In in order to achieve DevOps success, you must measure how well your DevOps initiatives work. Tracking the right DevOps metrics will help you evaluate the effectiveness of your DevOps practices.
In this article, I’ll explain many DevOps metrics, including their significance, the key metrics for various goals, and — best of all — tips for improving the score of each DevOps metric discussed here.
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DevOps metrics: do you need them?
DevOps metrics are the data that aid in measuring the performance of key DevOps practices or processes such as:
- Continuous Integration (CI)
- Continuous Deployment
- Automated Testing
- Continuous Monitoring
These metrics enable organizations to monitor their progress — are you achieving the goals that you’ve set out? Metrics also help identify any bottlenecks that prevent you from maximizing application performance and employee productivity from DevOps processes. Utilizing these metrics will allow you to make the necessary improvements and get the maximum return on your investments.
In this article, we’ve broken down these metrics into a few categories:
- DORA’s four key metrics
- Optimizing tests and code quality
- Optimizing deployment
- Optimizing continuous integration
- Measuring customer satisfaction
- Optimizing monitoring practices
Key DevOps Metrics: DORA's Four Keys
A well-known set of DevOps metrics are from DORA, Google's DevOps Research and Assessment (DORA) team. Over the years, DORA has identified what distinguishes high-performing DevOps teams. These four metrics are defined thanks to 7+ years of research on DevOps principles and their practical applications.
The following section briefly explains these four key DevOps Metrics, what a good score is and how to improve them.
Change failure rate (CFR)
CFR is the percentage of production deployments that lead to immediate bug fixes or rollbacks of the deployment. It does not count the bug fixes before deploying to production. You can calculate CFR by counting the number of deployments and, out of them, how many have resulted in hotfixes or rollbacks.
This metric is a good indicator of:
- Your code quality
- The effectiveness of your testing methods
The CFR of your team should sit between 0-15% if you are following effective DevOps practices. Practices like trunk-based deployment, test automation and working in small increments can help improve this metric.
Deployment frequency (DF)
DF is a measure of how frequently you deploy changes to production. High-performing teams usually deploy code to production on-demand or multiple times a day. Deploying monthly or weekly will result in a lower DF.
This metric helps teams to:
- Respond to changing customer requirements.
- Fix bugs faster.
- Introduce enhancements to existing features.
- Reduce the risks associated with less frequent or larger deployments.
You can achieve a higher DF by employing an automated deployment pipeline with automated testing and feedback.
Lead time to changes (LT)
Lead time is the time a code commit requires to become production-ready after passing all the necessary tests in the pre-production environment. Calculate this metric using the times of the code commit and the start of the release.
Mature DevOps teams maintain LT in hours, while medium or low-performing teams usually take days or weeks. You can improve the LT by implementing practices like trunk-based deployment, working in small batches and test automation.
Mean time to restore service (MTTR)
MTTR is the time it takes to recover from a total failure or partial service interruption in a production environment. High-performing teams maintain an MTTR of less than one hour, while it can be as high as a week for low-performing teams. You can calculate MTTR by considering the time an incident occurred and the time it took to resolve it.
The MTTR score depends on how quickly you can identify an incident when it occurs and deploy a fix for it. You can improve the MTTR score by continuously monitoring systems and services and alerting the relevant personnel as soon as an incident occurs. It allows them to take the necessary actions quickly.
DevOps metrics for different goals
OK so DORA’s metrics give us a great place to start. Still, several more important DevOps metrics help measure the success of DevOps processes. Let's go through them in the following sections. We’ve broken these sections into metrics for tests and code quality, deployment, continuous integration, customer satisfaction and, lastly, monitoring practices.
Metrics for optimizing tests & code quality
Defect escape rate
This metric measures the number of defects that escaped from lower-level testing and were pushed into production. Your teams should maintain this value close to zero. A higher Defect Escape Rate indicates that your testing processes need more automation and improvement.
DevOps teams should find at least 90% of the defects in pre-production environments before releasing the code to production.
