Aug 8, 2023
Contents:
Why GitHub + Screenful?
By integrating with GitHub, Screenful offers valuable insights into your software development process. With Screenful, you can:
Get out-of-the-box insights into your draft issues, issues, and pull requests
Slice and dice your data by any of your custom fields
Create custom charts, including advanced ones like Scatter plot, Burndown chart, Table chart, Workload (planned), and Forecasting chart
Forecast your delivery dates using historical data
How to set up the integration
Setting up the Screenful GitHub app is easy. Just follow these steps:
Go to the Analytics & Reports App page and click Install
Create and register the GitHub App
Import repositories and projects (classic or new) as data sources
Your setup is complete, and you can navigate to the Insights tab to see the automatically generated charts and metrics. If you have added custom fields to your boards, they are automatically imported and made available for charts.
Creating custom charts
To create a custom chart, go to the Charts tab and click Add new chart in the top right corner.
A modal is opened with a set of chart templates. You can create a new chart from scratch or pick one of the predefined charts.
When you click Create chart, the chart is created, and you can find it under the Charts tab under the main navigation.
From there, you can assign it to a dashboard or a report, or share with others using the share links.In addition to the common ones like line, bar, and pie charts, we also have more advanced charts such as the Scatter plot, Burndown chart, Table chart, Workload (planned), and the Forecasting chart.
Forecasting your delivery dates
As a project manager, the question you often hear from stakeholders is when will it be done? It’s an easy question to ask but much harder to answer with any level of certainty.
The traditional approach is to detail out all requirements and estimate each task in story points or hours/days, and then use that data to come up with the project timeline. The problem is that people, in general, are not very good at estimating how long some piece of work takes.
Fortunately, there is an alternative approach, which is based on looking at your historical data and extrapolating that to your future work.
Here’s a chart that shows how much work a team has completed per week during the last 3 months:
If team composition is the same for the remainder of the project, wouldn’t it be helpful to use this information to estimate how long it takes to complete the remaining work?
A data-driven approach looks at your historical throughput and makes a prediction based on that. Here’s an example of such a forecast using the Forecasting chart
This chart looks at the weekly history of the team’s throughput and creates three scenarios based on the distribution of the work completed per week:
Optimistic: 80th percentile
Most likely: 50th percentile
Pessimistic: 20th percentile
The optimistic scenario expects that your team will complete more work than in 80% of the past weeks. In the most likely scenario, your team completes the equal amount as what is the median week. In the pessimistic scenario, the team completes only as much as 20% of the past weeks.
You can learn more about the Forecasting chart from this how-to guide.
Sharing with reports
As a project manager, the question you often hear from stakeholders is when will it be done? It’s an easy question to ask but much harder to answer with any level of certainty.
The traditional approach is to detail out all requirements and estimate each task in story points or hours/days, and then use that data to come up with the project timeline. The problem is that people, in general, are not very good at estimating how long some piece of work takes.
Fortunately, there is an alternative approach, which is based on looking at your historical data and extrapolating that to your future work.
Here’s a chart that shows how much work a team has completed per week during the last 3 months:
If team composition is the same for the remainder of the project, wouldn’t it be helpful to use this information to estimate how long it takes to complete the remaining work?
A data-driven approach looks at your historical throughput and makes a prediction based on that. Here’s an example of such a forecast using the Forecasting chart
This chart looks at the weekly history of the team’s throughput and creates three scenarios based on the distribution of the work completed per week:
Optimistic: 80th percentile
Most likely: 50th percentile
Pessimistic: 20th percentile
The optimistic scenario expects that your team will complete more work than in 80% of the past weeks. In the most likely scenario, your team completes the equal amount as what is the median week. In the pessimistic scenario, the team completes only as much as 20% of the past weeks.
You can learn more about the Forecasting chart from this how-to guide.
Let us know if you have questions or feedback by contacting hello@screenful.com. To stay on the loop, read our blog, or follow us on LinkedIn.
