KPI Tree

Metric Definition

Forecasting a release date from scope

Forecast date = today + (Remaining scope / Average completion velocity)
Remaining scopeTotal scope minus completed work, in story points or items
Average completion velocityWork completed per period, averaged over recent periods
Total scopeAll planned work in the release at the current point in time

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Metric GlossaryOperations Metrics

Release burnup analysis

Release burnup analysis is a way of reading a burnup chart that plots completed work against total planned scope over the life of a release, so the team can forecast when the remaining work will be done. Unlike a burndown chart, it separates progress from scope, so scope changes are visible rather than hidden. It turns a backlog into an evidence-based delivery date.

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What is release burnup analysis?

Release burnup analysis is the practice of reading a burnup chart to forecast when a release will finish and to see how scope is changing along the way. A burnup chart has two lines: one for total planned scope and one for work completed to date. The gap between them is the work remaining. As the completed line rises towards the scope line, the release gets closer to done.

The analysis matters because it answers two questions at once. The completed line shows whether the team is making progress at a steady rate. The scope line shows whether the plan is growing underneath them. A burndown chart collapses both into a single falling line, which hides scope creep. A burnup keeps them apart, so a flat completed line and a rising scope line tell two very different stories that a burndown would blur into one.

What to measure in

Pick one unit for both lines and stay with it. Story points and completed item counts both work, but mixing them makes the gap meaningless. The forecast is only as honest as the consistency of the unit and the way the team sizes work.

How to calculate release burnup analysis

The chart is the artefact, but the forecast is the calculation. Take the work remaining, which is total scope minus completed work, and divide it by the average rate at which the team is completing work. That gives the number of periods left. Add those periods to today and you have a projected release date.

For a worked example, suppose a release has 400 points of scope, the team has completed 240, and the average velocity over the last four sprints is 40 points per sprint. Remaining scope is 160 points, so 160 divided by 40 gives four sprints. If a sprint is two weeks, the projected date is eight weeks out. Recompute it every sprint, because both scope and velocity move.

  1. 1

    Total scope

    The full set of planned work in the release, measured in story points or item counts, captured as of today.

  2. 2

    Completed work

    Everything accepted as done so far, in the same unit. Only count work that meets the definition of done, not work in progress.

  3. 3

    Average velocity

    Work completed per period averaged over a recent window, usually the last three to four sprints, to smooth out one-off spikes.

  4. 4

    Remaining scope

    Total scope minus completed work. This is the gap the forecast has to close.

Release burnup analysis in a metric tree

A burnup forecast slipping is a symptom, not a cause. To act on it you have to know whether the date moved because the team slowed down or because the plan grew. A metric tree decomposes the forecast into the drivers that actually move it, so the conversation is about a specific branch rather than a vague sense that the release is late.

When the projected date jumps, you can walk the tree. If velocity is steady but scope rose, the problem is intake, not delivery. If scope is flat but velocity dropped, look at carry-over, blockers, or capacity. KPI Tree connects each branch to the team and the action that influences it, so the person who owns intake sees the scope branch and the person who owns delivery sees the velocity branch. That is the difference between a dashboard that shows a slipping date and a Decision Intelligence model that shows who can pull it back.

Metric tree insight

When the forecast slips, the tree tells you which lever moved. A rising scope branch with steady velocity is an intake problem owned by the product lead. A falling velocity branch with flat scope is a delivery problem owned by the team. The same slipped date demands two different interventions.

Release burnup analysis benchmarks

There is no single target for a burnup, because the shape depends on release length and how scope is managed. What you benchmark instead is the health of the two lines: how stable velocity is and how much the scope line climbs after the release starts. The ranges below describe what a healthy, watch and at-risk pattern look like over a typical multi-sprint release.

PatternHealthyWatchAt risk
Scope growth after kickoffUnder 10 percent10 to 25 percentOver 25 percent
Velocity variance sprint to sprintUnder 15 percent15 to 30 percentOver 30 percent
Forecast date movement per sprintUnder 1 sprint1 to 2 sprintsOver 2 sprints
Carry-over share of velocityUnder 10 percent10 to 20 percentOver 20 percent

How to improve release burnup analysis

Improving the analysis is less about working faster and more about making the two lines trustworthy. A forecast you can stand behind comes from steady velocity, controlled scope intake, and consistent sizing. The cards below cover the levers that move those branches.

Gate scope intake

Make adding work to an in-flight release a deliberate decision with an owner. A rising scope line should always trace to a recorded choice, not silent creep.

Stabilise velocity

Reduce carry-over and protect capacity from interrupt work. A steady completion rate makes the forecast tighter, even if the average is lower.

Size consistently

Hold short estimation calibration sessions so points mean the same thing across the team. Drift in sizing quietly corrupts every forecast built on it.

Hold the definition of done

Count only fully accepted work as complete. Partial credit inflates the completed line and produces a release date that arrives later than promised.

Common mistakes when tracking release burnup analysis

  1. 1

    Reading it like a burndown

    Treating a flat completed line as the whole story misses a rising scope line. The gap between the two lines is the point of a burnup.

  2. 2

    Freezing the scope line

    Drawing total scope as a fixed horizontal line hides intake. The scope line must move whenever work is added or removed.

  3. 3

    Forecasting on a single sprint

    Using one good or bad sprint as the velocity input produces a wildly swinging date. Average over a recent window instead.

  4. 4

    Counting work in progress

    Adding nearly-done items to completed work flatters the forecast. Only accepted work belongs on the completed line.

Related metrics

Sprint velocity

Agile planning metric

Operations Metrics
Jira

Metric Definition

Sprint Velocity = Sum of Story Points Completed in a Sprint

Sprint velocity measures the amount of work a team completes during a sprint, typically expressed in story points, ideal days, or another unit of estimation. It is a planning tool that helps agile teams forecast how much work they can commit to in future sprints based on their historical completion rate. Velocity is one of the most widely used and most frequently misunderstood metrics in agile software development.

View metric

Cycle time

Process speed

Operations Metrics
Jira

Metric Definition

Cycle Time = Process End Time − Process Start Time

Cycle time measures the total elapsed time from the start to the end of a process. It is a fundamental operations metric used in manufacturing, software development, service delivery, and any context where the speed of a process directly affects throughput, cost, and customer satisfaction.

View metric

Deployment frequency

DORA metric

Operations Metrics
GitHub

Metric Definition

Deployment Frequency = Number of Production Deployments / Time Period

Deployment frequency measures how often an organisation successfully releases code to production. It is one of the four DORA (DevOps Research and Assessment) metrics that predict software delivery performance and organisational outcomes. Teams that deploy more frequently deliver value to users faster, reduce the risk of each individual release, and create tighter feedback loops between development and production.

View metric

Metric trees for engineering teams

Metric Definition

See how release burnup analysis fits alongside the other delivery metrics an engineering team tracks to forecast and protect ship dates.

View metric

How to build a metric tree

Metric Definition

Learn how to decompose release burnup analysis into the scope and throughput drivers that move the forecast so the team can act on it.

View metric

Model your release burnup as a metric tree

Build the forecast date as a tree in KPI Tree, with scope intake and delivery velocity on separate branches and a named owner on each. When the date moves, the owner of the branch that caused it is the one who hears about it.

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