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
In the world of mission-critical software, where failure is not merely a bug but a potential catastrophe, the greatest obstacle to automation is not technology: it is security. For sectors such as aerospace, defence, and medtech, we face a fundamental contradiction known as the Air-Gap Paradox. It is the architectural reality that the systems requiring
The conversation around AI in software development has rapidly evolved from simple code generation to the far more ambitious concept of Agentic AI. We are moving towards systems that do not just follow instructions but reason, plan, and execute tasks autonomously. This paradigm shift holds immense promise for quality assurance, yet a critical chasm prevents
For many years, testing and robotic process automation (RPA) were treated as separate disciplines. Testing focused on validating applications and managing risk. RPA focused on automating repetitive business processes to improve efficiency. Each had its own tools, teams and ways of working. That separation once made sense. Today, it is becoming increasingly difficult to justify. As organisations
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,
UI automation has evolved rapidly over the last year. The rise of AI assisted testing, the continued shift toward hybrid application architectures and growing pressure for cross platform coverage have all reshaped the automation landscape. With so much change, now is the ideal time to revisit the question many organisations are asking in 2026. What
AI has rapidly shaped the conversation around software testing in 2025, but many teams are still searching for practical ways to use it without losing control, accuracy or repeatability. That is why we are excited to introduce T-Plan AI, a new capability that brings the speed of AI-powered code generation together with the reliability of
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
Precision and creativity are often viewed as opposites, yet the most advanced industries today depend on both. Whether in engineering, architecture, gaming, healthcare, or defence, success increasingly relies on the balance between technical accuracy and visual excellence. The software that supports these image-heavy and design-led environments is complex and highly interactive, demanding a new kind
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