When working in Git, you want to ensure your team is working as efficiently as possible.
With Git analytics, you can use tools to easily track your team’s progress, cycle lengths, productivity, and more.
But how do you know you’re using the right Git analytics tools to track the right data?
Here’s a look at how you can ensure your team uses the right Git analytics tools to gain powerful insights on team productivity, code effectiveness, and more.
Git analytics is any of the available tools for Git that help development teams get a strong overview of how they’re working.
Git analytics uses data in Git and give teams powerful insights on things like:
Git analytics goes deeper into productivity than how many lines of code were written, how much code churn occurred, and how many hours team members logged. It gives teams a magnified look at where, when, and why problems occurred so they can refine processes and ensure things run as smoothly as possible.
There’s no shortage of available Git analytics tools to help your team improve productivity, see where there may be wasted time or efforts, and work more efficiently.
Here are a few different metrics your team should be tracking with Git analytics to improve their performance.
It’s important to know how long it takes developers to resolve tickets because cycle times can indicate if a team is effectively applying lean principles and having low waste.
Take advantage of tools that track the time it takes developers to work when:
A short cycle time: Indicates your team is applying lean principles successfully. They can deliver value to customers quickly with low waste.
A long cycle time: May indicate problems with your team processes that cause work to pile up and impact your team’s efficiency.
Git analytics tools can reveal how much effort your team is putting into code churn. These track how much code that was recently written is rewritten.
A modest amount of code churn can be healthy and indicate your development team is incorporating customer feedback, adding new features, and making updates as necessary.
Code churn that is rare — or never happens — means your team is likely not incorporating customer feedback, making needed changes, or there is not enough direction from managers.
High amounts of code churn, especially soon after the code was put into production, can indicate that your teams are not self-editing and there is an issue with review and self-edit processes.
Tools that reveal how much code is rewritten soon after it’s written can help reveal any problems with self-editing, review processes, or mismatches with developer skills and certain projects.
While it’s important to know how much code churn your team has, it’s also important to know where it’s occurring.
Before a Review: Code churn is optimal when it happens before it’s submitted for review. This means developers are self-editing, reviewing, and refining their own work.
After a Review: Code churn that occurs after a review can be problematic.
This can mean tasks are too difficult for certain developers, and certain projects may need to be assigned to those in more senior roles.
After Made Into Production: The worst place for code churn to occur is soon after it goes through review and is put into production. It requires developers to make hotfixes to live bugs because they failed to discover issues during the testing and review. This can indicate problems with review processes or poor direction from management teams.
Identifying where code churn occurs can reveal if there are underlying issues related to code churn that hold up other projects or keep your team from focusing on other important tasks.
While you want to know how your software is operating, it’s also important to know how your team members are performing. While Git analytics cannot tell you if there’s an individual performance problem, it shows what each person worked on so managers can easily assess if those tasks align with expectations.
Knowing how each team member performs can help you:
The key to getting the best data to help your development team succeed is using the right tools.
Here are a few of the tools your team should be using to improve and optimize processes.
Code Climate is one of the most popular development analytics tools. Code Climate aims to help teams “align,” “deliver,” and “improve” their software.
It gives teams data-driven insights to improve workflows, outline goals, find room for improvement and help development teams improve overall.
Code Climate allows teams to:
PluralSight is a visual organization tool that helps teams learn about their software function and team productivity.
Teams can gain powerful insights on things like:
With the insights PluralSight provides, teams can develop through helping individuals improve their skills, increasing knowledge sharing, boosting collaboration, and more. Teams can also improve software by evaluating where bottlenecks happen and bettering practices to run more efficiently.
In short, PluralSight Flow can you understand how your team functions alongside software to streamline and improve practices.
LinearB is one of the most popular Git analytics tools that helps teams improve and optimize workflows.
LinearB helps teams:
Some of the key metrics LinearB tracks include deployment frequency, breakdowns of new work, refactoring, and reworking, time spent on non-functional code, features, or bugs in code.
It gives a visual breakdown of the projects each team member is working on, which some project managers appreciate — but others may find cumbersome.
minware goes above and beyond the traditional metrics many git Analytics tools provide.
While Git Insights and other analytics tools can show data surrounding cycle time with conventional metrics, they may not give teams enough understanding into why issues occur and show any problems when a team is merging bad code into the main branch.
In this case, developers are essentially marking tasks as done when they are not really complete, and the code still needs more testing and fixes. The cycle time may look sufficient, but it is inaccurate because the code is still being written and debugged after the "end" of the cycle.
Unlike other Git analytics tools, minware can identify anti-patterns because it traces what happens to code after it gets written. Therefore, minware can identify high churn immediately post-merge.
minware also empowers teams with data on where code churn occurs. This helps teams identify if code churn happens before reviews, after reviews but before production, or after production to help identify where processes may be falling short.
Unlike traditional analytics tools, minware, gives valuable insights into the entire lifecycle of code. By tracing merging and removal over time, teams can remove waste from defects and product inefficiencies.
Software development analytics are the key to ensuring your team is working as effectively as possible.
Take advantage of tools that track what your teams are doing, how long cycles take, how software runs, and dig deep into code churn. When your team has data that shows deeper insights on cycle times, code churn, and performance, you can refine processes to help everyone work smarter — not harder.