By measuring certain metrics during code reviews, teams can identify areas for improvement and enhance their overall code review process. This guide will focus on those key metrics, how to measure them, and how to use tools like Graphite Insights for better visibility and analysis.
Key code review improvement metrics
Measuring the effectiveness of code reviews can be a daunting task, especially when teams lack structured metrics to guide their evaluations. Without specific metrics, organizations face several challenges that hinder their ability to assess the quality and efficiency of their code review processes, including a lack of visibility and difficulty in measuring the impact on code quality. To avoid this, here's a list of those important key metrics for you to track:
- Time to review
This metric measures the average time taken from the creation of a pull request (PR) to its review completion. Tracking this metric helps identify bottlenecks in the review process. For example, if a team has a goal to complete reviews within 48 hours, tracking this metric can help assess if they consistently meet that target.
- Review velocity
This measures the number of pull requests reviewed over a specific period. It helps gauge the team's productivity and can highlight trends in review workloads. If a team reviews an average of 10 PRs per week, tracking this over time can help understand if they are increasing or decreasing in their review capabilities.
- Code churn
Code churn refers to the percentage of a codebase that is modified shortly after it has been added. High churn rates might indicate unclear requirements or excessive back-and-forth in the review process. For example, if a file is modified within a week of being reviewed, it may signal that further clarification or a different approach is needed.
- Approval rate
This metric tracks the percentage of pull requests that get approved versus those that are rejected or require changes. A high approval rate may indicate a clear understanding of the codebase and good collaboration. Consider this: if out of 100 PRs, 85 are approved, the approval rate is 85%. Tracking this can provide insights into team collaboration and reviewer effectiveness.
- Post-review defect rate
This measures the number of defects found in the code after it has been reviewed. A lower defect rate indicates that the review process is effective. For example, if a team finds 5 bugs out of 100 reviewed PRs after merging, the post-review defect rate is 5%.
Measuring code review effectiveness
By implementing a metrics-driven approach to code reviews, teams can overcome the challenges that a lack of insights presents. So, now that we've covered the metrics to track, let's take a look at how to effectively measure them. Here’s how:
Manual tracking with spreadsheets: Teams can start by logging key metrics like review response times, number of review cycles, and PR merge times in a shared spreadsheet. This allows for simple data collection and analysis, especially for smaller teams or those new to tracking metrics.
Automated tools: Using tools like GitHub's insights or Graphite Insights, teams can automatically gather metrics such as the number of PRs merged, average review time, and the number of reviews per team member. These tools reduce manual effort and provide real-time data for immediate analysis.
Custom dashboards: Create custom dashboards using tools like Graphite Insights or data visualization platforms (e.g., Tableau or Power BI). These dashboards can display key performance indicators such as team velocity, average PR size, and time-to-merge, giving teams a holistic view of their code review process.
Key takeaways
Tracking metrics for continuous code review improvements is essential for enhancing the effectiveness of the review process. By measuring key metrics such as time to review, review velocity, and post-review defect rates, teams can gain insights into their workflows and identify areas for improvement. Tools like Graphite Insights can help visualize these metrics and enable data-driven decisions that lead to continuous improvements in code quality.
For further reading on best practices for code reviews, check out Graphite's best practices for code reviewers, Google's guide to code reviews, or GitHub's best practices for code review.
