Practical insights for teams building better software.
The conversation about whether traditional QA is broken has moved from conference hallways to public timelines. Here is what the noise is actually pointing at — and what engineering teams should do about it.
SmartBear just wired its testing platform into Claude Code, GitHub Copilot, and AWS AI tools. The move exposes a problem that's been quietly accumulating: AI-generated code is outpacing the ability of teams to verify it.
GitLab's 2026 AI Accountability Report confirms what many engineering leaders already suspect: AI accelerates coding but the overall delivery pipeline hasn't kept pace. Here's what the data actually says and what to do about it.
AI tools are changing more than how engineers write code — they're reshaping how engineers think about their value, their expertise, and their place on a team. Here's what's actually happening and what engineering leaders should do about it.
Vibe coding ships fast. Spec-driven development ships reliably. The question isn't which one is better — it's understanding when each approach breaks down, and what enterprise teams need to prove ROI on AI-assisted delivery.
Software testing isn't just a quality gate — it's a business risk management discipline. Here's how each major testing type maps to real commercial outcomes, and how to decide which ones deserve your investment.
Generative AI has moved from a novelty in QA tooling to a genuine shift in how teams write, maintain, and run tests. Here is what the 2026 landscape actually looks like — and what engineering leaders should do about it.