Matt Calder

I still remember my early days sending vague bug titles and getting zero responses. Over 10 years, I figured out what actually makes QA work. Let me share my guide + tools that helped me most 👇 #SoftwareTesting #TestAutomation 1/10

For teams building that discipline, Tuskr gives you the test management foundation that makes it executable. Test plans, execution tracking, and coverage reporting that apply a human quality standard to AI-generated output , the layer that most teams are still missing.

That discipline requires structure. Defined test cases mapped to business requirements, not generated by the same model that wrote the code. Repeatable execution cycles. Coverage visibility that tells the team what has actually been verified before anything ships.

46% of code on GitHub is now AI-generated. AI-assisted pull requests produce 1.7x more issues than human-written ones. A 90% increase in AI adoption correlated with a 154% increase in PR size and a 91% increase in code review time. Velocity went up. So did everything downstream.

For product and delivery leaders serious about real-time visibility, Celoxis provides the connected portfolio layer that makes AI insights actionable. Milestone tracking, resource workload, and dependency data in one place so AI signals arrive when they can still change outcomes.

This is the actual gap AI is supposed to close: the time between a delivery risk emerging and a decision-maker knowing about it. For most organisations that gap is still measured in days. The teams closing it are building the portfolio infrastructure AI needs to work from.

AI in project management is solving the wrong problem. Most teams are using it to produce status reports faster. The actual problem is that by the time any report reaches a leader, the moment to intervene has already passed.

Audit your SDLC: How long does it take to trace a production bug back to the original requirement and test gap? If the answer is "hours or days," you have a traceability debt. For more on closing the SDLC loop, follow me!

The outcome is surgical delivery. Requirement changes trigger immediate test impact analysis. Failed tests auto-create linked bugs. Release reports show exactly which requirements are validated. Your team stops chasing context and starts shipping with confidence.

The biggest hidden cost in the SDLC isn't bad code. It's lost context. Requirements written in Jira, discussed in Slack, tested in spreadsheets, and deployed with fingers crossed. By the time a bug surfaces, no one remembers what "done" actually meant.

The question worth asking this sprint: how many of your BDD scenarios were last executed more than 30 days ago? If that number is significant, you do not have a BDD problem. You have a test management problem that BDD has made more visible.

BDD produces system documentation that is automatically checked against the system's behaviour. That is only true if someone is managing the scenarios with discipline. Without it, BDD produces documentation that was once checked and has since quietly drifted.

BDD promises shared understanding between product, dev, and QA. Most teams end up with 400 Gherkin scenarios nobody reads, a step definition file nobody maintains, and a CI pipeline nobody trusts. The promise is real. The execution usually is not.