SEI Analytics

Configure sprint analytics, investment allocation, capacity forecasting, and release readiness assessments in Harness Software Engineering Insights.

Instructions

Step 1: Establish Scope

Confirm the user’s org, team, and tracking period.

Call MCP tool: harness_list
Parameters:
  resource_type: "project"
  org_id: "<organization>"

Step 2: Identify the SEI Task

Determine which analytics the user needs:

1. Sprint Analytics – Velocity, estimation accuracy, scope change tracking
2. Investment Allocation – Engineering time breakdown by category
3. Sprint Planning and Capacity Forecast – Data-driven sprint planning
4. Release Readiness Assessment – Checklist for release go/no-go

Step 3: Configure Sprint Analytics

Gather from the user:

• Team name and sprint length (weeks)
• Tracking period (last N sprints)
• Issue tracker integration (Jira, Azure DevOps, Linear)

Analyze sprint metrics:

Velocity:

• Story points committed vs. completed per sprint
• Velocity trend over the tracking period
• Carryover rate (% of stories that spill into next sprint)

Estimation Accuracy:

• Actual vs. estimated story points across sprints
• Which issue types are consistently underestimated
• Estimation accuracy trend (improving or degrading)

Scope Change:

• Stories added mid-sprint (scope creep %)
• Stories removed mid-sprint
• Net scope change and its impact on completion rate

Delivery Composition:

• Breakdown by issue type: features, bugs, tech debt, maintenance
• Breakdown by priority: P0/P1 vs. lower priority
• Team-level vs. individual-level trends

Step 4: Analyze Investment Allocation

Gather from the user:

• Teams to analyze
• Time period for analysis
• Investment categories (features, bugs, tech debt, maintenance, ops)

Calculate allocation:

• % of engineering time per category
• Compare against target allocation (e.g., 70% features, 15% tech debt, 10% bugs, 5% ops)
• Trend over time: is tech debt growing or shrinking?
• Per-team breakdowns to identify teams disproportionately spending on bugs or ops

Present with:

• Current allocation vs. target allocation
• Recommendations for rebalancing
• Impact of current allocation on velocity and quality trends

Step 5: Sprint Planning and Capacity Forecast

Gather from the user:

• Sprint number, duration, and team size
• Planned PTO, meetings, and other commitments

Calculate capacity:

• Available engineering days = team size * sprint days - PTO - meeting overhead
• Historical velocity per engineer per sprint
• Recommended story points for the sprint (based on rolling average)

Risk factors to flag:

• Large stories (>8 points) that could block the sprint
• Dependencies on other teams
• Carryover from previous sprint
• Tech debt items that may slow feature work

Step 6: Release Readiness Assessment

Gather from the user:

• Service name, version, target environment
• Change summary and target release date

Assess readiness:

Code Quality:

• Code coverage delta (current vs. threshold)
• Static analysis findings (new warnings or errors)
• Code review completion (all PRs reviewed and approved)

Pipeline Health:

Call MCP tool: harness_list
Parameters:
  resource_type: "execution"
  org_id: "<organization>"
  project_id: "<project>"

• CI pipeline pass rate for the release branch
• All required stages green (build, test, security, integration)

Testing:

• Unit test pass rate and coverage
• Integration test results
• Performance test results vs. baseline

Security:

• No unresolved critical or high CVEs
• Security scan completed and approved
• Compliance attestations present

Operational Readiness:

• Monitoring and alerting configured for the release
• Runbooks updated for new features
• Rollback plan documented and tested

Present as a structured readiness report with GO/NO-GO recommendation.

Examples

• “Show sprint velocity trends for the platform team” – Analyze committed vs. completed points over the last N sprints
• “How is our engineering investment split between features and bugs?” – Calculate investment allocation with recommendations
• “Help plan Sprint 24 based on our capacity” – Calculate available capacity and recommend story point commitment
• “Is the checkout-service v2.1 ready to release?” – Run full readiness assessment with go/no-go recommendation
• “Why are our sprints always over-committed?” – Analyze estimation accuracy and scope change patterns

Performance Notes

• Sprint analytics require consistent use of story points across the team – mixed estimation methods reduce accuracy.
• Investment allocation analysis needs accurate issue labeling – unlabeled issues default to “unclassified” and skew results.
• Capacity forecasts should account for context-switching overhead (typically 10-20% of engineering time).
• Release readiness assessments should run at least 24 hours before the planned release date.

Troubleshooting

Velocity Data Inconsistent

• Verify story points are assigned before sprint start (not mid-sprint)
• Check that completed stories are marked done within the sprint, not after
• Ensure the issue tracker integration is syncing correctly

Investment Allocation Missing Categories

• Check issue labeling conventions – all issues should have a type (feature, bug, tech debt)
• Review the mapping between issue tracker labels and SEI categories
• Add default categorization rules for unlabeled issues

Release Readiness Check Incomplete

• Verify all pipeline stages have run for the release branch
• Check that security scan results are available for the release artifacts
• Ensure monitoring configuration has been validated in staging