This article takes the risk statements from the previous one and turns them into a ranked list you can act on. Each risk gets a score: exposure = likelihood × impact. The result is a prioritized table with traceable rationale, and explicit decisions about what not to invest i...

This post is devoted to identifying and quantifying risks before deciding what to test. That means collecting stakeholder fears, converting them into explicit risk statements, and estimating their impact. The output is a prioritized list of risks, not a test plan.

In most of the companies I worked for, testing efforts usually started quite simply: someone opens a ticket...

There's a defining moment in every engineering organization when the QA team's work stops being a checkpoint at the end of the sprint and starts being the infrastructure the whole company runs on.

You're not just catching bugs anymore. You're designing the quality systems that let your organization ship with confidence, maintain velocity as the codebase grows, and give ...

Testing is an economic investment, not a cost to minimize. The question "how much testing do we need?" is broken: it should be "which risks justify which investments?". This post introduces the Cost of Quality framework, portfolio thinking, and a four-step process for making testing decisions grounded in risk.

I read arguments and battles on "what's the proper coverage"...

If you work in tech, or a tech adjacent industry, you’ll know that AI has accelerated everything, including software development. Engineers now write, refactor, and ship code faster than ever using tools like Claude, Cursor, and Copilot.

Development speed has increased, but testing workflows often struggle to keep up. Engineering teams face more features, more commits, ...