Change failure rate: What it is, how it's evolving, and how to measure it

Change failure rate (CFR) is a software development metric that highlights how often software deployment results in failure. If your team is facing challenges such as bug reports, recurring incidents, or customer complaints, keeping an eye on your change failure rate can help you discover the root cause of your development problems.

Change failure rate can be a powerful tool for understanding software delivery performance. As noted by the 2024 DORA report, when CFR is measured against other factors, such as rework rate, we better understand the probability that deployments will require additional work. The goal isn't to reduce your change failure rate to zero but to manage a careful balance between risk and innovation.

Table of contents

• What is change failure rate?
• How to measure and calculate your change failure rate
• What is a good change failure rate?
• 7 tips to manage your change failure rate
• FAQ

Change failure rate is a metric that measures how often deployments or software releases result in problems. In the world of DevOps metrics, it's a critical data point that helps indicate software's stability and quality. Your team's change failure rate can help spotlight issues within your development process, allowing you to maintain a high-quality product that delivers increased user satisfaction and a better return on investment.

You can utilize tools such as Pluralsight Flow to better view your development process and identify where issues occur. By categorizing failures, your team can better understand reoccurring problems, helping you spot where and why they occur.

Change failure rate is one of four key DORA metrics that allow DevOps teams to understand the effectiveness of their processes. By examining these four metrics throughout the development process, teams can ensure a healthy level of stability and throughput. 

Like any software development KPIs, monitoring DORA metrics should be an ongoing process that helps your team improve its pipeline. By comparing these software delivery metrics against industry standards, teams can better understand their overall competitiveness in the marketplace.

In addition to change failure rate, other DORA metrics include:

• Deployment frequency: How often code changes are released into production, highlighting an effective delivery pipeline
• Lead time for changes: The time it takes for code to be committed to production, with shorter times highlighting faster feedback loops
• Time to restore service: The time it takes to recover from a production failure, minimizing disruption to users

To measure and calculate your team's change failure rate, you'll need the number of failed changes and the total number of changes within your development process. Your rate is represented as a percentage by dividing the number of failed changes by the total number of changes and multiplying the result by 100. Here is the complete formula:

CFR = (Number of failed changes / Total number of changes) x 100

For example, if your team has made 16 deployments and three caused issues, you'd calculate your CFR by dividing three by 16, equaling 0.1875. Then, multiply the result by 100 to get a change failure rate of 18.75%.

Before collecting data and calculating your change failure rate, it's important to establish what you consider a failure and success for your team. If you don't set clear guidelines for what constitutes a failure, your change failure rate may be inconsistent. For example, are failures only issues that impact users, or is it any problem that requires a rollback or fix?

Set a clear definition for what your team considers a failed deployment and document your definition in a location everyone can easily access and reference; this can also prevent slowing down your software team. Consider categorizing failures by categories, such as minor, major, and critical, to get a better picture of the problems that occur during development.

Once your team clearly understands what constitutes a failure, it's time to track your deployments. Use a tracking system that can document deployments made within your pipeline. Include details of each deployment, including the date and time, version number, and changes. It can also be helpful to note what team members were involved with each deployment change.

As you track your deployments and their successes and failures, consider integrating the information with other data collection systems for increased accuracy, such as your version control system or CI/CD pipeline. Maintaining accurate and complete records is critical for calculating your team's change failure rate.

Once you've collected the data, you can calculate your change failure rate using the formula: CFR = (Number of failed changes / Total number of changes) x 100. You must select a period to calculate, such as the previous week, month, or quarter. 

Calculating multiple periods can give you a better view of how your team is doing over time compared to recent periods. If you redefine failures or successes at any time, carefully note it in your documentation, as this decision can significantly affect your change failure rate.

Calculating your change failure rate is the first step, but analyzing the resulting data is critical to turning your information into more actionable engineering insights. For starters, does your CFR show a trend over time? Is the rate getting worse, improving, or staying the same?

Based on your change failure rate analysis, you can then choose to make changes within your process. For example, you'll want to take a closer look at where the errors occur in your pipeline. You can then make changes, such as increased testing coverage or implementing a code review checklist, to address the problem.

Defining a good change failure rate is challenging, as many developers disagree on what a "good" change failure rate is. The general consensus is that it should fall below 20%-30% and be as close to zero as possible—some developers go as far as to say that a CFR of 5% is ideal but perhaps unrealistic.

