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A high first-pass rate measures gate performance, not end-to-end delivery. The first rate asks whether work passed one named check on its first recorded attempt; delivered share asks what happened to a defined group of started changes by a stated date. Because the measures use different outcomes—and may use different populations—one cannot substitute for the other.
What first-pass rate measures—and what it leaves out
A first-pass rate is meaningful only when its gate, attempt rule, and denominator are explicit. For a named gate, calculate it as:
First-pass rate = changes that pass their first recorded attempt at that gate ÷ changes with a recorded first attempt at that gate.
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Passing on the first attempt says nothing by itself about whether the change was later delivered, remained open, was replaced, or was abandoned. It is a gate result, not a lifecycle outcome.
How delivered share answers a different question
Delivered share measures the outcome of a defined started-work cohort:
Delivered share = changes in the cohort that reach the defined delivery event ÷ started changes in the cohort.
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The denominator can include work that never reached the first-pass gate. Before reporting the figure, specify what counts as “started,” which changes belong to the cohort, what event qualifies as delivery, and the date through which outcomes are observed. Without those rules, two delivered-share figures may not be comparable even if they carry the same label.
Define the cohort and observation cut-off
Work in a recent cohort may still be in progress. If every open change is treated as a failure, the reported delivered share can fall even though some of that work will deliver later. At a dated cut-off, keep distinct states visible rather than forcing open work into a terminal category.
- Delivered: reached the stated delivery-acceptance event by the cut-off.
- Superseded: replaced by other work under the workflow’s defined rule.
- Abandoned: stopped without delivery under the workflow’s defined rule.
- Pending: has no terminal disposition by the cut-off.
Report the cohort dates and inclusion rules alongside the observation date. The available source does not establish a maturity threshold or a time-to-delivery method, so neither should be assumed. A fixed-cohort illustration shows why the cut-off matters: of 100 hypothetical changes, an earlier snapshot might show 40 delivered and 45 pending, while a later snapshot might show 58 delivered and 17 pending. These are illustrative counts, not measured results.
Keep lifecycle measures separate
Additional measures can clarify what happened between start and outcome, but they answer different questions and should not be combined into one score:
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- Repair cycles per started change: recorded repair cycles divided by started changes, using a stated definition of a repair cycle.
- Terminal non-delivery share: superseded or abandoned changes divided by started changes.
- Pending share: changes without a terminal state at the cut-off divided by started changes.
A useful lifecycle record should preserve each change identifier, timestamps, stage, work or risk class, gate attempts, repair transitions, and final disposition. Link each finding to its disposition and to any revision that addressed it. The proposed state model needs validation against local cases such as rollback after delivery, rejection after approval, partial deployment, reopened work, and changes leaving the tracked workflow; add transitions where those cases require them.
What the reported percentages can—and cannot—show
James Smith’s AFT Group article, posted August 28, 2026, reports a 91.7% first-pass rate and a 52.2% delivered share of started work. It also reports 63.3% blocking review findings, 0% requiring a second pass, zero repair cycles per started change, and 34.8% approved-plan coverage. These are reported observations, not independently verified results or benchmarks.
The underlying measurement draft and raw counts are unavailable. The gate definition and numerator for the first-pass figure are not supplied; the delivered-share figure lacks raw counts, cohort rules, an observation window, and a delivery definition. The reported finding and repair values likewise lack the records and definitions needed to audit them. They therefore cannot establish comparative team performance or support a reliable comparison across periods.
In particular, blocking findings alongside zero recorded repair cycles do not prove that repairs did—or did not—occur. A finding might have been corrected before the measured gate, followed by no recorded repair event, remained open, been superseded or abandoned, left the tracked workflow, or still reached delivery. Trace each finding to its disposition and any revision before drawing conclusions about repair demand or engineering capacity.
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Use broader delivery metrics as context, not a substitute
DORA describes five software delivery performance metrics grouped around throughput and instability, and cautions against treating a single metric as the only goal. That broader frame can add context, but it does not replace clear local definitions for the gate, started cohort, lifecycle states, and delivery-acceptance event. See DORA’s software delivery metrics guidance.
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Tools can help collect and visualize events, but they do not decide what your organization means by “started,” “passed,” or “delivered.” DORA lists tools for delivery-metric collection and visualization; Atlassian documents Jira work-item and delivery metrics, some of which require development or deployment integrations. Instrumentation can expose activity; local cohort and acceptance rules still need to be defined.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical reporting checklist
- Name the first-pass gate and define what counts as an attempt and a pass.
- Show the first-pass numerator and denominator, including how unattempted or missing records are handled.
- Define “started,” cohort dates, and inclusion and exclusion rules for delivered share.
- State the delivery-acceptance event and the observation cut-off.
- At that cut-off, show delivered, superseded, abandoned, and pending counts separately.
- Preserve event-level history so findings, attempts, repairs, revisions, and dispositions can be traced to each change.
- Compare teams or periods only after confirming stable definitions, collection methods, and cohort maturity.
For wider background on measuring software delivery performance, Accelerate: The Science of Lean Software and DevOps: Building and Scaling High Performing Technology Organizations, by Nicole Forsgren, Jez Humble, and Gene Kim, is an optional source of context. Simon & Schuster lists the first-edition trade paperback published by IT Revolution, ISBN 9781942788331. It addresses the broader subject; it does not validate the reported percentages above.
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