DevOps has become an inseparable part of the Agile culture. Some would say that the absence of DevOps practices from the software development process is a matter of “bad manners.”
At UTOR, we believe DevOps can work pretty much for everyone if appropriately integrated. To judge the impact DevOps makes on your organization, you need specific merits by which you can measure success.
Key Performance Indicators (KPIs) give you a better idea of specific results and also point out potential issues down the road. The delivery pipeline consists of multiple stages; you wouldn’t know where the problem is unless you have the right means to spot it.
This article provides DevOps KPIs and metrics, both commonly used, and some of our preferences that you can start using right away. Get in touch if you’d like us to integrate DevOps into your development process.
What Is It Exactly You Need to Measure in DevOps?
DevOps is all about bringing together technology, people, and processes, so there are several aspects to measure about it.
Read more: CI/CD Workflow: How to Implement QA Automation
To grasp the technical side, you’d need to look at deployment time, failure rate, uptime, etc. These are the most easily measured metrics, and the ones will talk more about in this article.
DevOps implies a close connection between the development and operations departments, by definition. Speaking about people, you’d probably want to know the extent of the interdepartmental collaboration. How smooth is communication? How effective is the knowledge-sharing process? These are some of the questions to tackle.
Processes are concerned about individual performance, such as the time required to complete a specific task.
Key DevOps KPIs to Watch Out
Why are KPIs important? There can be quite a few metrics to use, depending on what exactly DevOps means for your organization. You don’t necessarily need all of them. The metrics below would be a good fit for any project.
Deployment Frequency
A good thing about frequent deployments is that it makes software testing a lot easier, which we genuinely appreciate at UTOR. You want regular releases: this way, you ship product updates faster to the end-users, meaning both your client and your client’s clients are happy. Win-win for everyone.
Apart from the deployment frequency, it would help to look at the ability to perform deployment on-demand.
Deployment Time
Deployment time marks the amount of time required to release changes after a “green light” has been given. A lot here depends on the specific set of tools you use – we covered that in detail in our article about the benefits of CI/CD.
Everything under one hour (an industry average) is considered a good result, so make sure to aim for it.
Deployment Failure Rate
As much as we enjoy our success, we shouldn’t forget about the other side of the coin. In fact, your failed deployments matter just as much as your success.
Ideally, you want your failure rate as low as possible. But what are the possible reasons that result in a low deployment failure rate?
It can be a sign of the overall poor quality of your software, meaning that you need more testing done to cover it. But that can also be also the result of a dysfunctional pipeline. Most often, you would stumble across one of these two reasons.
The KPI to watch here is the mean time to failure (MTTF) that measures the amount of time from one failure to another.
Mean Time to Recovery (MTTR)
MTTR is arguably one of the most important metrics in DevOps. It reflects your team’s ability to find and resolve issues quickly. Your team’s proficiency is pretty much what defines MTTR. However, it wouldn’t be easy to fix compressive code errors, even for quite an experienced team.
Consider employing automated recovery tools like Terraform: you can decrease your MTTR by rolling back undesired changes with the help of automated recovery. As soon as your DevOps team can fix an issue, you’re back on track.
One hour is the maximum allowance as far as MTTR is concerned.
Change Failure Rate
This metric defines the percentage of changes that require to be fixed after the production releases. After all, you win nothing if your change failure rate increases along with the frequency of production releases. To avoid that, test things locally first. You want to be dead sure about the changes you’re about to commit.
The top-performing teams keep their change failure rate within the 0-15% range, according to Atlassian.
Defect Escape Rate
This metric tells you how many of the previously discovered defects ended in the production environment. It tells you pretty much about the ability of the QA team to track down the identified issues until their successful resolution. Defect Escape Rate also shows how effective the collaboration between the QA and development team is.
How to Implement KPIs in the DevOps Process
We discussed the KPIs above that would set your project off to a great start, but here is a catch: knowing your DevOps KPIs is one thing; integrating them into the process is totally different.
Here is a step-by-step guide on putting these DevOps metrics into practice:
- Analyze the existing workflow. Set up the data collection process, and identify the areas that need improvements.
- Analyze data and define metrics. Once you’ve come with workarounds, decide how you will be able to measure DevOps results against performance.
- Use ticket system to monitor KPIs. DevOps metric tools like Nagios, Zabbix, Sensu, and Prometheus allow you to spot changes in a scorecard so you can get a general idea of what is going on just from a quick look.
DevOps Metrics: The UTOR’s Pick
Now that you are a bit of a DevOps expert yourself, we’d like to share some metrics of our own preference that we strongly recommend using. Getting yourself familiar with DevOps metrics also helps in case you wouldn’t want to know how to hire a QA company.
Availability and Uptime
Downtime can cost you as much as $5600 per minute on average, according to Atlassian. Although as much as we don’t want that, certain portions of downtime are inevitable. It’s every DevOps expert’s job to define what’s the acceptable downtime ratio.
You measure uptime as the percentage of time the platform has been available during a specific period. It should also be intolerant to the changes in user volumes. For instance, 99.95% uptime during the last six months.
Time to Detection
This one shows how effective and adequate was the response from the DevOps team. Having low time to detection shows that you have robust DevOps processes in place.
Time to detection illustrates how quickly your team deals with issues from the time of their occurrence and remedy.
Load Time
Load time shows how much time does it take for the change process to take place. It tells you how quickly the updates make their way into the production.
Customer Ticket Volume
This KPI can be your worst nightmare if it has margin values. Just imagine: you rolled out the app updates after rigorous testing just to be overwhelmed with the sudden influx of end-user alerts – quite a terrible position to end up.
Code Committed
It’s up to every DevOps team to define the acceptable ratio of code commits. But as a matter of rule, you need to aim for a golden mean since too frequent commits can signal a poor quality code. On the other hand, rare commits might indicate an underperformance of the development team.
Error Rate
The acceptable error rate ratio is below 2%. Everything that exceeds it might require detailed investigation as it can indicate some fundamental workflow problems. A high error rate could mean that developers in charge are not up for the job, perhaps due to the lack of experience.
Let us know if you’d like to hire UTOR to audit your software development process, or are looking into QA outsourcing.
