Change Failure Rate: Calculation & Best Practices

Introduction

Deploying updates without disrupting user experience is a key challenge for software development teams. While your team can assess the impact and reliability of changes, how can they prevent failures that harm user experience? The answer is: by measuring and improvign change failure rate.

What is change failure rate?

Change failure rate (CFR) is a metric in software development that helps you measure the percentage of changes or updates that fail when deployed to a production environment. It provides insights into the stability and reliability of the software system.

Change failure rate tells you how often these changes fail when they are deployed. It’s like keeping track of how frequently the software breaks or doesn’t work as expected after an update. The metric is expressed as a percentage, which makes it easier to understand.

Understanding your team's change failure rate is crucial for identifying areas where the development process can be improved.

What is a good change failure rate?

The goal is to keep change failure rate as low as possible. A low change failure rate indicates that your software is stable, reliable, and well-tested before deployment. On the other hand, a high change failure rate suggests that there might be issues in your development process, common mistakes such as inadequate testing or poor quality control.

A good change failure rate (CFR) in software development is typically less than 15%, according to industry benchmarks. CFR is the percentage of changes that result in a failure in production, requiring a rollback, a fix, or a hotfix. Here's a more detailed breakdown:

1. Elite Performers: Less than 5%
2. High Performers: Between 5% and 15%
3. Medium Performers: Between 15% and 30%
4. Low Performers: More than 30%

Why to track change failure rate?

Monitoring and analyzing change failure rate will help your development team understand the impact of changes and identify areas for improvement. By tracking this metric over time, your team can strive to reduce failures and improve the overall quality and reliability of their software.

Tracking the change failure rate is crucial as you gain insights into the stability of software changes. As a result, it fosters a culture of continuous improvement and will empower your team to deliver reliable software with minimal disruptions.

Benefits of improving change failure rate

Here are five key advantages that highlight the importance of reducing failures and improving the stability and reliability of software changes:

Enhanced user experience

When software changes fail less frequently, it results in a more consistent and reliable user experience. Change failures can result in degraded service, which negatively impacts user experience. Users can rely on the software to work as expected, without encountering frequent issues or disruptions. This improves user satisfaction and trust in your application, leading to increased user engagement and loyalty.

Less time spent on fixing bugs

A lower change failure rate means your developers will spend less time fixing issues and dealing with deployment failures. They can focus more on developing new features, improving existing functionality, and delivering value to users. Therefore, increased productivity will allow your team to work more efficiently, delivering changes faster and reducing time wasted on rework.

Avoiding increased costs and damaged reputation

Failures in software changes can be costly, both in terms of financial resources and reputation. Failed deployments may require additional resources to diagnose and fix issues, resulting in increased development and operational costs. Moreover, frequent failures can damage your company’s reputation, leading to potential customer churn or loss of business opportunities. By putting an emphasis on reducing change failure rate you will save costs and preserve your brand image. Analyzing incident data can help identify the root causes of change failures and reduce associated costs.

Promoting faster feedback loops and iterative development mindset

Agile development methodologies promote frequent iterations and continuous improvement. Keeping a low change failure rate is essential for the success of such iterative approaches. It will allow your team to confidently deploy changes in smaller increments, ensuring that each iteration is stable and reliable. This promotes faster feedback loops, enables faster learning, and facilitates the adoption of an iterative development mindset in your company. Maintaining a high deployment frequency is crucial for agile development and continuous improvement.

Increased innovation and experimentation

A lower change failure rate creates a safer environment for innovation and experimentation. When engineering teams have confidence that their changes will work reliably, they are more likely to take risks and explore new ideas. This fosters a culture of innovation, where teams can experiment with new features, technologies, and approaches without the fear of frequent failures. Ultimately, this leads to more innovative and competitive software products.

How to improve change failure rate?

Let's take a look at some strategies that can help you improve CFR and ensure reliable system operations:

1. Automate testing and Continuous Integration:
   • Implement a robust automated testing framework, including unit, integration, and end-to-end tests.
   • Use continuous integration (CI) to ensure that changes are tested automatically every time code is committed.
2. Implement Continuous Deployment (CD):
   • Use continuous deployment to automate the release process, reducing human error and ensuring consistent deployment practices.
3. Improve code review practices:
   • Conduct thorough code reviews to catch potential issues before they reach production.
   • Foster a culture of constructive feedback and knowledge sharing among team members.
4. Enhance monitoring and alerting:
   • Implement comprehensive monitoring to detect issues early.
   • Set up alerts to notify the team immediately when something goes wrong.
5. Use feature toggles and blue-green deployments:
   • Implement feature toggles to deploy new features safely and roll them back if needed.
   • Use blue-green deployments or canary releases to minimize the impact of changes by gradually rolling them out.
6. Conduct post-mortems and root cause analysis:
   • After a failure, conduct post-mortems to understand what went wrong and why.
   • Perform root cause analysis to identify underlying issues and prevent them from recurring.
7. Enhance documentation and knowledge sharing:
   • Maintain up-to-date documentation for systems and processes.
   • Encourage knowledge sharing through regular team meetings, workshops, and training sessions.
8. Adopt a DevOps culture:
   • Foster collaboration between development and operations teams to improve communication and reduce silos.
   • Emphasize the importance of shared responsibility for the quality and reliability of deployments.
9. Regularly update dependencies:
   • Keep libraries, frameworks, and dependencies up-to-date to benefit from security patches and improvements.
   • Use dependency management tools to automate updates where possible.
10. Implement a strong rollback strategy:
    • Have a clear rollback strategy in place for quickly reverting changes that cause failures.
    • Test rollback procedures regularly to ensure they work effectively.
11. Invest in team training:
    • Provide ongoing training for team members on best practices, new tools, and technologies.
    • Encourage continuous learning and professional development.
12. Improve change management processes:
    • Implement a structured change management process to assess the risk and impact of changes.
    • Use change advisory boards (CABs) to review and approve significant changes.

