The Benchmarks Dashboard provides a snapshot of your team’s performance across key metrics tracked by DevStats. Each metric includes a gauge visualization to help you quickly assess how your team is doing, categorized into performance tiers such as Elite,Strong,Fair, or Needs Focus. Below is a breakdown of each benchmark and guidance on what the results mean, along with links to the relevant detailed reports for deeper insights.
The Benchmarks Dashboard visually presents key performance indicators for a development team using a set of gauge-style charts. Each chart focuses on a critical metric, such as PR Cycle Time,Issue Cycle Time,Planning Accuracy,Change Failure Rate,Code ReviewSize,DeploymentFrequency, PRs Merged, Issues Resolved, Days With Commits, and Innovation Investment, with clear indicators of performance tiers like Elite, Strong, Fair, and Needs Focus.
Metrics
PR Cycle Time
What it measures: The total time it takes for a pull request to move from creation to deployment.
What to look for:Elite: Less than 42 hours (high efficiency).Strong: 42–95 hours.Fair: 96–188 hours.Needs Focus: Over 188 hours (indicates bottlenecks in coding, review, or deployment stages).
What it measures: The time it takes to complete a task once it's actively being worked on.
What to look for:Elite: Less than 2 days.Strong: 2–5 days.Fair: 5–9 days.Needs Focus: Over 9 days (suggests delays in execution or unresolved blockers).
What it measures: The average number of lines of code included in pull requests under review.
What to look for:Elite: Less than 105 lines (small, manageable PRs).Strong: 105–155 lines.Fair: 155–229 lines.Needs Focus: Over 229 lines (large PRs may lead to slower reviews and higher defect rates).
What it measures: How often your team deploys code to production.
What to look for:Elite: Daily or more frequent deployments.Strong: At least once per week.Fair: Less than one per week.Needs Focus: Fewer than one deployment per sprint (slows delivery of value to users).
How many pull requests each developer merges per week. This metric reflects throughput and the team's ability to deliver incremental changes consistently.What to look for:Elite: ≥ 4 merges per week per developer.Strong: 3 – 3.9 merges.Fair: 1.5 – 2.9 merges.Needs Focus: < 1.5 merges (indicates slow throughput and potential bottlenecks).Learn more: Throughput.
Issues Resolved
What it measures:
How many issues (tasks) each developer completes per week. This indicates delivery pace, execution reliability, and overall flow of work.What to look for:Elite: ≥ 4 issues per week per developer.Strong: 3 – 3.9 issues.Fair: 1.5 – 2.9 issues.Needs Focus: < 1.5 issues (signals slow progress and backlog accumulation).Learn more: Productivity.
Days with Commits
What it measures:
The number of days per week in which a developer actively commits code. This measures consistency, flow of work, and the stability of delivery habits.What to look for:Elite: 4.5 – 5 days with commits per week.Strong: 3.5 – 4.4 days.Fair: 2.5 – 3.4 days.Needs Focus: < 2.5 days (indicates interruptions in flow and irregular development cadence).Learn more: Productivity.
Focus on a particular squad to analyze their benchmarks individually. This filter is useful for identifying team-specific strengths or areas for improvement.
Select a specific time period to analyze performance benchmarks over days, weeks, or months. Use this filter to track trends or evaluate specific timeframes, such as the last sprint or quarter.
The Players Filter allows you to focus on specific team members within your squad. Use this filter to evaluate individual contributions to key metrics, such as PR Cycle Time or Code Review Size. This is particularly helpful for understanding how individual performance aligns with team goals or identifying opportunities for coaching and support.
The Repository Filter enables you to analyze benchmarks for a specific repository or across multiple repositories. This filter is essential when working on projects with distinct codebases, allowing you to assess how performance varies between repositories and optimize workflows for each codebase.
Use the Data Source Filter to refine deploy metrics based on the origin of the data, whether from a Git provider or a project management tool.
Git provider: Filters deploy metrics sourced from version control systems, such as code commits and pull requests.
Project management: Filters deploy metrics sourced from project management tools.
Conclusion
The Benchmarks Dashboard provides a powerful snapshot of your team’s overall performance across critical metrics, such as PR Cycle Time, Planning Accuracy, and Deployment Frequency. By offering a consolidated view, this dashboard allows you to quickly identify strengths and areas for improvement. Using the provided filters, you can tailor the view to focus on specific squads, branches, or time periods, making it an essential tool for continuous improvement in software delivery practices.
