence Concept. Business, modern technology, internet and networking concept.
AI

AI Can Generate Tests. But Can They Be Repeated?

The rise of AI-driven tools has transformed how quickly teams can create test scripts. Functional tests can now be generated in minutes using cloud-based platforms, low-code tools, or AI assistants. This shift has redefined expectations around speed and accessibility in test automation. But speed is only part of the equation. A generated test has limited

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Accounting

Why UI Testing Is Becoming the Last Line of Defence in Cybersecurity

When organisations think about cybersecurity, the focus is typically on infrastructure, encryption, and code-level vulnerabilities. Firewalls are strengthened, penetration tests are scheduled, and dependencies are scrutinised. But there is a critical layer that is often overlooked. The user interface. The UI is where systems, users, and environments intersect. It is the point at which complexity

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UI issues in production not detected by traditional automation testing
Automation

Production Issues Not Covered by Traditional UI Automation

High test coverage is often used as a proxy for confidence in software quality. Test suites pass, pipelines remain stable, and releases move forward without issue. However, many production issues don’t originate from gaps in functional validation. Instead, they arise from differences between how systems are tested and how they are actually experienced by users.

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UX failures in production impacting business performance without triggering system errors
UI testing

The Business Impact of UX Failures in Production

UX failures in production rarely appear as critical incidents, yet they are often where the most significant business impact is introduced. Most software issues are measured in system failures. Errors are logged, incidents are raised, and when systems stop working, teams respond quickly. However, many of the most costly problems in modern applications do not

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Money and cost implications of AI. A man holding an iPad with a graph hovering above it.
AI

The Hidden Cost of Testing AI-Generated Software Without UI Validation

Code can now be generated, modified and deployed faster than ever before. Development cycles are shorter, iteration is constant, and testing pipelines are expected to keep pace. On the surface, everything appears under control.Test suites pass. APIs respond correctly. Automation reports are green. But users still encounter problems. Buttons don’t appear. Totals display incorrectly. Layouts

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Testing

When Self-Healing Masks UI Regression

Self-healing automation frameworks were introduced to address a persistent challenge in test automation: brittle selectors. In large test suites, even minor interface refactoring can cause cascading failures. Identifiers change, attributes shift and structural hierarchies are reorganised without necessarily affecting functional behaviour. Traditional frameworks interpret these structural adjustments as defects, creating maintenance overhead that grows over

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Visual Test Automation, AI face and code looking at validation. Blue and orange colour.
AI

Why Visual Test Automation Is the Missing Layer in AI-Augmented QA

Artificial intelligence is no longer experimental within software delivery. It’s embedded directly into development pipelines. Teams are generating UI components from prompts, refactoring service layers automatically and producing automation scripts with minimal manual effort. As a result, many organisations now describe their approach as AI-augmented QA. However, most augmentation is occurring at the authoring level.

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AI-Generated Code Defects face made of building block. orange and blue grey
AI

AI-Generated Code Defects: How to Test and Validate AI-Built Software

AI-Generated Code Has a Defect Problem. Here’s How to Test It Properly Artificial intelligence can now generate working software in seconds. From UI components and API integrations to full test scripts, AI coding assistants are accelerating development at a pace few organisations could have imagined even three years ago. However, speed is not the same

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