Metrics List
View a comprehensive list of the metrics in Oobeya, which is organised into different categories for your convenience.
Oobeya Metrics List
This page provides a module-by-module reference for the metrics, scores, distributions, and analytical indicators avaier permissions
Team, repository, project, and data-source mappings
Selected date range
Organization-specific configuration
Oobeya metrics should be interpreted together and within the context of each team. A single metric should not be used as an individual performance target.
Metrics by Module
Development Analytics
Development Analytics uses source code management data to analyze engineering activity, contribution patterns, code changes, work types, and development behavior.
Supported data sources include GitHub, GitLab, Azure DevOps, Bitbucket, and Gitea.
For detailed configuration and calculation information, see Git Analytics – Metric Definitions.
Engineering Efficiency and Impact
Coding Efficiency (%)
The percentage of analyzed code changes classified as productive work rather than short-term rework or Code Churn.
Coding Impact Score
A configurable score representing the scope and approximate cognitive impact of code changes. It considers files added, modified, or deleted; Git hunks; and lines added, edited, or deleted.
Impact Ratio
Shows how Coding Impact is distributed among contributors within a team. It helps identify concentrated ownership, knowledge silos, and workload imbalance.
Coding Impact per Developer
The Coding Impact Score attributed to each developer during the selected period.
Rework Rate (%)
The percentage of analyzed development activity classified as Code Churn or short-term rework.
Coding Impact Trend
The change in Coding Impact Score over the selected period.
Coding Efficiency Trend
The change in Coding Efficiency over time.
Learn more about Coding Impact Score and Impact Ratio.
Work Type Metrics
New Work
Newly written code lines.
Refactor
Changes made to existing code after the configured aging period. The default aging period is 21 days.
Help Others
Changes made by a developer to another developer’s recent work.
Code Churn / Rework
Code rewritten or deleted by the same developer shortly after it was originally written. The default period is 21 days.
New Work Rate (%)
Percentage of analyzed code changes classified as New Work.
Refactor Rate (%)
Percentage of analyzed code changes classified as Refactor.
Help Others Rate (%)
Percentage of analyzed code changes classified as Help Others.
Code Churn Rate (%)
Percentage of analyzed code changes classified as Code Churn.
Work Type Distribution
Distribution of New Work, Refactor, Help Others, and Code Churn within the selected period.
Development Activity Metrics
Total Commits
Total number of commits during the selected period.
Active Coding Days
Number of days with at least one commit or coding activity.
Coding Days per Week
Average number of active coding days per week.
Active Contributors
Number of contributors with development activity during the configured activity period.
Commits per Contributor
Average number of commits per active contributor.
Development Activities
Total development activities included in the selected scope and period.
Contributor Activity Distribution
Distribution of development activity among contributors.
Repository Contribution Distribution
Distribution of development contributions across repositories.
Contribution by Repository
Contribution attributed to each repository.
Contribution by Team
Contribution attributed to each mapped team.
Contribution by Developer
Contribution attributed to each developer profile.
Code Change Metrics
Lines Added
Number of code lines added.
Lines Deleted
Number of code lines deleted.
Lines Edited
Number of existing code lines modified.
Total Code Changes
Total number of added, deleted, and edited code lines.
Files Added
Number of new files added to the repository.
Files Modified
Number of existing files modified.
Files Deleted
Number of files deleted.
Files Touched
Total number of unique files affected by development activity.
Git Hunks
Number of separate change blocks within the modified files.
Average Change Size
Average size of the analyzed commits or development changes.
Change Size Distribution
Distribution of development activities by change size.
File Statistics
Summary of files added, modified, deleted, or touched.
Language Distribution
Distribution of code changes by programming language.
Commit count and lines of code should not be treated as direct productivity measures. Use Coding Impact, Coding Efficiency, work type, quality, and delivery metrics together.
Pull Request Analytics
Pull Request Analytics helps teams understand review responsiveness, collaboration, flow efficiency, pull request size, and review risks.
Pull Request Volume
Total Pull Requests
Total number of pull requests included in the selected period.
Open PRs
Number of pull requests currently open and awaiting review, approval, or merge.
Merged PRs
Number of pull requests successfully merged.
Closed PRs
Number of pull requests closed without being merged.
PRs Created
Number of pull requests created during the selected period.
PRs per Contributor
Average number of pull requests created per active contributor.
Reviewed PRs
Number of pull requests reviewed by the selected reviewer, developer, or team.
Reviewed PRs / Total PRs (%)
Percentage of pull requests reviewed by the selected reviewer, developer, or team.
Pull Request Time Metrics
Coding Time
Time between the first commit and the opening of the pull request.
Code Review Cycle Time
Time between the pull request opening and merge.
Time to Merge
Time between the first commit and merge.
