Smooth and reliable deployments are key to maintaining user satisfaction and business continuity. This is where DORA metrics play a crucial role.
Among these metrics, the Change Failure Rate provides valuable insights into how frequently deployments lead to failures. Hence, helping teams minimize disruptions in production environments.
Let’s read about CFR further!
In 2015, Gene Kim, Jez Humble, and Nicole Forsgren founded the DORA (DevOps Research and Assessment) team to evaluate and improve software development practices. The aim is to improve the understanding of how organizations can deliver faster, more reliable, and higher-quality software.
DORA metrics help in assessing software delivery performance based on four key (or accelerate) metrics:
While these metrics provide valuable insights into a team's performance, understanding CFR is crucial. It measures the effectiveness of software changes and their impact on production environments.
The Change Failure Rate (CFR) measures how often new deployments cause failures, glitches, or unexpected issues in the IT environment. It reflects the stability and reliability of the entire software development and deployment lifecycle.
It is important to measure the Change Failure Rate for various reasons:
Change Failure Rate calculation is done by following these steps:
Apply the formula:
CFR = (Number of Failed Changes / Total Number of Changes) * 100 to calculate the Change Failure Rate as a percentage.
For example, Suppose during a month:
Failed Changes = 2
Total Changes = 30
Using the formula: (2/30)*100 = 5
Therefore, the Change Failure Rate for that period is 6.67%.
An ideal failure rate is between 0% and 15%. This is the benchmark and standard that the engineering teams need to maintain. Low CFR equals stable, reliable, and well-tested software.
When the Change Failure Rate is above 15%, it reflects significant issues with code quality, testing, or deployment processes. This leads to increased system downtime, slower deployment cycles, and a negative impact on user experience.
Hence, it is always advisable to keep CFR as low as possible.
Follow the right steps to measure the Change Failure Rate effectively. Here’s how you can do it:
Clearly define what constitutes a ‘Change’ and a ‘Failure,’ such as service disruptions, bugs, or system crashes. Having clear metrics ensures the team is aligned and consistently collecting data.
Firstly, define the scope of change that needs to be included in CFR calculation. Besides this, include the details to be added for deciding the success or failure of changes. Have a Change Management System to track or log changes in a database. You can use tools like JIRA, GIT or CI/CD pipelines to automate and review data collection.
Understand the difference between Change Failure and Deployment Failure.
Deployment Failure: Failures that occur during the process of deploying code or changes to a production environment.
Change Failure: Failures that occur after the deployment when the changes themselves cause issues in the production environment.
This ensures that the team focuses on improving processes rather than troubleshooting unrelated issues.
Don’t analyze failures only once. Analyze trends continuously over different time periods, such as weekly, monthly, and quarterly. The trends and patterns help reveal recurring issues, prioritize areas for improvement, and inform strategic decisions. This allows teams to adapt and improve continuously.
DORA Metrics provide valuable insights into software development performance and identify high-level trends. However, they fail to capture the nuances such as the complexity of changes or severity of failures. Use them alongside other metrics for a holistic view. Also, ensure that these metrics are used to drive meaningful improvements rather than just for reporting purposes.
Various factors including team experience, project complexity, and organizational culture can influence the Change Failure Rate. These factors can impact both the failure frequency and effect of mitigation strategy. This allows you to judge failure rates in a broader context rather than only based on numbers.
Filter out the failures caused by external factors such as third-party service outages or hardware failure. This helps accurately measure CFR as external incidents can distort the true failure rate and mislead conclusions about your team’s performance.
Identify the root causes of failures and implement best practices in testing, deployment, and monitoring. Here are some effective strategies to minimize CFR:
Implement an automated testing strategy during each phase of the development lifecycle. The repeatable and consistent practice helps catch issues early and often, hence, improving code quality to a great extent. Ensure that the test results are also made accessible so they can have a clear focus on crucial aspects.
Small deployments in more frequent intervals make testing and detecting bugs easier. They reduce the risks of failures from deploying code to production issues as the issues are caught early and addressed before they become significant problems. Moreover, the frequent deployments provide quicker feedback to the team members and engineering leaders.
Continuous Integration and Continuous Deployment (CI/CD) ensures that code is regularly merged, tested, and deployed automatically. This reduces the deployment complexity and manual errors and allows teams to detect and address issues early in the development process. Hence, ensuring that only high-quality code reaches production.
Establishing a culture where quality is prioritized helps teams catch issues before they escalate into production failures. Adhering to best practices such as code reviews, coding standards, and refactoring continuously improves the quality of code. High-quality code is less prone to bugs and vulnerabilities and directly contributes to a lower CFR.
Real-time monitoring and alerting systems help teams detect issues early and resolve them quickly. This minimizes the impact of failures, improves overall system reliability, and provides immediate feedback on application performance and user experience.
Creating a learning culture within the development team encourages continuous improvement and knowledge sharing. When teams are encouraged to learn from past mistakes and successes, they are better equipped to avoid repeating errors. This involves conducting post-incident reviews and sharing key insights. This approach also fosters collaboration, accountability, and continuous improvement.
Since the definition of Failure is specific to teams, there are multiple ways this metric can be configured. Here are some guidelines on what can indicate a failure :
A deployment that needs a rollback or a hotfix
For such cases, any Pull Request having a title/tag/label that represents a rollback/hotfix that is merged to production can be considered a failure.
A high-priority production incident
For such cases, any ticket in your Issue Tracker having a title/tag/label that represents a high-priority production incident can be considered a failure.
A deployment that failed during the production workflow
For such cases, Typo can integrate with your CI/CD tool and consider any failed deployment as a failure.
To calculate the final percentage, the total number of failures is divided by the total number of deployments (this can be picked either from the Deployment PRs or from the CI/CD tool deployments).
Measuring and reducing the Change Failure Rate is a strategic necessity. It enables engineering teams to deliver stable software, leading to happier customers and a stronger competitive advantage. With tools like Typo, organizations can easily track and address failures to ensure successful software deployments.
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