Risks connected to focusing on the change failure rate

It can't be that easy. Relying solely on this metric and prioritizing its improvement can lead to certain risks and challenges:

Narrow focus on failure rate

Overemphasizing change failure rate might lead to a narrow focus on reducing failures at the expense of other important key metrics, such as innovation, scalability, and user experience. A sole focus on minimizing failures may hinder your team from taking necessary risks or exploring new ideas that could lead to significant improvements.

Neglecting contextual factors

Change failure rate alone may not provide a complete understanding of the underlying reasons for failures. You should also consider contextual factors, such as complexity, dependencies, and external influences, that contribute to failures. Ignoring these external factors can result in misguided decisions and ineffective improvement efforts.

Unintended consequences

Exclusively striving to reduce change failure rate may inadvertently introduce other negative consequences. For instance, your team might become overly cautious and hesitant to make necessary changes or innovate due to the fear of failures. This risk aversion can impede progress, hinder experimentation, and limit the potential for growth and improvement.

Incomplete quality assessment

Change failure rate is just one metric among many which you can use to evaluate the quality of software changes. If you rely solely on this metric, you may overlook other crucial aspects, such as performance, security, usability, and user satisfaction. 

Lack of continuous learning

Simply measuring change failure rate without a strong focus on learning and continuous improvement may limit your team's ability to address root causes and prevent future failures. It's essential that you foster a culture of learning from failures, encouraging post-mortems, and implementing corrective actions based on insights gained from failures rather than solely aiming to reduce the rate itself.

How to measure change failure rate?

To calculate change failure rate, you will need to track the number of changes that fail when deployed and compare it to the total number of changes made. Here’s how you can measure it, using an example for clarity:

Calculating the team’s change failure rate involves dividing the number of failed changes by the total number of changes.

Define a time period

Select a specific time frame, such as a month or a quarter, to measure the change failure rate. This will  help you in capturing an adequate sample size of changes.

Track changes

Keep a record of all the changes made during the defined time period. These changes could be bug fixes, feature enhancements, or any other modifications to the software.

Identify failed changes

Determine which of these changes resulted in failures when deployed to the live environment. A change is considered a failure if it doesn't work as expected, causes disruptions, or requires immediate rollback.

Calculate failure rate

Divide the number of failed changes by the total number of changes made during the defined time period. Multiply the result by 100 to get change failure rate as a percentage.‍

For example, let's say you made 50 changes to your software in a month. Out of those, 3 changes resulted in failures when deployed. Change failure rate would be calculated as (3/50) * 100 = 6%. This means that 6% of the changes made during that month resulted in failures when deployed.

Alternatives to change failure rate

While change failure rate is a valuable metric for assessing the stability of software changes, there are alternative metrics that provide different perspectives on code quality and performance. Here are a few main alternatives you may want to consider:

DORA metrics, including change failure rate, provide a comprehensive view of software quality and performance.

Mean time between failures (MTBF)

MTBF measures the average time between failures in the software system. It focuses on the time aspect rather than the percentage of failures. MTBF can provide insights into the reliability and robustness of your software. It may help you identify the average duration of stability between failures.

MTBF is suitable when you want to understand the time interval between failures and prioritize reliability improvements.

Mean time to recover (MTTR)

MTTR measures the average time taken to recover from failures and restore the system's functionality after a failure occurs. It assesses the efficiency of the recovery process and the ability to quickly resolve issues. MTTR is valuable for understanding the impact of failures on system availability and minimizing downtime. 

Choose MTTR when you want to focus on rapid recovery and minimizing the impact of failures.

Customer satisfaction score (CSAT)

Customer satisfaction score measures the level of satisfaction or happiness of users with your software. You can gather it through surveys, feedback mechanisms, or user ratings. This metric provides a direct indication of how well your software meets user expectations and requirements. Customer Satisfaction helps understand the overall user experience and can guide improvements to meet user needs. 

Choose customer satisfaction when user perception and satisfaction are key factors in evaluating software quality.

Defect density

​​Defect density measures the number of defects or issues found within a specific portion of the software code. It will help you identify the concentration of defects in specific modules, components, or features. Defect density is useful for pinpointing areas that require more attention and improvements. It is particularly valuable during the development process to track and address quality issues early on. Choose defect density when you want to focus on identifying and resolving code-level issues.

Lead time

Lead time measures the time it takes for a change to move from the initial idea or request to its deployment in the live environment. It encompasses your entire development and delivery process, including planning, development, testing, and deployment. Lead time focuses on efficiency and speed of deployment processes, thereby allowing your team to identify bottlenecks and streamline development workflows.

Choose Lead Time when you want to optimize development processes, reduce cycle times, and deliver changes faster.

Next steps

Change failure rate is a key metric for successful product development and scaling as it provides a clear understanding of the stability and reliability of software changes. By enhancing the stability and reliability of software, it leads to improved user experience and increased customer satisfaction. Measuring change failure rate is crucial for the success of the software development team.

However, every team needs to compose a set of metrics that are beneficial for their unique case and align with specific product and business goals.

To improve your software delivery performance, as well as software quality, learn more about some other metrics:

• Software Delivery Speed
• Code Churn Rate
• Cyclomatic Complexity