Average Review Time
Average time taken by reviewers to complete a pull request review.
Coding Time Over Goal (%)
Percentage of pull requests whose Coding Time exceeds the configured goal.
Code Review Cycle Time Over Goal (%)
Percentage of pull requests whose review cycle exceeds the configured goal.
Time to Merge Over Goal (%)
Percentage of pull requests whose Time to Merge exceeds the configured goal.
PRs Merged Within Goal (%)
Percentage of merged pull requests completed within the configured target time.
Pull Request Size and Review Metrics
Pull Request Size
Total number of lines added, removed, and changed in a pull request.
Average Pull Request Size
Average size of pull requests during the selected period.
Pull Request Size Over Goal (%)
Percentage of pull requests exceeding the configured size goal.
Number of PR Reviewers
Number of reviewers assigned to or participating in pull requests.
Review Comment Count
Number of review comments added to pull requests.
PR Approvals
Number of pull request approvals.
PR Needs Work
Number of pull requests returned for changes.
PR Revert Rate (%)
Percentage of merged pull requests whose changes were later reverted.
Pull Request Risk Indicators
Oversized PRs
Number of pull requests exceeding the configured size threshold.
Overdue PRs
Number of pull requests exceeding the configured completion or review-time threshold.
Stale PRs
Number of open pull requests without meaningful activity for the configured period.
Pull Request Risks
Combined count of Oversized, Overdue, or Stale pull requests.
PR Risk Distribution
Distribution of pull requests by identified risk type.
Delivery Analytics and DORA Metrics
Delivery Analytics connects source code management, CI/CD, deployment, and incident data to measure software delivery performance.
See DORA Metrics Introduction and Deployment Analytics.
Four Key DORA Metrics
Lead Time for Changes
Time from a code commit or development change until that change is successfully deployed to production.
Deployment Frequency
How often the team successfully deploys changes to production.
Change Failure Rate (%)
Percentage of production deployments that result in a failure or incident.
Time to Restore Service / MTTR
Time required to restore service after a production failure.
Delivery Flow Metrics
Development Time
Time between the first commit and merge of the related pull request or change.
Waiting for Deploy
Time between the pull request merge and the start of the deployment pipeline.
Deployment Duration
Time between the deployment pipeline start and successful completion.
Lead Time Breakdown
Breakdown of Lead Time for Changes into development, waiting, and deployment phases.
Deploy Size
Number of commits and pull requests included in a deployment package.
Average Deploy Size
Average number of commits and pull requests delivered per deployment.
Deployment and Incident Volume
Number of Deployments
Total number of detected successful production deployments.
Number of Contributors
Number of contributors whose changes were included in deployments.
Deployments Leading to an Incident
Number of deployments associated with a production incident or failure.
Production Incidents
Number of incidents included in Change Failure Rate and restoration calculations.
Successful Deployments
Number of production deployments completed without a detected failure.
Failed Changes
Number of production changes associated with an incident or rollback condition.
Deployment Frequency Trend
Change in successful deployment frequency over time.
Change Failure Rate Trend
Change in the percentage of failed production changes over time.
Lead Time Trend
Change in Lead Time for Changes over time.
Time to Restore Service Trend
Change in restoration time over time.
Change Failure Rate and Time to Restore Service require a correctly configured failure-detection source and production deployment mapping.
Project Analytics
Project Analytics analyzes work items from Jira, Azure Boards, and supported project management systems.
Metrics can be calculated by work-item count, effort, story points, or time estimation depending on the data-source configuration.
See Project Analytics – Metric Definitions.
Board-Level Metrics
Completed Sprints
Number of sprints started and completed during the selected period.
Average Velocity by Effort
Average amount of estimated effort completed per sprint.
Average Lead Time
Average time from work-item creation to completion.
Average Cycle Time
Average time from work start to completion.
Completed Work Items
Total number of work items completed during the selected period.
Completed Work Items per Sprint
Average number of work items completed in each sprint.
Average Throughput per Week
Average number of work items completed per working week.
Flow and Reaction Time Metrics
Pickup Time
Initial time between work-item creation and its recognition or placement into a ready backlog or queue.
Actual Reaction Time
Time from the configured reference point or ready state until work starts.
Total Reaction Time
Pickup Time plus Actual Reaction Time.
Cycle Time
Time from when active work begins until the work item is completed.
Lead Time
Time from work-item creation until completion.
Lead Time Breakdown
Distribution of total Lead Time across reaction, active development, waiting, and configured workflow phases.
State Cycle Time
Time spent in each mapped workflow state.
Time in Status
Amount of time a work item remains in a specific status.
Waiting Time
Time a work item remains idle between active workflow states.
Sprint Planning Metrics
Planned
Number of work items included before the sprint planning cut-off date.
Planned Effort
Total estimated effort of work items included before the planning cut-off date.
