How to Analyze Slow Pull Requests with devActivity Retrospective Insights
devActivity's Retrospective Insights empower your team to move beyond guesswork and make data-driven decisions to optimize your development workflow. This guide will walk you through how to effectively use the 'Merged Pull Requests with Cycle Time above the Average' feature to identify, analyze, and ultimately improve your team's pull request process.
By following these steps, you can rapidly pinpoint areas of inefficiency and implement targeted improvements, leading to faster delivery and a more efficient development cycle.
Access Retrospective Insights
Navigate to the Analytics section in your devActivity dashboard and select Retrospective Insights. This will bring you to the overview page for your chosen period.
Identify Anomalous Pull Requests
Within the Retrospective Insights details, locate the table titled 'Merged Pull Requests with Cycle Time above the AVG'. This table automatically highlights all merged pull requests that have taken significantly longer than your team's average cycle time.
Examine Cycle Time Breakdown
For each identified pull request, pay close attention to the columns:
- TO AVG: This shows exactly how much longer this PR took compared to the average. A large number here indicates a significant anomaly.
- CODING: The time spent in the initial coding phase.
- PICKUP: The time the PR waited to be picked up for review. High values here often signal a bottleneck in review assignment or availability.
- REVIEW: The time spent actively reviewing the PR. Prolonged review times might suggest complex changes, extensive feedback loops, or reviewer capacity issues.
This breakdown is crucial for understanding where the delays occurred.
Investigate Specific PRs
Click on the PULL REQUEST title in the table to navigate directly to the original pull request in your Source Code Management (SCM) system (e.g., GitHub). This allows you to delve into the context, comments, and changes to understand the qualitative reasons behind the prolonged cycle time.
Formulate Actionable Insights
Based on your analysis, identify patterns or recurring issues. For example:
- If many PRs have high 'PICKUP' times, consider strategies like dedicated review rotations, clearer notification systems, or limiting work-in-progress.
- If 'REVIEW' times are consistently high, explore options like smaller, more focused PRs, early code quality checks, or pair programming.
Use these data-driven insights to prepare for and conduct focused retrospective meetings, leading to targeted process improvements and increased team efficiency in future development periods.
Frequently Asked Questions
How can I use this data in retrospective meetings?
The insights from this view provide concrete examples of PRs with prolonged cycle times, allowing your team to have data-driven discussions about specific process issues and brainstorm solutions.
What does a high 'PICKUP' time indicate?
A high 'PICKUP' time often suggests delays in a pull request being picked up for review. This could be due to reviewer availability, prioritization issues, or a lack of clear notification systems.
How can we reduce our team's overall cycle time?
By consistently analyzing anomalous PRs and addressing the identified bottlenecks (e.g., improving review processes, optimizing PR size, enhancing communication), your team can incrementally reduce overall cycle time.
Does devActivity integrate with my SCM for this data?
Yes, devActivity integrates with popular SCMs like GitHub, GitLab, and Bitbucket to automatically collect the necessary data for cycle time analysis.
