Quality & Engineering Leadership
Release confidence
that shows what was verified, and what was not.
Release confidence means your release decision can point to what was verified and what was not. It matters when the test cycle sets your release rhythm, or when the decision leans on whoever knows the suites best.
The real question
Whether anyone could tell you if quality improved last quarter.
From the Quality Maturity AssessmentMeasurement & Continuous Improvement
01Questions
The questions
it answers.
What was verified, and what was not?
The decision rests on coverage and gaps reported by product area, not on test counts and pass rates.
What is in scope for this release?
Checking starts from a scope agreed with product and engineering, not from the full pass by default.
Can the decision be made on today’s evidence?
Progress and blockers are reported as the cycle runs, so the decision uses the evidence available that day.
How does this release compare with the last?
Results come in the same shape every release, so each release is compared with the last, not judged afresh.
02Approach
How I work
on it.
Find where confidence breaks down
I review how release decisions are made today: what they rest on, and which parts of a release are not exercised at all.
Plan each cycle to the release
I help your team set a scope for each release before the cycle starts, instead of running the full pass by default.
Report every release the same way
I set up reporting in the same shape every release: what is covered, what failed, what has no automated coverage and what is blocked.
What you leave with
- A clear assessment of release risks and of what release decisions rest on today.
- A way to scope each regression cycle to the release, agreed with product and engineering.
- A report per release by product area: what was verified, what failed and what was not covered.
- Measures of release health, comparable from one release to the next.
03Evidence
The topic,
in practice.
From my employment: case studies where quality evidence was measured and made visible, each with the limits of its evidence stated.
Case studies
Modernising quality engineering at a UK property-technology company.
Automated checks were connected to the requirements and risks they covered and coverage was reported by product area, which helped teams explain what had been verified and identify where further testing was needed.
Sustaining quality across two mobile apps and their content delivery platform.
Performance and stability were measured across devices and operating-system configurations, giving the team evidence of how the apps behaved under real device conditions.
Making AI evaluations trustworthy at an AI-native language-technology platform.
Dashboards tracked the AI evals’ effectiveness, execution time and cost, and observability dashboards on live customer data showed the success rate of each part of the user journeys, so that improvements would have data behind them.
Want to improve reliability
and delivery?
Describe the team, the pipeline and what is getting in the way.