Version 2.6.0: Dependency-Aware Planning with Enhanced Forecasting and Performance

Version 2.6.0: Dependency-Aware Planning with Enhanced Forecasting and Performance

Version 2.6.0 · Released June 2, 2026 · Jira Cloud. Available now — no configuration required.

Version 2.6.0 brings the realities of interconnected work into planning. Until now, forecasts assumed every item could be worked independently — but releases are shaped by dependencies. With 2.6.0, dependencies become first-class citizens: the Monte Carlo engine understands that a release can only move as fast as its longest blocking chain, backed by a new Critical Path view and proactive scope-risk analysis.

What's new

Intelligent, dependency-aware forecasting

  • Critical-path integration: the engine calculates critical-path slack; negative slack pushes the probable completion date later.

  • Cross-team reality checks: the forecast adjusts to the slowest contributor in a blocking chain.

  • Transparent risk attribution: a Critical Path Method backward pass attributes risk to the specific blocking issues.

  • A/B scenario toggle: compare the forecast "with dependencies" versus "without" to quantify the delay.

Visualizing the critical path

  • Dedicated Critical Path view: the exact sequence of work dictating your date, as an ordered ribbon.

  • Cross-release context: blockers living in other Fix Versions appear as distinct context nodes.

  • Cycle detection: infinite dependency loops are detected, omitted from the path, and flagged.

  • Release-filtered focus with direct navigation back to Jira.

Proactive scope-risk analysis

  • Ranked risk heatmap: flagged items sorted by descending risk score.

  • Outlier flagging: items stalled beyond typical cycle time or blocked past a threshold are surfaced.

  • "What-if" scope modeling: simulate removing at-risk items and watch the confidence date recalculate — no changes to live Jira until you're ready.

Enterprise-grade performance

Enhancement

Benefit

Enhancement

Benefit

Intelligent forecast caching

Returning to a previously viewed release is near-instant when inputs are unchanged.

Optimized capacity engine

Absence and headcount records are pre-indexed rather than re-scanned each iteration.

Smoother rendering

Filtering and grouping are memoized, cutting the CPU cost of large boards.

What's in this release

Type

Summary

Type

Summary

Feature

Dependency-aware Monte Carlo forecasting with critical-path slack

Feature

Critical Path view with cross-release context and cycle detection

Feature

Proactive scope-risk heatmap and what-if scope modeling

Improvement

Forecast caching, pre-indexed capacity engine, memoized rendering

Get started

Try it on the Atlassian Marketplace · Learn more on divim.io · User Guide · Book a 30-minute demo

Previous release: Version 2.5.0: Capacity Aware Planning.