By Alex Circei, CEO & co-founder of Waydev.
The change failure rate (CFR) is a metric that measures the frequency with which errors or problems arise for customers following a deployment to production. The rate at which changes are unsuccessfully deployed is known as the “change failure rate.” Change Failure Rate, like the other DORA measures, is a gauge of an organization’s or team’s level of development and quality. The success rate of a transition is the topic of this article. This statistic makes understanding how much time is spent resolving issues easier. You can gain an understanding of its quantification and mitigation techniques.
What are the DORA metrics?
The DORA metrics identify four measures as closely connected with success, and these metrics serve as a yardstick by which DevOps organizations can evaluate their performance. Deployment Rate, Change Failure Rate, Recovery Time and Mean Lead Time are the four metrics to track. Comments from 31,000 experts all over the world who responded to a poll over six years helped pinpoint these trends.
For each indicator, the DORA team also established performance criteria that describe the qualities of “Elite,” “High-Performing,” “Medium-Performing” and “Low-Performing” teams.
What is the change failure rate?
If you take the number of incidents and divide it by the total number of deployments, you get the Change Failure Rate, which is the percentage of deployments that fail in production. As a result, managers can see how much time is spent addressing bugs in the code that is being shipped. Achieving a change failure rate of 0% to 15% is typically within reach for DevOps teams.
There will always be errors when new features and fixes are constantly sent out to live servers. These flaws can sometimes be pretty trivial or cause catastrophic failures. It’s essential to remember that these are not a reason to single out any person or group for blame, but engineering leaders must keep track of how often such things occur.
How much does a high CFR affect a company, and how can you minimize it?
You need the whole set of data shown on a car’s dashboard to perform routine maintenance, much as you need one set of metrics to know when everything is fine with your code and another set to know when something is wrong. Collective use of metrics is preferable to their application. The rate at which your changes fail to take effect is a lagging indicator of issues inside your developer workflow. If your engineering teams see a high change failure rate, they may need to reevaluate their PR review procedures.
You can lower your CFR by taking a few different actions. It is possible to put some into place while still developing; these center around testing and automation. The deployment phase also encompasses additional measurements such as infrastructure as code, distribution techniques and feature flags.
Failures are less likely to occur when code quality is increased. If you want higher-quality code, better testing is a must. That necessitates a comprehensive set of tests for your application’s code. The unit test is the most basic type of test, and its purpose is to ensure that specific procedures or parts of a larger whole function are as intended.
Integration tests are the next level of testing, and they verify the interoperability of the system’s various components. There is also disagreement over whether or not integration testing should use natural upstream systems or sandboxed ones. While the former may simulate deployment in a more realistic setting, the latter gives testers more leeway to simulate unexpected outcomes.
End-to-end testing allows you to simulate real-world user actions in a fully functional setting. This is usually performed before code is regarded as suitable for deployment or as part of the testing process after a deployment has occurred. In both cases, these tests validate whole workflows.
Test automation, or the means through which tests are run, is the second strategy for enhancing code quality. The developers use the findings to determine what needs to be prioritized.
It is possible to automate the execution of a whole suite of tests for small networks at predetermined times, such as when a new code is submitted, when a pull request is created and when a new branch is merged into the main one. By programming tests to run automatically in response to predetermined conditions, your team may reduce the likelihood that tests will be skipped and the amount of time they spend waiting for someone to run them.
Create deployment strategies.
Teams can improve their CFR and reduce the likelihood of failed deployments when they follow a deployment plan rather than winging it.
Let’s take a step back and think about the simplest case: a team getting ready to release a new version of a product. When a new version of a product needs to be deployed and tested, the team plans an outage, shuts it down and then brings users back online. The problem with this strategy is that it is hazardous. There are no other means for end users to restore access than performing a rollback, repair, hotfix or fix ahead.
Ad hoc deployments carry a lot of risks. Thus many teams have started using a deployment plan instead. Canary releases, blue-green releases and rolling releases are the three most prevalent deployment methods.
The rate at which changes fail is a crucial indicator for gauging and enhancing the effectiveness of your engineering department. It’s a helpful indicator for gauging your team’s talents and seeing how they adapt and improve their processes as they encounter new challenges. This statistic, along with lead time for modifications, deployment frequency and recovery time, can help your team reach its maximum engineering potential.