Change Failure Rate: What It Is & How to Measure

If you've been in software development for any length of time, you've likely experienced the stomach-dropping dread of a failed deployment. These incidents aren't just frustrating—they can be downright expensive, damaging to your reputation, and demoralizing for your team.’

That’s why development teams use change failure rate (CFR) to monitor (and hopefully improve) the stability and reliability of your software delivery processes.

Imagine: It's Friday afternoon, and your team has just pushed a major update to production. Finally, weeks of hard work are live. But within minutes, Slack notifications start pinging. Users can't log in, transactions are failing, and your customer support team is drowning in tickets. What should have been a celebratory moment has turned into a full-blown crisis.

Let’s face it: bad ship happens.

Sound familiar? This is where the concept of change failure rate comes into play. Simply put, it's the percentage of changes to production that result in failures or service impairments. 

Below, we’ll walk you through everything you need to know about change failure rate, including: what it is, why it matters, and (most importantly) how to reduce it.

What is change failure rate?

Change failure rate is a software delivery metric that quantifies the reliability of your deployment process. It represents the proportion of changes pushed to production that result in service degradation or necessitate immediate remediation. This includes incidents that lead to system outages, significant performance issues, or require urgent corrective actions such as hotfixes, rollbacks, or emergency patches.

Here’s a simple formula to measure change failure rate:

For example, if you deploy 10 changes in a week and 2 of them cause issues, your change failure rate would be 20%. While there's no universal "good" rate, many high-performing teams aim for a change failure rate below 15%.

A higher change failure rate often indicates inadequacies in your testing methodologies, integration procedures, or deployment protocols. On the other hand, a consistently low rate is indicative of a mature, well-orchestrated delivery pipeline that reflects effective risk management and quality assurance practices throughout your software development lifecycle.

Change failure rate and other DORA metrics

Your change failure rate is usually used in combination with a few other DevOps Research and Assessment (DORA) metrics:

• Deployment Frequency: How often you deploy code
• Lead Time for Changes: How long it takes to go from code commit to production
• Mean Time to Recover (MTTR): How quickly you can recover from failures

A high change failure rate often correlates with longer lead times and MTTR, while successful teams typically see lower failure rates alongside higher deployment frequencies.

Why is change failure rate important to track (and reduce)?

When deployments go sideways, the impacts extend far beyond your codebase. High change failure rates can hit your business where it hurts—in multiple ways. Here’s how:

• Financial implications: There's the immediate cost of lost revenue during outages, especially for e-commerce or SaaS businesses. Factor in emergency fixes, additional infrastructure for rollbacks, and overtime for on-call teams, and the costs can quickly explode.
• Team morale and productivity: Nothing saps a development team's spirit quite like a string of failed deployments. The constant firefighting leads to burnout and decreased job satisfaction, potentially driving your best talent away. When developers are constantly putting out fires, they're not building new features or improving your product.
• Customer satisfaction and trust: Frequent outages or buggy releases erode customer trust and satisfaction, driving them straight into the arms of your competitors. This impacts immediate churn, not to mention your brand reputation and ability to acquire new customers.
• Competitive disadvantage: While you're grappling with failed deployments, your competitors are shipping new features and improving their products. Falling behind on your release schedule due to frequent failures can quickly turn into a major competitive disadvantage.
• Compliance and security risks: Failed changes aren't just operational headaches—they can open the door to serious compliance and security issues. Rushed hotfixes might bypass normal security reviews, introducing vulnerabilities. In regulated industries, failed deployments could lead to non-compliance, resulting in hefty fines or legal repercussions.

The bottom line? Investing in reducing your change failure rate isn't just good DevOps practice—it's smart business strategy.

Factors that contribute to high change failure rates

Understanding what drives up your change failure rate is the first step towards bringing it down. Here are a few common reasons for those failed deployments:

• Lack of proper testing: Insufficient or inadequate testing is often the root cause of many failed changes. This includes not having comprehensive unit tests, integration tests, and end-to-end tests. When testing is rushed or incomplete, bugs that could have been caught early slip through to production and lead to failures and service disruptions.
• Inadequate monitoring and observability: Without robust monitoring and observability practices, teams are often flying blind when it comes to the health and performance of their systems. This lack of visibility makes it difficult to detect issues early or understand the full impact of changes, often resulting in problems escalating before they're noticed.
• Poor change management processes: Weak or inconsistent change management processes can lead to chaos. This includes lack of proper documentation, insufficient review processes, or inadequate planning for rollbacks. When changes aren't properly tracked and managed, it becomes easier for problematic changes to slip through and harder to quickly identify and resolve issues.
• Insufficient collaboration between teams: Silos between development, operations, and other teams lead to miscommunication and misalignment. When teams aren't working together, important context can be lost, leading to changes that don't fully account for all system dependencies or operational requirements.
• Complex, tightly coupled systems: As systems grow more complex and interconnected, the risk of unintended consequences from changes increases. Tightly coupled systems make it difficult to isolate changes, increasing the likelihood that a change in one area will cause unexpected issues elsewhere.