Done Planned
Number of planned work items completed by the end of the sprint.
Done Planned Effort
Estimated effort of planned work items completed by the end of the sprint.
Pulled in Extra
Number of work items added after the sprint planning cut-off date.
Effort Pulled in Extra
Estimated effort of work items added after the planning cut-off date.
Done Pulled in Extra
Number of pulled-in work items completed by the end of the sprint.
Done Pulled in Extra Effort
Estimated effort of pulled-in work completed by the end of the sprint.
Unfinished
Number of work items incomplete at the end of the sprint.
Unfinished Effort
Estimated effort of work items incomplete at the end of the sprint.
Unfinished Planned
Number of planned work items incomplete at the end of the sprint.
Unfinished Effort Planned
Estimated effort of planned work items incomplete at the end of the sprint.
Unfinished Pulled in Extra
Number of pulled-in work items incomplete at the end of the sprint.
Unfinished Effort Pulled in Extra
Estimated effort of pulled-in work items incomplete at the end of the sprint.
Dropped
Number of work items removed from an active sprint after the planning cut-off.
Effort Dropped
Estimated effort of work items removed from the sprint.
End of Sprint
Total number of completed and incomplete work items at the end of the sprint.
Effort End of Sprint
Total estimated effort of completed and incomplete work at the end of the sprint.
Done
Total number of work items completed by the end of the sprint.
Done Effort
Total estimated effort of completed work items.
Sprint Performance Metrics
Sprint Velocity by Count
Number of work items completed during the sprint.
Sprint Velocity by Effort
Total estimated effort completed during the sprint.
Predictability (%)
Percentage of originally planned work completed: Done Planned / Planned × 100.
Productivity (%)
Total completed planned and extra work relative to the original plan: (Done Planned + Done Pulled in Extra) / Planned × 100.
Churn (%)
Percentage of completed work that was pulled into the sprint after planning: Done Pulled in Extra / (Done Planned + Done Pulled in Extra) × 100.
Sprint Delivery Rate by Count (%)
Completed work items divided by total work items at the end of the sprint.
Sprint Delivery Rate by Effort (%)
Completed effort divided by total effort at the end of the sprint.
Sprint Planning Accuracy by Count (%)
Completed work items divided by planned work items.
Sprint Planning Accuracy by Effort (%)
Completed effort divided by planned effort.
Sprint Scope Change
Amount of work added to or removed from a sprint after the planning cut-off.
Scope Change Rate (%)
Relative change in sprint scope after planning.
Backlog and Kanban Metrics
Backlog Size
Number of uncompleted work items in the backlog, excluding active work.
Backlog Age
Age of the oldest or longest-waiting item in the backlog.
Average Backlog Age
Average age of work items currently in the backlog.
Open Bugs in Backlog
Number of bug-type work items currently waiting in the backlog.
Current Backlog Items
Number of items waiting in a Kanban backlog.
Work in Progress
Number of work items currently in active workflow states.
Work in Progress Over 5 Days
Work items that have remained in progress for more than five days.
Throughput
Number of work items completed during the selected period.
Average Throughput per Week
Average number of work items completed per week.
Reopened Work Items
Number of work items reopened after completion.
Work Item Reopen Count
Total number of reopen events during the selected period.
Work Mix and Distribution Metrics
Innovation Rate by Count (%)
Percentage of completed or planned work items classified as innovation or new product work.
Innovation Rate by Effort (%)
Percentage of total effort allocated to innovation or new product work.
Work Item Type Distribution
Distribution of work by configured work-item type.
Work Item Priority Distribution
Distribution of work by priority.
Work Item Status Distribution
Distribution of work items across workflow statuses.
Work Item Category Distribution
Distribution of work across configured categories such as new work, maintenance, bugs, or support.
Work Type by Member
Distribution of project work among team members and work categories.
Project Contribution
Distribution of completed work or effort among contributors and teams.
Planning metrics depend on correct sprint dates, planning cut-off configuration, workflow mapping, effort fields, and excluded work-item types.
Code Quality Analytics
Code Quality Analytics uses SonarQube or SonarCloud data to provide visibility into technical debt, security, reliability, maintainability, and codebase health.
Core Quality Metrics
Technical Debt – Overall
Estimated remediation time required to fix all maintainability issues in the analyzed codebase.
Technical Debt – New Period
Technical debt introduced during the selected period or on new code.
Technical Debt per Developer
Technical debt attributed to each developer based on source-control author information.
Added Technical Debt
Technical debt introduced during the selected period.
Code Quality Issues
Total number of quality issues detected by the connected code-quality platform.
Bugs
Number of reliability issues.
Vulnerabilities
Number of security issues.
Code Smells
Number of maintainability issues.
Duplicated Lines (%)
Percentage of code identified as duplicated.
