Using AI for customer service in your company is a definite method to save time and money. If you’re like most business owners, you’re constantly searching for fresh, creative ways to improve your enterprise. We’re here to inform you that improving AI customer service is a simple and rapid win.
If you have a team of software engineers and want to move them to Mac, you will need to consider a number of things before you do so.
So, how’s your team doing? How much better should it be doing? It’s nice to have performance metrics for your software development team, but it only gives you a one-sided view. The ability to readily compare performance by team or project with others provides you with more actionable insight on what your team can improve upon. Let’s take a look at what Pluralsight Flow and Gitential have to offer when it comes to team and project benchmarking.
Pluralsight is a really good resource for software developers to skill up on their coding, but Pluralsight Flow is for team development. But, the only benchmarking mentioned by Pluralsight Flow is the “Industry Benchmarks” article in the support part of their website.
In their own words:
“Industry benchmarks are reference points based on the software development industry. Use industry benchmarks to see how your team compares with the industry. This can help you and your team identify potential areas of growth.”
Where does this data come from? They reference having analyzed 7 million commits by about 88k software engineers throughout 2016. Their analysis examined four main metrics: Active Days per Week, Commits per Active Day, Impact, and Efficiency.
Ok, so you can compare your teams against the industry in the mentioned above areas.
But how can you verify what’s going on inside your own company? Do you have to collect your metrics scores per team and upload them to an excel table to actually get some actual insights?
There’s a review and collaboration package providing three sets of metrics: Submit, Review, and Team Collaboration, effectively defined by the following metrics:
Submit
Review
Team Collaboration
Data sets are interesting but is there any comparison on company level included? Not really.
Gitential.com provides you with an actual Benchmarking feature that calculates your company average and compares projects and teams against it. But also, you can compare projects and teams between them. Just pick whatever project, teams and metrics you’d like to verify and enjoy the instant, custom comparison solution.
Top level:
Mid-level:
Low level:
You can use Benchmarking for both your Projects and your Teams.
You can adjust the time range for the analysis, as well as filter out specific data (e.g. filter on just a couple of projects or specific repositories only).
Currently, there are six main types of reports that you can automatically generate for your projects. These include:
We’ll show you below how simple it is to generate each of them.
1. Project vs Baseline Average chart
It compares the project of your choice against the average scores of the chosen metric of all your projects.
Pick the project you want to compare
Pick the metric you’re interested in
Get instant results
2. Projects Comparison
This chart shows the progress of all your projects according to the chosen metric, also against the baseline average which is marked by the light blue chart background
For the comparison, you can pick any metric you need
The calculations are immediate
3. Coding Productivity by Project
It compares the coding productivity of all your projects in one chart.
Understand the coding behavior of your projects. Which projects are the most productive in code writing and commit activities? The size of the buggle represents the total Code Volume contributed to the particular project.
4. Deployment Productivity by Project
It compares deployment productivity of all your projects in one chart.
Understand the deployment performance of your project based on how fast they are releasing new features.
The size of the bubble is the number of pull requests merged.
Coding Optimized: Projects being fast in both coding and pull request review process. Optimal team with optimized processes. Use them as an example to grow.
Juniors: They have a fast PR Review process but they are weaker in code writing.
Agile: Fast committers but they have room to improve their PR and review activities.
Process Optimized: They need assistance to improve their coding skills and set up KPIs to their PR and review processes.
5. Project Commit Activity
In this chart, you can compare your projects side by side, and metric by metric, based on commit activity.
Each of the metrics can be sorted by ascending or descending data. Just click on the metric to reverse the values. The small percentages below the results are indicators of the current scores vs scores of the previous period, according to the scale below:
6. Project Pull Request Activity
In this chart, you can compare your projects side by side, and metric by metric, based on pull request activity.
Each of the metrics can be sorted ascending or descending. Just click on the metric to reverse the values. The small percentages below the results are indicators of the current scores vs scores of the previous period, according to the scale below:
You can also generate team reports, just like you did for comparing projects:
Let’s take a look!
1. Team vs Baseline Average
This chart will show you scores of the chosen team according to the chosen metric compared to the company’s average
Pick your team of interest
Choose a metric you’d like to check
2. Team vs. Team Comparison
Choose a metric you’d like to check
Pick the metric that interest you
And get your results right away
3. Coding Productivity by Team
Understand the coding behavior of your teams. Which teams are the most productive in code writing and commit activities.
Size of the bubble represents the total Code Volume contributed by the particular team – with similar correlations of developers as defined under the project views.
4. Deployment Productivity by Team
Understand the deployment performance of your team based on how fast they are releasing new features.
The size of the bubble is the number of pull requests merged – with similar correlations of developers as defined under the project views.
5. Team Commit Activity
In this chart, you can compare your team side by side, and metric by metric, based on commit activity.
Each of the metrics can be sorted ascending or descending. Just click on the metric to reverse the values. The small percentages below the results are indicators of the current scores vs previous period’s scores, according to the scale below:
6. Team Pull Request Activity
In this chart, you can compare your team side by side, and metric by metric, based on pull request activity.
Each of the metrics can be sorted ascending or descending. Just click on the metric to reverse the values. The small percentages below the results are indicators of the current scores vs previous period’s scores, according to the scale below:
• Lead time
• Cycle time
• Velocity
• # of active days
• Coding hours
• Churn
• Technology churn
• Test volume
• Bug fixing rate
• Code efficiency
• Code complexity
• Waste
• # of PRs vs. multiple review cycles
• Responsiveness
• Co-authoring
• Review coverage
• Unreviewed PRs
Objective software development KPIs