Lack of gradual rollout strategies: Deploying changes to all users simultaneously is a high-risk strategy. Without the ability to gradually roll out changes, teams miss the opportunity to catch issues early and limit their impact. Feature flags can be a powerful tool here, allowing teams to control the rollout of new features and quickly disable problematic changes without a full rollback.

How to reduce change failure rate

Now that you know the factors contributing to high change failure rates, let's look at strategies to bring that number down. Remember, reducing your change failure rate isn't just about avoiding failures—it's about creating a more resilient and efficient software delivery process.

1. Implement robust testing practices

Quality assurance should be baked into every stage of your development process. Implement a comprehensive testing strategy that includes unit tests, integration tests, and end-to-end tests. Automated testing allows you to catch issues early and often.

Define clear acceptance criteria for each change and double-check your tests cover these criteria. This helps align your testing with actual business requirements and user expectations.

2. Adopt continuous integration and continuous delivery (CI/CD)

CI/CD practices are fundamental to reducing change failure rates. Integrating code changes frequently and automating the build, test, and deployment processes lets you catch issues earlier and deploy with more confidence.

Remember to set realistic goals and benchmarks for your CI/CD pipeline. Regularly review your pipeline's performance and look for opportunities to optimize.

3. Use feature flags for controlled rollouts

Feature flags allow you to decouple deployment from release, giving you fine-grained control over when and to whom new features are made available. This enables gradual rollouts, A/B testing, and quick rollbacks if issues arise.

By the way, this is our bread and butter. LaunchDarkly's feature management platform provides powerful tools for managing feature flags at scale. With LaunchDarkly, you can easily implement progressive delivery strategies to reduce risks associated with each deployment.

4. Improve monitoring and observability

Implement comprehensive monitoring and observability practices to gain real-time insights into your system's health and performance. This includes setting up alerts for key metrics, implementing distributed tracing, and using log aggregation tools.

While you’re at it, correlate your change failure rate with other key metrics like deployment frequency and mean time to recovery. This can provide valuable insights into the overall health of your delivery process.

5. Focus on collaboration and communication

Break down silos between development, operations, and other teams. Implement practices like ChatOps to improve real-time communication during deployments. Use collaborative tools that integrate with your existing workflows to streamline information sharing.

6. Implement automated rollback mechanisms

Despite your best efforts, some changes will still cause issues—bad ship happens. Having automated rollback mechanisms in place significantly reduces the impact of these failures. This could involve using blue-green deployments, canary releases, or leveraging your feature flag system for quick disabling of problematic features.

7. Conduct thorough post-mortem analyses

After any major failure, perform a blameless post-mortem to understand what went wrong and how to prevent similar issues in the future. Document these learnings and use them to update your processes and best practices.

And don’t just focus on the failures. Analyze successful deployments, too. Understanding what goes right can be just as valuable as learning from what goes wrong.

Optimize your change failure rate with LaunchDarkly

You have plenty of options when it comes to reducing your change failure rate, but nothing is as impactful and far-reaching as nailing your feature management process. And that’s where we can help.

LaunchDarkly's feature management platform provides a comprehensive solution to many of the problem’s we’ve discussed:

• Controlled rollouts: With LaunchDarkly, you can implement gradual rollouts, allowing you to release features to a small percentage of users and slowly ramp up. This approach reduces the blast radius of potential issues.
• Quick rollbacks: In case of problems, LaunchDarkly allows you to disable features instantly without needing to redeploy your entire application. This can dramatically reduce your MTTR.
• A/B testing: LaunchDarkly's platform enables easy A/B testing to let you compare different versions of a feature and make data-driven decisions about which changes to fully deploy.
• Environment management: Easily manage feature flags across different environments—from development to staging to production—to guarantee consistency and reduce environment-related failures.
• Observability: With LaunchDarkly's integrations and analytics, you gain valuable insights into feature performance and usage.

Remember, you’re not going to be able to avoid every deployment failure—however, you can better manage the fallout and mitigate its effects. And LaunchDarkly gives you the tools to do just that: turn your deployment process from a potential liability into a strategic advantage.

Don't let deployment fears hold you back—with LaunchDarkly, you can deploy with confidence and iterate with ease. Start your free 14-day trial today.