So, as a team lead, what should your team's change failure rate be? We recommend aiming for as close to zero as possible but with a few caveats. Namely, a score of 0% is impractical and potentially shows that your development team needs to take more risks to innovate; you might be playing it too safe. Focus less on your CFR as a hard number and instead as a trending metric that showcases whether your team is improving, remaining stagnant, or experiencing recurring issues.

Once you've identified your team's change failure rate, you will inevitably want to focus on improvement. Lowering your change failure rate can indicate that your team produces more stable software, delivering a better user experience and higher ROI for your organization. By following these seven tips, you can better understand how to calculate and implement changes for your CFR.

Testing your software is critical to identifying issues early in the development pipeline and reducing failures that could affect your change failure rate. Consider integration testing and more involved QA testing processes for your team that help ensure different system components work together as expected. For example, for an e-commerce website, you’ll want to examine the frontend and backend, backend and database, and backend and payment gateway to ensure they interact as expected.

Continuous integration and delivery tools (CI/CD) are synonymous with modern development processes. They help minimize errors and enable developers to push out releases more frequently with less risk. If not already in use, implement CI/CD tools within your team's process. When code changes, it's automatically built, tested, and deployed to a staging environment before production, helping to reduce potential problems. Don't fear halting anything in CI that isn't ready for deployment; this is the cheapest and most efficient time to pull back.

Ensuring your team embraces the concept of engineering observability is crucial for better understanding your development processes. By adopting observability practices, your team can record events and use KPIs to acquire a better overall view of how changes flow through your pipeline. As a result, when an error is detected, your team can quickly investigate the issue, locate the cause, and resolve it.

Feature flags enable developers to turn features on or off in production without deploying code. Using this method, your team can slowly release new features to a subset of users via a controlled rollout or A/B test for better user satisfaction. For example, rather than releasing a new potentially problematic feature to your entire user base, you can release it to a small set of users, choosing to implement a kill switch if issues arise.

Your team's learning culture has an overwhelming effect on your change failure rate. The latest DORA report acknowledges the pressure of delivering results quickly; not only can this view lead to a focus on delivery over quality, but it can also harm employee well-being. Every developer should feel comfortable collaborating without the fear of failure or burnout, leading to a more efficient environment that produces fewer issues. Consider a culture that focuses on healthy developer productivity tactics, learning from failures through open communication.

When examining your CFR, it's no surprise that your team's overall code quality has an immense impact. More maintainable code is more efficient at preventing issues and less complex when the need to identify and fix a problem appears. Embrace coding standards and style guides that assist with overall code readability. Establish strong code review practices to recognize issues before they occur.

Change failure rate is not a one-time calculation; it's an ongoing DORA metric that promotes continuous changes that can improve your processes. When your change failure rate increases, take the time to analyze the data to identify recurring patterns that may be causing issues. Don't fear experimentation and take time to analyze past results; they can act as a beacon of instruction, pointing out why previous failures occurred and providing valuable lessons for your team moving forward.

Here are answers to some of the most frequently asked questions about change failure rates and how they affect your team's performance.

Using a change failure rate is a multistep process that includes defining what your team considers a failure, tracking your deployments, calculating your rate, and making appropriate changes. By calculating and analyzing your CFR, your team can identify patterns in their development process that need improvement, leading to higher-quality software, more satisfied users, and a better ROI for your organization.

The most common causes of a high CFR include inadequate testing, a poor development process, and overall code quality issues. When analyzing your change failure rate, it's critical to understand what specific practices are causing the failures and identify any problematic patterns.

CFR, or change failure rate, focuses on how often deployments result in production failures; it's calculated by dividing the number of failed deployments by the total number of deployments and multiplying by 100 for a percentage. MTTR, or mean time to recovery, denotes how long it takes to recover from a production failure; it's calculated by dividing your total downtime by the total number of incidents.

Addressing your team's change failure rate can feel overwhelming, but software tools can ensure data and reports are tracked automatically and accurately for later reference.

Pluralsight Flow is an engineering transformation solution that can track DORA metrics, including change failure rate, so you know precisely how often incidents happen and how to solve the types of problems that matter to your team. Request a free demo of Pluralsight Flow today to tackle your CFR without fear.