Software engineering metrics are crucial. By using metrics, teams and organizations can evaluate their performance in clear and objective ways. The insight they obtain by tracking metrics can then lead them to better decision-making, resulting in higher team productivity and customer satisfaction.
Choosing the right metrics is often a challenge, though. There are lots of them, but they aren’t equally valuable. If your team uses Jira, then this post is for you. We’ll offer you some help by covering the five most valuable Jira metrics your team should consider adopting.
We’ll start the post with some fundamentals, talking about why metrics in software development are so crucial. Then we’ll briefly cover some of the different types of metrics used in software organizations, following that with the main metrics adopted by agile teams.
With the basics out of the way, we’ll treat you to our list of essential Jira performance metrics, before wrapping up with some final thoughts and tips. Let’s get to it.
The Importance of Metrics When Running a Dev Team
Despite software engineering being a STEM discipline, it’s riddled with unscientific reasoning for why things are done a certain way, including cargo cult and appeals to authority. Software engineering metrics offer a light in the form of objective assessments. The data don’t lie.
When it comes to managing an engineering team, tracking the right metrics is amazingly valuable. That’s particularly true if you manage a team that’s growing rapidly. There’s simply no way to keep on top of things if you don’t adopt a data-based approach.
However, to track the right metrics you must first choose the right metrics to track. Mistakes at this point—such as focusing too much on individual rather than team metrics—are certainly going to come back to bite you in the future.
Check out this interview we did with Dana Lawson from GitHub about the metrics they use:
We’ll now cover some of the metrics that are commonly used in agile, identifying the ones that are really effective.
What Are the Different Types of Metrics?
There are many software engineering metrics out there. Also, there are different ways in which you can categorize the different metrics available. For instance, software engineering metrics can be grouped into source code metrics, development metrics, and testing metrics.
- Source code metrics are measurements you can obtain from the code itself, such as lines of code or cyclomatic complexity.
- Testing metrics refer to measurements related to automated tests, especially unit tests. Branch coverage is a classical example of a testing metric.
- Development metrics refer to the development process itself. Most of the metrics we’ll cover today actually fall under this umbrella.
When it comes to agile metrics specifically, a popular categorization consists of dividing them into lean, kanban, and scrum metrics.
What Are the Metrics Used in Agile?
As you’ve just seen, there are not only a large number of software engineering metrics but also many possible ways to categorize such metrics. It stands to reason that not all metrics are equally valuable. So, how can you know the metrics that agile teams really use?
Keep in mind that the term “agile” usually refers to a large number of similar yet different development methodologies. The actual metrics teams use rely heavily on the “flavor” of agile development they favor, such as scrum or lean.
Since scrum is indeed what most agile teams and organizations favor, you should be aware of metrics such as sprint burndown, epic burndown, velocity, defect ratio, and cycle time.
The 5 Jira Metrics That Matter Most for Your Dev Team
Without further ado, let’s see the most important Jira performance metrics for your scrum team.
1. Velocity
Velocity is arguably the most well-known metric, not only in Jira but for agile in general. Velocity, as its name suggests, is about how fast your team is going. The value of your velocity metric refers to how many story points the team completes over time.
Velocity is an effective metric when used in the context of a single team. By continuously measuring velocity, you get better at setting sprint goals and sprint capacity planning. Unfortunately, many software teams routinely abuse this metric and suffer the consequences of doing so.
The most common mistakes are using velocity for comparison between teams and as a performance indicator, which can damage team morale and cause the developers to game the metric. The key takeaway here is that velocity can be an effective metric, but only when used correctly.
2. Cycle Time
Our second metric, and a great alternative to velocity, is cycle time: it refers to the amount of time the team spends working on issues, from the start of work until the feature reaches the end-users.
Cycle time is an incredibly valuable metric because it can help you discover inefficiencies in the whole software development process. For instance, work that takes too much time to be reviewed and/or merged contributes to longer cycle times, as does a high rate of rework.
Our data science team studied the cycle time of nearly 2,000 teams and discovered the average cycle time for the developers studied was 6 days + 5 hours. We went on to build engineering benchmarks to help you not only measure your cycle time but determine which phase of your cycle time needs the most attention.
3. Defect Ratio
If you want your scrum project to be of the highest quality possible—and who doesn’t?—you must be aware of how many defects are being introduced into the project, how long it takes (on average) for them to be fixed, etc. Using Jira capabilities, such as filters and dashboards, you can create sprint reports to depict the defect ratio of a given sprint, epic, or version.
4. Sprint Burndown
The sprint burndown chart shows the amount of work the team members still have to complete in the current sprint. Ideally, the team should be able to finish all the work it committed to finishing during to sprint.
So, keeping track of the sprint burndown chart, along with other Jira sprint metrics, can help you learn important things about your team’s health and productivity. You might find out that the team is committing to more work than it can handle—or the other way around.
In short, keeping track of the sprint burndown chart and evaluating how close the team delivers the work that was expected can help you diagnose and fix anti-patterns in your organization.
5. Epic Burndown
In agile jargon, “epic” is the name you give to a big user story. You then break it into smaller user stories that can fit into a single script. It’s essential to be able to evaluate how much work the team has done on each specific epic, and how much work is still left.
By viewing the epic burndown chart, you can see the progress your team is making toward the completion of the epic. You can then extrapolate their speed to estimate how many more sprints they’ll need to complete the epic, which assists in your sprint planning.
Jira Metrics Might Be Effective, But They’re Not Enough
You can’t improve what you don’t measure. That’s true for many industries, and software engineering is no exception. Quite the opposite: there are a plethora of software development metrics you could track.
Unfortunately, that abundance has its downside: it can be overwhelming to evaluate the available metrics and decide which ones are worth keeping track of. This post was about some of the main Jira metrics that can be helpful for dev teams.
However, it’s important to keep in mind that Jira metrics aren’t a panacea when it comes to evaluating your team’s performance. As you’ve seen, managers and even developers often misuse some metrics. That, in turn, might encourage developers to game the metrics, hurting team culture in the process.
Also, data from project management software like Jira can be inaccurate, and the picture you get from it might be lying to you.
Do you know what doesn’t lie? Your commits. Combining project data with statistics from your repository is a great way to maximize the benefits of your metrics. If you want to check out a tool that can offer you that, take a look at what LinearB can do.
