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Get a comprehensive overview of the Productivity dashboard, a key feature in the open source project analytics tool, and learn how to use it to enhance efficiency and measure productivity.
The Productivity page in the open source project analytics tool provides a centralized hub for monitoring project progress, identifying bottlenecks, and optimizing workflows. This dashboard offers a visual representation of essential metrics, including:
Commits per active day: Track the number of commits made by contributors on a daily basis.
New contributors: Identify new contributors and their level of engagement.
Drifting away contributors: Monitor contributors who have become less active in the project.
Engagement gap: Analyze the gap between contributors' expected and actual engagement levels.
Work time distribution impact: Understand how contributors spend their time on the project.
Effort by pull request size: Visualize the effort required for each pull request based on its size.
Key Benefits:
Real-time information: Stay up-to-date with the latest project metrics and trends.
Actionable analytics: Make informed decisions to improve collaboration and deliver high-quality software.
Data-driven decisions: Empower your open source project with data-driven insights to drive success.
Next Steps:
Explore the Productivity dashboard to better your project's performance.
Use the metrics and insights provided to identify areas for improvement and optimize your workflows.
Learn about the New Contributors Dashboard, a valuable feature in the open source project analytics tool, and discover why it's essential for the long-term sustainability and success of your project.
What is the New Contributors Dashboard?
The New Contributors Dashboard is a powerful tool that analyzes the participation of new contributors in your open source project. It provides a leaderboard that ranks new contributors based on their contributions over a selected period, giving you a clear picture of their involvement and impact.
Why is this metric important?
The New Contributors Dashboard is crucial for several reasons:
Sustainability and Succession Planning: As existing contributors move on or take on different responsibilities, new contributors help fill the gaps, ensuring the long-term sustainability of your project.
Fresh Perspectives and Ideas: New contributors bring diverse skill sets, innovative solutions, and fresh ideas to the project, contributing to its evolution and growth.
Key Benefits:
Identify and recognize new contributors who are making significant contributions to the project.
Understand the impact of new contributors on the project's growth and development.
Make informed decisions to encourage and retain new contributors, ensuring the project's long-term success.
Next Steps:
Explore the New Contributors Dashboard to analyze the participation of new contributors in your project.
Use the insights gained to develop strategies for retaining and engaging new contributors, ensuring the project's continued success.
The Commits per Active Day Dashboard provides insights into code commit frequency on active development days. It measures the average number of code commits contributors to make on active development days.
To calculate the Commits Per Active Day metric:
Identify Active Days: Count the number of days within a specified period where at least one commit was made.
Count Total Commits: Sum the total number of commits made within that period.
Calculate Average: Divide the total number of commits by the number of active days.
For example, if in a month there are 300 commits made over 20 active days, the Commits Per Active Day would be:
Commits Per Active Day = 300/15 = 20 days
This means, on average, there were 15 commits made on each day that had at least one commit.
Early Issue Detection: A higher number of commits per active day increases the likelihood of early issue detection. Regular code commits to provide more opportunities for contributors to identify potential issues or bugs during the development process.
Code Quality and Stability: A consistent number of commits indicates ongoing code enhancements and maintenance, leading to improved code quality over time.
Productivity Assessment: A higher number of commits per active day suggests that contributors are actively working on code changes, implementing new features, fixing bugs, and making improvements.
The Engagement Gap metric measures the difference between expected and actual levels of contributor engagement. The dashboard shows the ratio of the difference between the contributor who comments the most over PRs vs. the contributor who comments the least.
To illustrate the importance of the Engagement Gap Metric, consider the following example:
For an open-source project with 100 contributors but only 20 actively engaged users (e.g., responding to issues, contributing code, or participating in discussions), there is an engagement gap of 80%. This may indicate a lack of community involvement.
Performance Assessment: The Engagement Gap metric enables you to assess the project's overall engagement level by comparing it to the expected or desired level. It provides a quantitative measure of how actively contributors are participating and helps identify any gaps between the expected and actual engagement levels.
Community Health: The Engagement Gap metric provides valuable insights into the health and dynamics of the project community. Large engagement gaps may indicate potential challenges, such as communication issues, a lack of mentorship, or unclear contribution guidelines.
Targeted outreach: By analyzing the engagement gap, project maintainers can focus their outreach efforts on users who are already engaged with the project, increasing the likelihood of retaining their interest and encouraging continued participation.
How to Improve Engagement Gap?
Set Clear Expectations: Clearly define the expected engagement levels for each project to provide a benchmark for comparison.