Test Coverage (%)
Percentage of analyzed code covered by tests.
Lines of Code
Total analyzed codebase size used as an input for normalization and quality analysis.
Severity and Risk Metrics
Blocker Issues
Number of issues classified at the highest configured severity.
High-Severity Issues
Number of issues classified as high severity.
Medium-Severity Issues
Number of issues classified as medium severity.
Low-Severity Issues
Number of issues classified as low severity.
Security Severity – Blocker, Overall
Total blocker-level security issues in the analyzed codebase.
Security Severity – High, Overall
Total high-severity security issues in the analyzed codebase.
Security Severity – Blocker, New Period
New blocker-level security issues introduced during the selected period.
Security Severity – High, New Period
New high-severity security issues introduced during the selected period.
Issue Risk Score
Risk score calculated using issue severity, quality category, and remediation effort.
Project Security Risk Score
Aggregated security risk for the analyzed project.
Project Reliability Risk Score
Aggregated reliability risk for the analyzed project.
Project Maintainability Risk Score
Aggregated maintainability risk for the analyzed project.
The Issue Risk calculation follows this structure:
Total Code Quality Index Metrics
Total Code Quality Index – TCQI
Composite quality score based on issue severity, security, reliability, maintainability, remediation effort, and codebase size.
Security Index
Normalized score representing the security health of the codebase.
Reliability Index
Normalized score representing reliability health.
Maintainability Index
Normalized score representing maintainability health.
Security Score / Rating
Overall security rating provided by the connected quality platform.
Reliability Score / Rating
Overall reliability rating provided by the connected quality platform.
Maintainability Score / Rating
Overall maintainability rating provided by the connected quality platform.
TCQI Trend
Change in Total Code Quality Index over time.
Security Index Trend
Change in the Security Index over time.
Reliability Index Trend
Change in the Reliability Index over time.
Maintainability Index Trend
Change in the Maintainability Index over time.
TCQI coefficients can be configured under the Code Quality administration settings.
Application Performance Metrics
Application Performance metrics are available when Oobeya is connected to a supported APM or monitoring platform.
APDEX Score
Application Performance Index representing user satisfaction based on response-time thresholds.
Error Rate (%)
Percentage of application requests that result in an error.
Average Response Time
Average time required for the application or service to respond to requests.
Transaction Volume
Number of monitored application transactions during the selected period.
Incident Count
Number of detected operational incidents associated with the selected service or application.
Application Performance Trend
Change in application performance over time.
Error Rate Trend
Change in application errors over time.
Response Time Trend
Change in average response time over time.
Availability depends on the connected APM tool and the data exposed by that integration.
Test Analytics
Test Analytics combines manual and automated test data from supported test management and automation systems.
See Measuring and Improving Test Efficiency.
Test Efficiency (%)
Percentage of successful tests across all analyzed test runs.
Escaped Defects (%)
Ratio of defects discovered after UAT or the configured testing stage.
Automation Coverage (%)
Share of the tested scope or code covered by automated tests.
UAT Success Rate (%)
Percentage of successful User Acceptance Testing executions.
Defect Resolution Rate (%)
Percentage of detected defects resolved during the test cycle.
Execution Time (ms)
Average time required to execute a test.
Total Test Executions
Number of test executions during the selected period.
Successful Test Executions
Number of test executions completed successfully.
Failed Test Executions
Number of failed test executions.
Manual Test Executions
Number of manually executed tests.
Automated Test Executions
Number of tests executed through automation.
Test Success Trend
Change in successful test execution rate over time.
Escaped Defect Trend
Change in post-testing defect leakage over time.
Automation Trend
Change in automation coverage or automated execution share over time.
AI Coding Assistant Impact
The AI Coding Assistant Impact module measures adoption, engagement, usage effectiveness, and the relationship between AI usage and software engineering outcomes.
See GitHub Copilot – AI Impact and Measuring the Impact of AI Coding Assistants.
User and License Metrics
Licensed Users
Number of users assigned an AI coding assistant license.
Active Users
Number of licensed users who were active during the selected period.
Engaged Users
Number of active users who meaningfully interacted with AI coding assistant features.
Inactive Users
Licensed users without qualifying activity during the selected period.
Inactive Users – Last 7 Days
Users without qualifying AI activity during the last seven days.
Inactive Users – Last 30 Days
Users without qualifying AI activity during the last 30 days.
Adoption Rate (%)
Percentage of active users who are engaged: Engaged Users / Active Users × 100.
License Utilization (%)
Percentage of licensed users who actively use the AI coding assistant.
User Activation Rate (%)
Percentage of licensed users who became active during the selected period.
Total Users
Total number of users included in AI Impact reporting.
Suggestion and Acceptance Metrics
Total Suggestions
Number of code suggestions generated by the AI coding assistant.
