For today’s article I’ll talk a little about change failure rate and what it might mean to your organization. I cover it ever so briefly in the DORA metrics article I posted a while back. I’ll go a bit more in depth here today.
What is change failure rate?
Change failure rate is a metric used in the software delivery process to quantify the percentage of times a change, such as a new feature, bug fix, or configuration update, results in a negative outcome, such as a degraded user experience, system outage, or rollback. This metric helps organizations understand the stability and quality of their software delivery pipeline and identify areas for improvement.
Where did change failure rate come from?
The popularity of the change failure rate metric can be largely attributed to the research conducted by DORA (DevOps Research and Assessment). DORA started publishing the “State of DevOps Report” in 2014, which showcased various key performance indicators (KPIs) related to software delivery and operational performance. Change failure rate is one of the four key metrics, along with lead time for changes, deployment frequency, and mean time to restore (MTTR). These metrics together are often referred to as “DORA metrics” or “DORA’s four key metrics.” They are widely recognized and used by organizations to assess the effectiveness of their DevOps practices and software delivery processes. The extensive research and industry adoption of DORA’s findings have contributed to the popularity of the change failure rate metric.
Is high or low change failure rate better?
From a software delivery standpoint, a low change failure rate indicates that the development, testing, and deployment processes are effective and reliable, and the team is able to consistently deliver changes with minimal negative impact. A high change failure rate, on the other hand, may point to issues in the delivery process, such as inadequate testing, rushed deployments, or poor collaboration between teams.
How do you actually measure change failure rates?
The simplest example is the following:
I deploy twice a quarter and fail one of my releases. I need to roll back. The other time I try to deploy the change I succeed without an issue. My change failure rate is a whipping 50%! You can start off by doing this manually, you don’t need to automatically track off the bat. Once you’re more comfortable with the concept or it becomes really painful, create an automated system that can track successes and failures in an automated way. Scaling is key!
Here is a bit more info on starting to measure CFR:
- Define what constitutes a failure: First, establish a clear definition of what constitutes a failure or negative outcome. This may include incidents such as rollbacks, hotfixes, degraded user experience, or system outages. You can start off with a literal “yes/no” as a starting point.
- Collect data: Gather data on the number of changes deployed and the number of failures associated with those changes. This information can be extracted from your version control system, deployment tools, incident management tools, or other relevant sources.
- Calculate change failure rate: Divide the number of failed changes by the total number of changes deployed during a specific time period (e.g., per month or per sprint). Multiply the result by 100 to get the change failure rate as a percentage.
Change Failure Rate (%) = (Number of Failed Changes / Total Number of Changes) x 100
- Track and analyze trends: Monitor the change failure rate over time to identify trends and patterns. Analyze the data to determine if any specific factors, such as certain types of changes or teams, contribute to higher failure rates. This can help you focus your improvement efforts on areas with the most significant impact.
- Implement improvements: Use the insights gained from your analysis to implement changes in your software delivery process. This may involve refining testing strategies, improving collaboration between teams, automating deployments, or other actions to address the identified issues.
- Continuously monitor and iterate: Continue to measure and analyze your change failure rate over time. Use this information to drive ongoing improvements to your software delivery process and track the effectiveness of the changes you’ve implemented.
Organizations can reduce change failure rates by implementing best practices such as:
- Continuous Integration (CI): By frequently merging code changes into a shared repository, teams can identify and fix integration issues early on, reducing the likelihood of deployment failures.
- Continuous Deployment (CD): Automating the deployment process helps to ensure that changes are released in a consistent and reliable manner, reducing the potential for human error.
- Automated testing: By automating tests for each change, teams can quickly identify issues and address them before the change is deployed to production.
- Monitoring and observability: Implementing monitoring and observability tools enables teams to quickly identify and resolve issues that may arise after a change has been deployed.
- Collaboration and communication: Encouraging collaboration and communication between development, operations, and other teams helps to ensure that potential issues are identified and addressed before they lead to failures.
- Postmortems and continuous improvement: Conducting regular postmortems for incidents and using the lessons learned to drive improvements in the software delivery process can help to reduce change failure rates over time. It’s all about the lessons learned. Here’s a bit more on post mortem’s or lesson’s learned meetings in software delivery:
- Blameless postmortems are an essential aspect of a healthy software delivery culture, as they emphasize learning from incidents rather than assigning blame to individuals. I’ve been in quite a few meetings or post mortem’s where it’s more of a “What the *#&# did you do”. It did not go well for anyone.
- A blameless postmortem encourages open and honest discussion of mistakes, which in turn helps to identify areas for improvement and avoid similar issues in the future. This approach aligns with the core principles of the DevOps movement, which promotes collaboration, learning, and continuous improvement. Relating this back to change failure rates, blameless postmortems contribute to reducing change failure rates in the following ways:
- Encourage openness: When team members feel safe to discuss errors and incidents without fear of retribution, they are more likely to share valuable insights into the root causes of failures. This transparency is key to identifying and addressing issues in the software delivery process that contribute to change failure rates.
- Foster a culture of learning: Blameless postmortems create an environment where learning from mistakes is prioritized over assigning blame. This mindset encourages teams to iteratively improve their processes, tools, and practices, ultimately leading to a reduction in change failure rates.
- Promote collaboration: Blameless postmortems emphasize the importance of teamwork and collaboration in problem-solving. By working together to understand and address the root causes of failures, teams can develop more effective strategies to prevent future incidents and reduce change failure rates.
- Continuous improvement: Blameless postmortems drive continuous improvement in the software delivery process. By regularly analyzing incidents and implementing improvements, organizations can proactively address potential risks and reduce change failure rates over time.
Well, that’s the post for today. I often get asked quite frequently on what is CFR or change failure rate. There’s a lot more to the topic. IF you have questions or want this implemented at your organization, feel free to reach out as i’ve done this quite a few times!