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
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
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.
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
We’re all familiar with “Spaghetti Code”: source code that is unstructured, difficult to maintain, and tangled like a bowl of pasta. However, a less discussed but equally damaging phenomenon is plaguing Large-Scale Test Automation and Robotic Process Automation (RPA). We call it “Spaghetti Testing.” Spaghetti Testing occurs when automation suites grow without a unified architecture.
Software development has fundamentally changed. We’re moving past the era of writing precise syntax line by line and entering the age of “vibe coding” – where developers act as architects of intent rather than bricklayers of code. You describe the outcome, the “vibe”, and an AI model handles the implementation. It’s a productivity shift that
Ownership used to feel clear. QA teams planned it, executed it and carried responsibility for quality. Other roles contributed, but accountability was understood. Today, that clarity has shifted. Automation is everywhere, delivery cycles are shorter and responsibility for quality now spans QA, development, operations and the business itself. Testing happens across pipelines, platforms and processes,
Over the past year, AI has dominated conversations across the software industry. Predictions of fully autonomous QA, instant test generation and self-healing automation filled headlines, while fears around job displacement added fuel to the hype. As we move further into 2025, that excitement has begun to settle. Organisations are evaluating AI more realistically and with
In modern software testing, especially for QA professionals working in regulated sectors or high-precision environments, automation tools must go beyond basic object recognition. For QA testers responsible for validating complex systems – such as CAD, simulation, media, or engineering platforms – many frameworks run into technical limits as graphical accuracy rises and user demands increase.
This guide explores the leading image-based test automation platforms, including T-Plan’s business-ready solution and others such as Ranorex, to help QA teams select the most suitable option. In today’s technology landscape, quality assurance professionals face growing complexity when validating visually rich software and automating high-volume workflows. Traditional testing approaches often struggle to keep pace with
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