Accepted Suggestions
Number of AI-generated suggestions accepted by users.
Rejected Suggestions
Number of suggestions not accepted by users.
Suggestion Acceptance Rate (%)
Percentage of suggestions accepted: Accepted Suggestions / Total Suggestions × 100.
Suggested Lines
Number of code lines suggested by the AI coding assistant.
Accepted Lines
Number of suggested code lines accepted by users.
Line Acceptance Rate (%)
Percentage of suggested lines accepted: Accepted Lines / Suggested Lines × 100.
Accepted vs. Rejected Suggestions
Distribution of accepted and rejected AI suggestions.
Engagement and Acceptance Trend
Change in assistant engagement and acceptance over time.
Feature and Tool Usage Metrics
IDE Code Completion Usage
Usage generated through inline IDE code completions.
Chat Usage
Usage generated through AI chat interactions.
Pull Request Integration Usage
Usage generated through AI features integrated with pull request workflows.
Feature Usage Distribution
Distribution of AI usage across completion, chat, PR, and supported feature types.
Usage by IDE / Editor
Distribution of AI coding assistant activity by IDE or editor.
Usage by Programming Language
Distribution of AI activity by programming language.
Usage by Model
Distribution of usage across available AI models.
Usage by Product
Distribution across AI coding assistant products or SKUs.
Usage Trend
Change in AI coding assistant activity over time.
Team and User-Level Indicators
Team Adoption Rate (%)
Adoption Rate calculated for a mapped Oobeya team.
Team Engagement Rate (%)
Percentage of team members meaningfully interacting with AI features.
Team Acceptance Rate (%)
Suggestion or line acceptance rate for a team.
Most Active Team
Team with the highest qualifying AI usage during the selected period.
Most Efficient Usage Team
Team with the strongest usage-effectiveness result based on configured acceptance and engagement indicators.
Teams with Low Activity
Teams whose AI usage remains below the configured activity level.
Users with Low Activity
Users whose AI usage remains below the configured activity level.
Top Active Users
Users with the highest qualifying AI activity.
User Adoption and Engagement
Adoption and engagement indicators shown at user level.
Actual Contribution vs. AI Contribution
Comparison between overall development contribution and AI-assisted contribution signals.
AI Contribution Rate (%)
Share of tracked development contribution associated with AI-assisted activity, where supported.
AI Cost and Credits
The AI Cost and Credits module provides financial visibility into AI coding assistant consumption at organization, team, user, model, product, repository, and cost-center levels.
See AI Cost and Credits.
Summary Metrics
Total Users
Number of users represented in the selected AI cost and credit data.
AI Credit Pool Value
Monetary value of the organization’s available AI credit pool.
Total AI Credits Used
Total number of AI credits consumed during the selected period.
Remaining AI Credits
Unused credits remaining in the configured credit pool.
Total Gross Cost
Total cost before discounts or adjustments.
Discount Amount
Total discount applied to AI usage charges.
Net Cost
Cost after discounts and adjustments.
Applied Cost per Credit
Monetary rate applied to each AI credit.
Remaining Credit Pool Value
Monetary value of credits remaining in the configured pool.
Credit Utilization Rate (%)
Percentage of the total credit pool consumed.
Cost per User
Average or individual AI cost attributed to a user.
Credits per User
Average or individual AI credits consumed by a user.
Where the standard AI credit rate is configured as USD 0.01:
The actual rate may differ based on product configuration, provider pricing, contract terms, discounts, and imported billing data.
Cost and Credit Breakdown Metrics
Cost by User
AI cost attributed to each user.
Credits by User
AI credits consumed by each user.
Cost by Team
AI cost aggregated by mapped Oobeya team.
Credits by Team
AI credits aggregated by team.
Cost by Organization
AI cost aggregated by source organization.
Credits by Organization
AI credits aggregated by source organization.
Cost by Product
AI cost grouped by AI coding assistant product.
Credits by Product
AI credits grouped by product.
Cost by SKU
Cost grouped by provider SKU.
Credits by SKU
Credit usage grouped by provider SKU.
Cost by Model
AI cost grouped by language model.
Credits by Model
Credit consumption grouped by language model.
Model Usage Share (%)
Percentage of total AI consumption attributed to each model.
Cost by Repository
AI cost associated with each repository, where repository data is available.
Credits by Repository
AI credit usage associated with each repository.
Cost by Cost Center
AI cost aggregated by mapped cost center.
Credits by Cost Center
AI credit usage aggregated by cost center.
Cost Trend Metrics
Daily Credit Consumption
AI credits consumed per day.
Monthly Credit Consumption
AI credits consumed per month.
Credit Consumption Trend
Change in credit usage over time.
Daily AI Cost
AI cost generated per day.
