Get instant GitHub repo analytics and compare their performance to the top 100 most starred, forked, and active GitHub repositories.
Includes: Average Issue Completion, PR Throughput, Cycle Time, Code Review Time & more.
*Requires GitHub authentication
The free analytics tool for GitHub provides instant metrics from the last 4 weeks of collaborating with your team in a repository. The tool benchmarks your team’s activity against live data from the top 100 most starred, forked, and active open-source GitHub repositories, so you can better compare your performance. There is no need to sign up for a Zenhub account, but GitHub authentication is required if you are analyzing a private account.
What it is: The tool calculates average issue completion by taking the number of issues completed per week over the last 4 weeks and averages them to provide you with the average weekly completion rate per team and per developer.
How to use it: You can use average issue completion to estimate future sprints and project completion and to benchmark your team’s performance in the future.
What it is: Average cycle time is how long it takes to complete an issue, from issue creation to when it’s closed. We use data from your team’s last 4 weeks of collaboration to calculate the average cycle time of your team and the average time per developer (not specific to an individual) in days.
How to use it: Cycle time is a valuable metric for estimating the time you can expect issues to be completed, which can be used in sprint planning and release planning.
What it is: Code review time is exactly what it sounds like – how long it takes the team to review new code. This is calculated by looking at the amount of time from when a PR is open to when it is closed, measured in days.
How to use it: Knowing your team’s average code review time helps plan sprints and releases and identify QA resource gaps.
What it is: PR throughput refers to the average weekly number of pull requests merged.
How to use it: Knowing your team’s average PR throughput can be helpful for comparing against other metrics like issue completion to identify resource and process gaps, benchmarking, and estimating your team’s performance over time.
What it is: The overall project score is a weighted average score given to your repo and scored out of one hundred. The score uses data collected from the top 100 most starred, forked, and active GitHub repositories to give you a general idea of how productive your team is in comparison.
How to use it: You can use this score to get a general “health check” of how well your team performs compared to some of the most productive teams in GitHub. And, believe us, when you’re team performs well, the bragging rights are priceless (we recommend posting on social!).
The first step to improving your score is knowing your score. Once you’ve put your repository through the grader to receive a benchmark of where your team is currently at, you can begin to identify gaps in your team. Click the “see how you compare” button to see which metrics your team is falling short on. For example, perhaps you have a longer than ideal review time – in this case, it may be worthwhile to revisit your team’s QA processes.
Try identifying specific metrics your team can improve and then test out new processes to help improve them. Then, after another 4 weeks, run your repository through the grader again to see if there was an improvement.
For more in-depth reporting, we recommend using a tool like Zenhub reports which automatically generates reports using data synced in real time with your GitHub account. These reports provide more context into how your team is doing by using your team's entire historical sprint data and graphing it on various charts to help you better pinpoint gaps.
Reports available in Zenhub include cumulative flow charts, control charts, burndown reports, velocity tracking, and release reports.
There are two parts to this question. First, the GitHub part – since GitHub is a codebase, it's where all of the most up-to-date project data is, so when you analyze a GitHub repository, you're getting the most accurate analytics. Second, the value in gathering metrics in the first place is to get a better understanding of how your team works. Collecting this data allows you to look closer at your processes, understand when strategies are working and when they aren't, and see over time if performance is improving, consistent, or worsening.
Analytics can only be properly understood by having accurate benchmarks as reference points. When teams have yet to collect historical sprint data and cannot benchmark their current performance against their previous performance, it is difficult to interpret the current data. This is why being able to compare your team's stats to that of other teams is valuable context.
Additionally, when your team does have historical data, it may still be helpful to use benchmarks from top-performing teams to see how close your team comes to being high-performing.
*Requires GitHub authentication
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