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Insights V3 incorporates the Average Wait Time for First Review metrics to provide insights into the duration it takes for pull requests to receive their first review.
The Average Wait Time for First Review refers to the average time it takes for pull requests to receive their first review after being opened. It measures the time span between the creation of a pull request and when it receives its initial feedback or review.
In the vertical bar chart, each bar displays the Average Wait Time for 1st Review with x-Axis showing the date and the y-axis showing "Time in Hours".
The Average Wait Time for a selected time period can be computed by summing up the time it took for the first review for all PRs, divided by the number of pull requests and results displayed in minutes/hours/days.
Each data point on the chart represents the average wait time for the first review during that specific time period.
Code Quality and Bug Resolution: Longer wait times may delay the identification and resolution of code issues or bugs, potentially affecting the overall quality of the software.
Faster Development Cycle: Reducing the wait time for the first review contributes to a faster development cycle. This allows projects to deliver new features, bug fixes, or improvements in a timely manner, increasing the project's overall efficiency.
Collaboration and Iteration: The Average Wait Time for First Review metric directly impacts collaboration and iteration among contributors. Timely feedback on pull requests allows contributors to address issues or make improvements promptly.
A Velocity dashboard in Insights V3 is a visual representation that provides insights into the development team's velocity. Velocity refers to the rate at which the team completes work or delivers features over a specific period. This dashboard tracks and measures the team's productivity and progress.
It typically displays key metrics related to the team's velocity, such as Performance Metrics, Lead Time, Average Review Time, Average Wait Time for First Review, and Code Engagement.
A Velocity dashboard can help project managers and stakeholders understand the team's capacity and performance over time. The dashboard is useful to project managers, leads, development teams, and stakeholders in several ways:
The dashboard helps project managers estimate the team's capacity, progress, and performance over time. It provides insights into the team's historical productivity and better resource allocation.
The velocity dashboard allows development teams to assess their own performance and productivity. They can track their progress, identify patterns, and improve their estimation accuracy by comparing planned work with actual velocity.
The velocity dashboard provides stakeholders with visibility into the progress and productivity of the development team. It enables you to track the status of deliverables, understand the team's capacity, and make informed decisions based on real-time data.
Overall, a velocity dashboard is a useful tool for project management, performance evaluation, collaboration, and decision-making, benefiting all stakeholders involved in the open source software development process.
Lead time metric measures the average time between the time when a Pull Request is raised to the time it is merged.
It shows the entire lifecycle of a pull request, including the PR raised> Review started>PR accepted>PR merged.
The Lead Time metric can be effectively visualized using box plots. Box plots can provide a visual representation of the distribution of lead times.
Project Efficiency: you can analyze the complete PR review cycle Lead Time that provides efficiency in the software development process. By analyzing the time it takes for code changes to move through the development pipeline, project managers can identify delays or inefficiencies.
Quality Assurance: Lead time can provide insights into the quality assurance process. Longer lead times may indicate delays in testing or quality assurance activities, potentially leading to issues and bugs reaching production.
Insights V3 uses the Average Review Time by Pull Request Time metric to provide insights into the duration it takes for pull requests to be reviewed.
Average Review Time by Pull Request Time refers to the average duration it takes for pull requests to be reviewed by peers or project maintainers. It measures the time span between the creation of a pull request and when it receives thorough review feedback.
The chart consists of 5 bars, each of a different color. Each bar displays the average lead time in hours or days for pull requests based on the size of the request.
We have 5 buckets of Pull Request Sizes. They are:
1-9 lines
10-49 Lines
50-99 Lines
100-499 Lines
500+ Lines
Pull Request Size is computed by Lines "Changed". Lines changed could be lines of code added, deleted, or updated.
The length of the color inside the bar is determined by the average Review time. i.e., the longer it takes, the longer the length of the color inside the bar.
Code Quality Assurance: The metric helps you monitor the speed at which pull requests are reviewed. By minimizing the average review time, you can enhance the chances of identifying and resolving code issues promptly, resulting in higher code quality and overall project success.
Collaboration and Engagement: Prompt review feedback encourages active collaboration among contributors. It helps to maintain a responsive and interactive process. When pull requests receive timely reviews, contributors can address feedback and iterate on their code changes faster.
Project Velocity: Timely code reviews contribute to higher project velocity. The Average Review Time metric provides insights into the responsiveness of the review process, identifying areas for improvement. Minimizing review times helps ensure that code changes are integrated swiftly, allowing projects to deliver new features or updates faster.
Insights V3 uses the Average Lead Time by Pull Request Time metric to provide insights into the time it takes for pull requests to be completed.
Average Lead Time by Pull Request Time refers to the average duration it takes for pull requests to progress from opening to merging. It measures the time span between the creation of a pull request and its successful inclusion into the project's codebase.
The chart consists of five bars, each of a different color. Each bar displays the average lead time in hours/days for pull requests based on the pull request size.
We have five buckets of Pull Request Sizes. They are:
1-9 lines
10-49 Lines
50-99 Lines
100-499 Lines
500+ Lines
Pull Request Size is computed by Lines Changed. Lines changed could be lines of code added, deleted, or updated.
The length of the color inside the bar is determined by the average lead time. i.e., the longer it takes, the longer the length of the color inside the bar.
It is the "Average" Lead Time, so compute the average lead time for all PRs for a certain size and display the lead time in minutes/hours/days
Workflow Efficiency: This metric provides valuable insights into the efficiency of the pull request workflow. Optimizing the lead time results in a faster integration of code changes and promotes collaboration among contributors.
Collaboration and Feedback: It reflects the speed at which contributors receive feedback on their code changes. A shorter lead time indicates a more responsive review process, encouraging contributors to engage actively..
Project Velocity: Monitoring the average lead time enables project managers to assess the overall project velocity. A shorter lead time helps maintain a high project velocity, ensuring rapid innovation and faster delivery of software features.
In Insights V3, the Code Review Engagement metric assesses the level of involvement and participation in code review activities.
Following are the various factors that are considered in the Pull Request review process:
Number of Pull Request Participants
Pull Requests reviewed
Review comments for Pull Request
Code reviews
Process Improvement: Tracking the Code Review Engagement metric over time allows you to assess the effectiveness of code review processes and identify areas for improvement. Continuous improvement of the code review process leads to higher-quality code and improved productivity.
Quality Assurance: Code review plays a vital role in ensuring code quality and identifying potential issues or bugs. By tracking this metric, managers can identify areas where additional attention or improvement may be needed to maintain high code quality standards.
Insights V3 incorporates a Performance Metric to provide insights into key performance indicators such as time to merge pull requests, build frequency, and build failure rate.
The dashboard presents this information using a bar chart, allowing you to visualize and analyze these performance metrics over a selected time period.
The dashboard presents this information using a bar chart, allowing project managers to visualize and analyze these performance metrics over a selected time period.
Time to Merge Pull Requests: Evaluate the average time taken to merge pull requests. Identify any significant variations or trends in the time-to-merge, which can indicate potential inefficiencies in the code review and merge processes.
Build Frequency: Assess the frequency of software builds. A higher build frequency signifies more frequent integration of code changes and adherence to continuous integration practices. A consistent and regular build schedule ensures rapid feedback and promotes collaboration among contributors.
Build Failure Rate: Analyze the percentage of build failures. Higher build failure rates indicate issues in the build process, such as compilation errors, test failures, or compatibility issues. Identifying and addressing these failures promptly ensures a more stable and reliable software product.