Monthly AI Cost
AI cost generated per month.
AI Cost Trend
Change in AI cost over time.
Average Daily Cost
Average AI cost generated per day.
Average Monthly Cost
Average AI cost generated per month.
Projected Period Cost
Estimated cost for the full reporting period based on current consumption.
Budget or Pool Consumption Trend
Progress of cost or credit consumption against the configured pool.
Estimated Token Metrics
Detailed input, output, and cached-token telemetry may not be provided directly by every AI coding assistant.
When estimated token reporting is enabled, Oobeya may display:
Estimated Total Token Usage
Estimated number of tokens represented by the observed AI cost and model-pricing assumptions.
Estimated Input Tokens
Estimated input-token volume.
Estimated Output Tokens
Estimated output-token volume.
Estimated Cached Tokens
Estimated token volume served from provider cache.
Estimated Token Range
Low, expected, and high token-usage estimates based on pricing and usage assumptions.
Estimation Confidence
Confidence indicator associated with the available model, feature, and pricing information.
Estimated token metrics are estimates, not provider-reported exact telemetry. They must always be labeled as Estimated.
Cost and Engineering Impact Analysis
The module can compare AI cost and credit consumption with engineering outcome metrics.
AI Cost vs. Adoption Rate
Compares financial consumption with AI adoption.
AI Cost vs. Acceptance Rate
Compares cost with suggestion or line acceptance.
AI Cost vs. Coding Efficiency
Examines whether cost changes coincide with changes in Coding Efficiency.
AI Cost vs. Coding Impact
Compares AI spend with the scope and impact of development activity.
AI Cost vs. Code Churn
Examines whether increased AI consumption coincides with more or less short-term rework.
AI Cost vs. Pull Request Cycle Time
Compares AI spend with pull request review and merge speed.
AI Cost vs. Lead Time for Changes
Compares cost with end-to-end delivery time.
AI Cost vs. Deployment Frequency
Compares AI spend with production delivery frequency.
AI Cost vs. Change Failure Rate
Examines the relationship between AI spend and delivery stability.
AI Cost vs. Time to Restore Service
Compares AI spend with production restoration performance.
AI Cost vs. Technical Debt
Examines whether AI consumption coincides with changes in technical debt.
AI Cost vs. Code Quality
Compares AI spend with quality, security, reliability, and maintainability indicators.
Correlation does not prove causation. These analyses are intended to identify patterns that should be investigated together with team context, workflow changes, and sample size.
Document Analytics for Confluence
Document Analytics provides visibility into documentation activity, content freshness, contribution patterns, and Confluence Space health.
Metrics may be available at organization, Space, team, and contributor levels.
See Document Analytics – Confluence.
Organization and Space Overview
Total Spaces
Total number of Confluence Spaces included in the analysis.
Active Spaces
Number of Spaces with qualifying documentation activity during the selected period.
Inactive Spaces
Number of Spaces without qualifying activity during the selected period.
Total Pages
Total number of analyzed Confluence pages.
Total Contributors
Number of contributors associated with analyzed content.
Active Contributors
Number of contributors who created or updated content during the selected period.
Pages per Space
Average or total number of pages within each Space.
Contributors per Space
Number of active contributors associated with each Space.
Space Activity Score
Relative indicator representing documentation activity within a Space.
Space Ranking
Ranking of Spaces based on selected activity, freshness, or health indicators.
Documentation Activity Metrics
Pages Created
Number of pages created during the selected period.
Pages Updated
Number of unique pages updated during the selected period.
Page Update Count
Total number of page-update activities.
Content Activity
Combined view of page creation and update activity.
Page Creation Trend
Change in newly created pages over time.
Page Update Trend
Change in page-update activity over time.
Space Activity Trend
Change in documentation activity for a Space over time.
Contributor Activity
Number of page creation and update activities attributed to each contributor.
Contribution Distribution
Distribution of documentation activity across contributors.
Pages Created per Contributor
Average or individual number of pages created by contributors.
Pages Updated per Contributor
Average or individual number of pages updated by contributors.
Freshness and Documentation Health
Documentation Health Score
Composite indicator summarizing freshness, maintenance activity, contributor participation, and other configured documentation signals.
Fresh Pages
Number of pages updated within the configured freshness period.
Fresh Content Rate (%)
Percentage of analyzed pages classified as fresh.
Stale Pages
Number of pages not updated within the configured stale-content threshold.
Stale Content Rate (%)
Percentage of analyzed pages classified as stale.
Average Page Age
Average age of analyzed pages since creation.
Average Time Since Last Update
Average elapsed time since pages were last updated.
Oldest Page Update Age
Longest elapsed time since a page was updated.
Pages Needing Attention
Number of pages identified as stale, inactive, or requiring review.
