Development Analytics tools, also known as Git Analytics tools, are applications meant to analyze software developers’ activity and offer comprehensive reports. For a long time, we’ve been using mostly quantitative ways to measure software developers’ performance, for example, metrics like Lines of Code (LoC), which don’t speak for the quality of the work. Now, it’s time to appreciate engineering activity in a meaningful way by relying on real-time data.
How they work
All you need to do is register to a Development Analytics tool like Waydev, Gitprime, Pluralsight Flow, or Jellyfish. After registering, all you need to do is connect your Git repositories, and in a short time, you will be able to see reports and metrics based on your engineers’ activity.
Who should use development analytics tools
With the help of software development tools, engineering managers can increase team performance, identify healthy work patterns and then reinforce them.
Giving feedback to software engineers becomes a much more straightforward affair. Managers only have to look into the reports for each developer to provide them with objective feedback.
Trends and work patterns that would otherwise go unnoticed can be recognized for what they are, achievements in healthy work patterns, or underlying problems that require assistance in the case of unhealthy work patterns.
Engineering managers need to perfect the art of standups to keep themselves, and their team informed regarding the latest updates in their activity. Data-driven standups showcase how Git Analytics tools improve engineering management by creating a sense of collaboration and shared contribution.
Software developers can significantly benefit from using Git Analytics tools. These tools improve teamwork because engineers can see what everybody is working on and then assist their colleagues experiencing difficulties.
Growing as a developer and moving up in the organizational hierarchy become more achievable goals when there is data signaling the areas that need improvement.
Product managers can visualize the software delivery process from idea to production and change the way they’ve been evaluating the software development process. By shifting their perspective from what was built to the building process, they can spot roadblocks or specific issues regarding the process and then act towards streamlining it.
Product managers aim to achieve product stability, and Git Analytics tools are a good start towards that. These tools enable product managers to visualize the product’s development in real-time, avoid risks and overcome technical debt as they come.
Executives are able to take their organization to the next level by adopting a data-driven decision-making process. In order to improve something, one needs to measure it first. This is the case with engineering performance as well.
As an executive, you must be interested in where you stand in comparison with your competitors. Comparing your teams to industry benchmarks will give a sense of your organization’s overall baseline.
Knowing this allows executives to experiment with workflows and processes and then observe how things change in correlation to that baseline.
How should you use Development Analytics tools?
Here we are going to list some use cases where the data-driven approach provided by Development Analytics tools provides the best results:
Daily standups are used to identify the sprint status, the work in progress, and the work in a review. Because of their repetitive nature, it might seem like everything is progressing smoothly.
No engineer is reporting any blockers, so we must be progressing just fine? Most of the time, this is not the case because standups rely on self-reporting. Sometimes, developers might omit any difficulties because they think it’s a regular part of their work when signaling a severe issue.
Bringing up data into standups will drive the discussion towards the issues at hand, and the engineers will feel like they are using this time effectively.
One-to-ones are occasions to use data to support your narrative. Developer Analytics tools provide metrics and reports that can create a qualitative analysis of engineering activity.
With this kind of analysis, managers, senior leaders, and nontechnical stakeholders have an easier time processing the information effectively.
Besides increasing visibility, concrete data also contributes to building trust and visibility for your team. Stepbacks caused by ambiguous requests will be avoided by using data to paint an image of the consequences of this practice.
Code review Workflow
When evaluating a sprint, you will need to understand how long it took the team to complete the code review. Let’s say there are sprints with missed deadlines.
In this case, data can be used to start a productive discussion with your team regarding the time to resolve pull requests.
Most organizations adopted an agile method for their software development process, meaning that they adhere to concepts such as quick iterations, creating smaller stories, and giving timely, valuable feedback.
The metrics and reports provided by Development Analytics tools are in sync with all these concepts and aim to drive better collaboration between developers.
It’s frustrating for an engineer to open a pull request and have to wait for a long time for it to be resolved, but at the same time, the reviewer might be overwhelmed because the amount of delivered code was very high, and it’s almost the end of the sprint.
As you can see, there is more than one perspective, and metrics are needed to explore all of them and identify the pain points.
Monthly and quarter reports are when the team looks back on the previous sprint, contemplates their successes and failures, and decides what actions to take to improve their performance in the next sprint.
When you include the data provided by Development Analytics tools in these reports, you will observe reoccurring bottlenecks or roadblocks that haven’t been addressed yet.
These issues are harder to identify because developers are busy with tasks; one might forget the past month’s accomplishments and failures.
By automating these reports engineering leaders are able to use this time to drive discussions about what the teams would like to do and why.
By understanding how work focus and volume vary over time, you can identify your developers’ main work focus and align it with your organizational goals. By communicating with your engineers, you might get a sense of how certain events have impacted their activity.
But you have to keep in mind that self-reporting is not always objective or complete and rely on Development Analytics tools to make data-driven decisions.