Encourage Collaboration: Foster a culture of collaboration within your team by encouraging open communication and sharing of ideas.
Provide Feedback: Regularly review the Engagement Gap metrics with your team and provide feedback on areas that need improvement.
Recognize Achievements: Acknowledge and reward team members who actively contribute to reducing the Engagement Gap, motivating others to follow suit.
The Effort By Pull Request Batch Size metric analyzes the relationship between the size of pull requests (measured by lines of code changed) and the time contributors spend reviewing and merging them.
Here are ways you can interact with the chart to gain deeper insights:
Filter by Date Range: This allows users to analyze the metrics across different periods to observe how the trends have evolved.
Compare Trends: The chart compares previous periods, enabling you to spot differences or improvements.
The metric shows the distribution of pull requests across different size categories, from "Very Small" to "Gigantic."
This information can help identify potential bottlenecks or areas where the team may need additional support or process improvements.
The metric tracks the average time required for reviewing and merging pull requests in each size category.
Longer review and merge times for larger pull requests may indicate a need for better code organization, more thorough review processes, or additional resources.
The metric includes information on the number of participants and comments associated with each pull request size category.
Higher numbers of participants and comments for larger pull requests suggest increased collaboration and coordination efforts, which can be both positive (better code quality) and negative (potential delays or inefficiencies).
Development Cycle Time: The Effort By Pull Request Batch Size metric provides insights into the overall development cycle time. By analyzing the relationship between batch size and effort, you can identify trends that affect the time taken to review and merge pull requests.
Review Efficiency: The Effort By Pull Request Batch Size metric helps project managers evaluate the efficiency of the pull request review process. By analyzing the effort required for different batch sizes, You can identify patterns and trends that impact the speed and quality of reviews.
Q: What does the Effort By Pull Request Batch Size metric measure? A: It measures the relationship between the size of pull requests, in terms of lines of code changed, and the amount of time contributors spend reviewing and merging them.
Q: Why is it important to analyze the Effort By Pull Request Batch Size metric? A: Understanding this metric helps optimize the review process by identifying the most efficient batch sizes for pull requests, thus reducing review time and improving workflow efficiency.
Q: How can I interact with the chart to get more insights? A: You can filter the analysis by date range to observe trends over time and compare these trends with previous periods to identify improvements or regressions in efficiency.
Q: Can analyzing this metric reveal trends over specific periods? A: Yes, by filtering the data by specific date ranges, it's possible to observe how the trends in pull request batch sizes and review times have evolved, helping teams adapt their strategies accordingly.
The Work Time Distribution Impact Dashboard analyzes how contributors allocate their work time and the impact of different activities on project progress.
The chart shows the trends of commits and finds patterns if long hours, non-business hours, or weekends have contributed to burnout. Burnout can be thought of as a lower number of commits over a long period, before which there was heightened activity (commits).
The purpose of the chart is to find out if there is a risk of burnout among contributors due to long hours.
The chart displays the distribution of work time among contributors, highlighting the impact of different activities on project progress. The chart shows the following:
Commits: The number of commits made by contributors over a specified period.
Time of Day: The time of day when commits were made, categorized into business hours, non-business hours, and weekends.
Vertical Bar Chart: The chart displays a vertical bar chart showing the number of commits made during different time periods.
The Work Time Distribution metric is calculated by analyzing the time of day when commits were made. The metric is calculated as follows:
Time categorization: Commits are categorized into business hours, non-business hours, and weekends.
Counting commits: The number of commits made during each time period is counted.
Calculating percentage: The percentage of commits made during each time period is calculated based on the total number of commits.
Workload Distribution: Monitoring the Work Time Distribution Impact helps identify potential workload imbalances among contributors. If one or a few contributors are consistently spending a disproportionate amount of time on specific activities, it can lead to burnout or reduced productivity.
Performance Evaluation: The Work Time Distribution Impact metric can contribute to performance evaluation and feedback processes. By analyzing how contributors allocate their work time, project managers can identify patterns of efficiency or areas that require improvement.
The Drifting Away Contributors metric focuses on identifying contributors who were once active in an open-source project but have gradually become less engaged over time.
This chart is not impacted by time filter changes. That means the data will always show with respect to "today".
Drifting Away Contributors are:
Users who made at least 5 code contributions at all times for the project.
At least one of those contributions must have been made in the last 6 months.
The contributor disappeared over the last 3 months.
The Drifting Away Contributors metric is essential for maintaining a healthy and active contributor community. By identifying contributors who are gradually becoming less engaged, project managers can take proactive measures to understand their reasons for disengagement and find ways to re-engage them.
