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We'll show you exactly how AI is impacting your speed and code quality.
Get visibility into what engineering committed to, what shipped, how capacity was split across new features versus rework, and whether bug volume is climbing against the releases you're planning — without waiting for the sprint retrospective to find out.
Every sprint starts with a plan. By the end, some features shipped, some spilled over, and some never made it to the board. The explanation is usually vague — "engineering was busy," "there were some bugs," "we had to refactor something." What was actually consuming capacity, and how much, stays invisible until the retrospective. By then, the next sprint's commitments are already made.
A feature that didn't ship this sprint is a story point that moved to the next one. Whether it spilled because of a review bottleneck, a scope change, or a codebase that required three days of rework to touch — none of that is visible in the Jira ticket.
How much of last quarter's engineering effort went to new features versus fixing existing ones? Most product leaders can't answer this — and can't make an accurate capacity commitment to stakeholders until they can.
Bugs accumulate quietly across Jira projects. By the time inflow outpaces resolution, a release timeline is already at risk. There's no cross-project view to catch it earlier.
See sprint-by-sprint delivery accuracy: what was committed, what completed, and what spilled over — visualized as completion and spillover rates per sprint. Drill from the delivery view into the specific epics and issues that carried over to understand whether the pattern is improving or recurring.
See how engineering capacity distributed across product areas, epics, and specific work items — in story points or ticket counts, by sprint or by date range. The allocation view shows where engineering time actually went versus where the roadmap assumed it would go.
See the split between new feature development, rework, and maintenance — week by week, across every team. When rework is consuming 40% of a sprint, that's not an engineering problem — it's a signal that requirements or quality gates upstream need attention.
See bug creation and resolution rates across every Jira project — grouped by time or by project. The quarter where bug inflow starts outpacing resolution is visible before it becomes a release decision. Compare bug-to-feature ratios across product areas to see where quality is under pressure.
Yes. The investment view lets you switch between work item, epic, product, and allocation views — all filterable by sprint or date range. Drill from the sprint delivery summary into specific epics to see which ones completed, which spilled, and which were added mid-sprint.
Rework is code modified or deleted within 30 days of being originally written — covering bug fixes caught in QA, scope changes mid-sprint, and integration failures. The 30-day window aligns to a typical 2-3 week sprint cycle. Code modified after 30 days is classified as maintenance. Both signal different upstream problems worth addressing in planning.
Yes. The bug inflow view supports multi-project selection — choose any combination of Jira projects and see issue type distribution grouped by time or by project. This gives product leaders a cross-product quality view that individual Jira boards can't provide.
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