Spaces Needing Attention
Number of Spaces with low activity, stale content, or other documentation-health risks.
Team and Ownership Indicators
Mapped Teams
Number of Oobeya teams associated with analyzed Confluence Spaces.
Spaces with Team Mapping
Number of Spaces connected to at least one Oobeya team.
Spaces without Team Mapping
Number of Spaces without an assigned team relationship.
Pages by Team
Documentation pages attributed to each mapped team.
Activity by Team
Documentation creation and update activity attributed to each team.
Contributors by Team
Active documentation contributors grouped by team.
Team-to-Space Relationships
Distribution of mapped team relationships across Spaces.
Documentation Coverage by Team
Visibility into whether mapped teams maintain active and current documentation.
Freshness and health results depend on the configured stale-content period and the availability of Confluence history and contributor data.
Resource Allocation
Resource Allocation shows how planned workload and engineering capacity are distributed across projects and contributors.
See Resource Allocation.
Overview Metrics
Total Projects
Number of projects included in Resource Allocation analysis.
Total Resources
Number of contributors included in the analysis.
Utilization by Effort (%)
Resource utilization calculated using estimated effort.
Utilization by Count (%)
Resource utilization calculated using work-item count.
Resource Status Distribution
Distribution of resources classified as Underutilized, Optimally Utilized, Slightly Overloaded, or Overloaded.
Planned Work Items
Number of work items assigned to a resource or team.
Delivered Work Items
Number of work items completed by a resource or team.
Planned Effort
Estimated effort assigned to a resource or team.
Delivered Effort
Estimated effort completed by a resource or team.
Team Average Capacity
Delivered work divided by the number of contributors in the team.
Resource Utilization Metrics
Resource Utilization (%)
Planned work assigned to a resource relative to the team’s average delivery capacity.
Team Utilization (%)
Aggregated utilization level of a team.
Underutilized Resources
Resources whose utilization is below the configured lower threshold.
Optimally Utilized Resources
Resources operating within the configured optimal range.
Slightly Overloaded Resources
Resources above the optimal range but below the highest overload threshold.
Overloaded Resources
Resources whose utilization exceeds the configured overload threshold.
Default calculation:
Default classification:
≤ 85%
Underutilized
85%–110%
Optimally Utilized
110%–130%
Slightly Overloaded
> 130%
Overloaded
Thresholds can be customized by administrators.
Project Allocation Metrics
Project Allocation (%)
Percentage of a contributor’s planned work assigned to a particular project.
Allocation by Work-Item Count (%)
Project allocation calculated using planned work-item count.
Allocation by Effort (%)
Project allocation calculated using estimated effort.
Projects per Contributor
Number of projects to which a contributor is allocated.
Contributors per Project
Number of contributors allocated to a project.
Allocation Distribution
Distribution of contributor capacity across projects.
Calculation:
Bug Report
The Bug Report module provides organization- and team-level visibility into open defects, severity, ownership, and bug trends.
See Bug Report Dashboard.
Open Bugs
Number of bug-type work items currently in an open status.
Open Bugs Over Time
Trend of open bugs across the selected period.
Bugs by Severity
Distribution of bugs across Blocker, Critical, High, Medium, and Low severity levels.
Blocker Bugs
Number of open bugs mapped to Blocker severity.
Critical Bugs
Number of open bugs mapped to Critical severity.
High-Severity Bugs
Number of open bugs mapped to High severity.
Medium-Severity Bugs
Number of open bugs mapped to Medium severity.
Low-Severity Bugs
Number of open bugs mapped to Low severity.
Bug Distribution by Team
Number or percentage of bugs associated with each mapped team.
Team Bugs
Bugs assigned to or associated with the selected team.
Unassigned Bugs
Bugs without an assigned owner.
Other Teams’ Bugs
Bugs visible in the current scope but associated with other teams.
Bug Status Distribution
Distribution of bugs across mapped workflow statuses.
Bug Type Distribution
Distribution across configured Bug, Defect, Problem, Incident, Error, or similar work-item types.
Bug Trend by Severity
Change in open bugs for each severity level over time.
Bug reporting depends on correct category and severity mapping in the administration settings.
Activity Heatmap
Activity Heatmap visualizes daily developer activities and helps identify workload imbalance, concentrated contribution, unusually high activity, and low participation.
See Activity Heatmap.
Activity Inputs
Commits
Number of commits made by a developer.
Lines Added
Number of code lines added.
Lines Deleted
Number of code lines deleted.
Lines Edited
Number of code lines modified.
PRs Created
Number of pull requests created.
PR Reviews
Number of code reviews performed.
PR Approvals
Number of pull requests approved.
PR Needs Work
Number of pull requests returned for changes.
PR Comments
Number of comments added to pull requests.
Each activity type can have a configurable coefficient that determines its contribution to the score.
