Insights

Thinking on the future of software quality.

Perspectives on where the QA industry is heading, what modern engineering teams are getting wrong, and what the next standard looks like.

Industry6 min read

The Third Era of Software Quality Has Already Begun

Manual testing gave us process. Automation gave us speed. Neither gave us confidence. Here is what the third era gets right.

Every major shift in software engineering follows the same arc. A model emerges that solves the problems of its time. It scales well, becomes standard practice, and eventually becomes the ceiling. The teams that recognise the ceiling early are the ones that define what comes next.

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Engineering5 min read

Why High Test Coverage Does Not Mean High Confidence

Coverage metrics are easy to measure and hard to trust. The gap between what they promise and what they deliver is where most production incidents live.

There is a moment familiar to most engineering leaders. A major incident occurs. The post-mortem begins. Someone checks the test coverage for the affected area. It is 87%. The room goes quiet. This is not an edge case. It is the rule.

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Industry7 min read

AI-Generated Code Changes Everything About Software Quality

When the volume of code produced exceeds what human review can process, the quality model must change. Most engineering teams are not ready for this.

In 2024, GitHub reported that more than 40% of code written in Copilot-enabled environments was AI-generated. That number is rising. More code, written faster, by a process that does not understand the system it is modifying.

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Engineering5 min read

The Real Cost of Treating QA as a Phase

When quality is a gate at the end of delivery, the entire engineering organisation pays the price. The numbers are larger than most teams realise.

Quality assurance as a phase made sense when software was built in long cycles with clear handoff points. Modern delivery does not work that way. When a team ships multiple times a day, a quality gate at the end is not a safety mechanism.

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Leadership6 min read

What Engineering Leaders Get Wrong About Scaling Quality

Hiring more QA engineers is the most common response to a quality problem. It is also the least effective one at scale.

When a quality problem surfaces in a fast-growing engineering organisation, the instinct is to staff the solution. More testers, more reviewers, more process. It is a reasonable response. It is also a signal that the organisation is scaling the wrong model.

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Vision4 min read

Autonomous Quality Is Not a Feature. It Is Infrastructure.

The engineering teams that will define the next decade are already treating quality intelligence as foundational, not optional.

There is a difference between a team that has quality tooling and a team that has quality infrastructure. Tooling is selected, configured, and maintained. Infrastructure is foundational, always present, and trusted by default.

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