CI test failure rate
This metric is a good indication of your code quality. You can measure it by dividing the number of tests that failed in the CI pipeline by the total number of tests executed.
A high CI test failure rate indicates your code needs more improvement and persuades developers to execute their own unit tests before committing the code.
Code coverage indicates the amount of code tested by the automated test suite. Generally, the best DevOps practice is to maintain a higher automated code coverage as it helps detect failures quickly. However, having 100% test coverage does not ensure maximum code quality, as it can include unnecessary testing.
Metrics for optimizing deployment & workflows
Cycle time measures the time between the start of working on a specific item and when it becomes ready for end-users. In terms of development teams, cycle time is the time between a code commit and when it is deployed to production.
The longer the Cycle Time, the more work in progress — and the less efficient the workflows are. Teams need to optimize and improve the efficiency of their workflows to improve the Cycle Time.
Deployment size is determined by the number of implemented features, stories and bug fixes. You can measure it using the number of story points completed for each deployment. Combine this metric with other metrics like deployment frequency and cycle time to understand the productivity of each deployment.
Deployment time is the time it takes to complete a deployment. It is a useful DevOps metric to measure the efficiency of your deployment pipelines. If the deployment time is very long, taking many hours to deploy, it indicates a potential problem and decreases the productivity of your release team.
You can improve this metric by removing unnecessary steps from the deployment pipeline and introducing parallelization mechanisms.
Metrics for optimizing continuous integration (CI)
CI runs per day
This metric is a count of daily CI pipeline executions. High-performing teams maintain more CI runs per day, typically 4 or 5 times per developer. It indicates the proper practice of frequent releases and trusts in the CI/CD pipeline.
CI success rate
CI can take place many times a day, even though each of them may not be successful. The CI success rate is measured by dividing the total number of successful CIs by the total number of CI runs. It’s better to have a higher CI success rate: it indicates that your CI/CD processes are well maintained and developers perform dev testing effectively.
Metrics to measure customer satisfaction
Of course, the goal of all of this is to satisfy your customers. Here’s what to measure.
Customer ticket volume
The number of reported incidents or support tickets filed by customers indicates how satisfied your customers are with your products. This metric also helps track customer feedback on your releases and provides visibility into the severity of production issues.
Lower customer ticket volumes indicate you have the right approaches and only need slight improvements.
Application availability is the time the application is available to the end-users in its fully functional state. Application errors can cause longer downtimes, causing frustration among users who try to access your application. To improve application availability, teams must lay out strategies like:
- Highly available cloud infrastructure
- Disaster recovery plans (backup and restore)
- Tight security mechanisms
This metric evaluates how the application performs under stress and various user loads. Teams must carry out these tests before deploying to production in a pre-deployment environment equivalent to production.
This metric enables them to identify transactions that can fail and defects when the system is under load. Then, they can optimize the code before deploying and provide a consistent user experience.
Metrics for optimizing monitoring practices
Mean time to detection (MTTD)
MTTD is the time it takes to detect a production failure and flag it as an issue. This metric helps evaluate the effectiveness of your monitoring and alerting systems. The lower the MTTD, the more likely it is that you can fix the issue and push it into production before it affects end users. You can improve MTTD by:
- Employing robust monitoring tools.
- Maintaining good application monitoring coverage.
(Read all about DevOps monitoring and see how Splunk can help.)
Application usage and traffic
This metric indicates how many users are accessing their systems and how many transactions are happening in real-time. There is a high risk of systems failing when there is a high load. Therefore, you can keep your DevOps team on standby to respond in case of any issues.
DevOps metrics for a variety of practices
DevOps metrics are data that enable organizations to assess the effectiveness of their DevOps practices and how they contribute to the achievement of organizational goals. The four key DevOps Metrics include Change Failure Rate, Deployment Frequency, Lead Time To Change, and Mean Time To Restore Services.
Additionally, several other DevOps metrics have been identified related to key tasks of a software delivery pipeline, including deployment, testing, monitoring and end-user experiences. These metrics can ensure successful business outcomes by measuring them with the DevOps processes organizations have implemented.
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