Calculated Scores
Commit Activity Score
Weighted score generated from commits and code-change activity.
Pull Request Activity Score
Weighted score generated from PR creation, review, approval, change-request, and comment activity.
Total Activity Score
Combined weighted score across commit and pull request activity.
Daily Rank Ratio
A developer’s daily score relative to the highest-scoring developer for that day.
Daily Normalized Score
Daily activity score normalized for team comparison.
Activity Z-Score
Standardized activity score calculated from the team’s daily distribution.
Heatmap Score
Normalized 0–100 score used to determine heatmap intensity.
Developer Total Score
Total activity score attributed to a developer during the selected period.
Team Activity Distribution
Distribution of normalized activity scores across team members.
Activity Heatmap scores represent activity intensity, not work quality, value, or individual performance.
Gamification
Gamification converts selected Oobeya metrics and manually defined KPIs into configurable points, rounds, leagues, and rankings.
See Gamification.
Gamification can use metrics from:
Project Management
Development Analytics
Pull Request Analytics
Code Quality
Security
Engineering Symptoms
Manually defined KPIs
Gamification Outputs
Metric Result
Actual value of the selected engineering metric for a scoring round.
Metric Threshold
Configured value range used to assign points.
Metric Points
Points earned from a metric based on its scoring thresholds.
Manual KPI Points
Points manually entered and approved by a league referee.
Round Score
Total points earned by a team during a scoring round.
Approved Round Score
Round Score after referee review and approval.
Cumulative Score
Total points accumulated across completed rounds.
Team Rank
Position of a team based on cumulative or selected-round score.
League Ranking
Ranking of all participating teams within a league.
Maximum Available Points
Maximum points a team can earn under the configured rules.
Achievement Rate (%)
Earned points relative to the maximum available points, where displayed.
Gamification scores are derived outputs. The underlying engineering metrics retain their original definitions and units.
Team Scorecards, Developer Profiles, and Dashboards
Team Scorecards, Developer Profiles, and Custom Dashboards do not create a separate set of underlying engineering metrics.
They present and aggregate metrics from the modules listed above at different organizational levels.
Team Scorecards
Team Scorecards can include:
Development Analytics
Pull Request Analytics
DORA Metrics
Project Analytics
Code Quality
Test Analytics
Bug Analytics
AI Impact
Resource Allocation
Custom scorecard widgets
Metrics are filtered by the repositories, projects, integrations, and users mapped to the selected team.
Developer Profiles
Developer Profiles can include:
Coding Impact
Coding Efficiency
Work Type
Code Churn
Commit activity
Repository contribution
Pull requests
Code reviews
Code quality contribution
Project activity
AI usage and engagement, where available
Developer-level metrics depend on accurate identity matching across connected tools.
Engineering Insights and Symptoms
Engineering Insights and Symptoms analyze multiple metrics together to identify significant patterns, risks, improvements, and recurring engineering anti-patterns.
They are analytical outputs rather than separate raw metrics.
Examples include:
Recurring high rework
Oversized pull requests
Review bottlenecks
High cognitive load
Low Coding Efficiency
Delivery slowdown
Low Sprint Predictability
High Change Failure Rate
Unbalanced contribution
Rising technical debt
Workload imbalance
Each insight or Symptom may include:
Current Value
Metric value for the selected reporting period.
Previous Period Value
Metric value for the comparison period.
Change (%)
Percentage change between periods.
Benchmark Value
Relevant engineering benchmark or configured target.
Primary Contributor
Repository, team member, project, service, or pipeline most associated with the result.
See the Symptoms Catalog and Engineering Benchmarks.
Important Interpretation Guidelines
Use Trends, Not Isolated Values
A single value rarely provides enough context. Review:
Current value
Previous-period value
Long-term trend
Team history
Engineering benchmark
Product and workflow context
Do Not Rank Developers Using Activity Volume Alone
Metrics such as commits, code lines, pull requests, or Activity Heatmap scores should not be used alone to evaluate individual performance.
They do not directly measure:
Business value
Code quality
Task difficulty
Mentoring
Architecture work
Incident response
Collaboration outside tracked tools
Product discovery
Customer impact
Correlation Does Not Prove Causation
When two metrics move together, use language such as:
Appears to be associated with
Coincides with
May be contributing to
Is concentrated in
Should be investigated together with
Avoid assuming that one metric directly caused another without supporting evidence.
Validate Data Configuration
Unexpected metric results are often related to:
Incorrect user or contributor mapping
Missing repositories
Incorrect branch selection
Incorrect production pipeline mapping
Missing incident sources
Incorrect workflow status mapping
Excluded work-item types
Missing effort values
Incorrect team membership
Missing Confluence Space mappings
Missing AI product, model, or cost-center mappings
Related Documentation
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