Which DevOps Metrics Should You Be Tracking

Within DevOps, metrics go beyond assessing performance: measuring key aspects, like deployment frequency, system reliability, error rates, and overall efficiency at each stage of software production, are a way to a system’s continuous improvement. This article provides a comprehensive guide to crucial DevOps metrics, including DORA metrics, and offers practical advice for enhancing team performance, system reliability, and deployment efficiency through the use of these metrics.

What are DevOps Metrics and Why Do You Need Them

Fundamentally, DevOps metrics are data points that teams use to track the performance and effectiveness of DevOps practices. The purpose is to deliver information about team collaboration, deployment efficiency, and issue resolution speed, therefore helping teams measure the success of their software development and delivery. DevOps metrics are used in terms of technical capabilities (e.g., automated testing or CI/CD pipeline performance) as well as various team processes (e.g., collaboration between development and operations teams).

What makes DevOps metrics vital for continuous improvement in software delivery processes is their ability to provide insights into pipeline performance. The information they deliver enables DevOps teams to make optimal decisions and improve various facets of software, such as workflows, system reliability, and release cycles, and ensure that these processes align with business objectives.

Optimize Performance and Identify Inefficiencies

Using DevOps metrics helps enhance speed, reliability, and productivity by allowing DevOps teams to detect bottlenecks and inefficiencies across the pipeline and focus on resolving the issues in areas that require immediate attention.

Enable Data-Driven Continuous Improvement

The starting point for achieving continuous improvement is regular measuring of key metrics. With the insights this data provides, DevOps teams can track progress, verify the improvements, and ultimately achieve more efficient workflows and higher-quality outputs.

Align Technical Efforts with Business Objectives

Monitoring DevOps metrics can significantly improve overall organizational success: the insights they provide empower taking actions that improve quality, ensure compliance with customer expectations and boost business outcomes, such as faster time-to-market and higher customer satisfaction.

Foster Collaboration Across Development and Operations

Among other benefits, DevOps metrics bring attention to the quality of collaboration between development and operations teams. These insights reveal areas that require improvement in coordination and ensure faster feedback cycles and more successful deployments.

DORA DevOps Metrics

As a specific set of key performance indicators (KPIs), DORA DevOps Metrics were developed by the DevOps Research and Assessment (DORA) team to measure the performance of software delivery and operational efficiency. Speed and stability are the two aspects these defined metrics are centered on, which means they estimate how quickly and reliably a team can deliver software, and provide insights accordingly. DORA’s research can detect high-performing DevOps teams through an analysis of their performance using four metrics, which are as follows.

Deployment Frequency (DF)

This metric focuses on how often new code is deployed to production. High- and low-performing DevOps teams are defined based on this frequency, depending on whether they deploy multiple times a day, or only once every few weeks or months. The goal of tracking this specific metric is to enable frequent minor deployments and reduce risks that come with large releases and accelerating feedback cycles.

In order to enable frequent and reactive deployments, CI/CD should be used to automate deployment pipelines. You can significantly reduce risks and improve the speed of the releases by breaking them down into smaller changes and optimizing the release process to increase the efficiency, speed, and consistency of deployments.

Lead Time for Changes (LT)

Lead time for changes measures the time from code commitment to successful deployment in production. The benefit of faster lead times is that they enable DevOps teams to address issues, respond to business needs, fix bugs, and deliver new features promptly. For this reason, high-performing teams strive to finish the process within hours, which significantly improves productivity and ensures rapid iterations.

If your aim is to achieve reduced lead time for changes, adopt trunk-based development for regular code integration into the main branch, implement automated testing for rapid issue detection, and utilize feature toggles for secure deployments.

Change Failure Rate (CFR)

This specific metric tracks the percentage of deployments that result in failures (e.g. outages or production issues requiring hotfixes). For high-performing DevOps teams, CFR ranges from 0% to 15%, where the lower the CFR, the greater the frequency and confidence of releases. This is a sign of effective testing, deployment practices, and quality assurance.
For early defect detection and resolution, strengthen automated testing, use canary releases or blue-green deployments to reduce risk, enhance code reviews, and implement robust monitoring for quick issue detection.

Mean Time to Restore (MTTR)

This metric is vital for reducing the impact of production issues on end-users, as it measures the time it takes to recover from a failure in production. Lower MTTR means reduced downtime and service reliability, therefore high-performing DevOps teams aim for an MTTR of less than an hour to swiftly resolve incidents.

To ensure faster resolution of incidents, implement automated rollback mechanisms for quick recovery, utilize proactive monitoring and real-time alerts for early issue detection, and maintain well-defined, regularly practiced incident response playbooks.

DevOps Metrics for Different Goals

Choosing the right DevOps metrics is essential for effective performance management and allows teams to focus their efforts and drive meaningful improvements in their software development and delivery processes. This section provides a practical guide to selecting the appropriate metrics for various goals.

For Local Development

Local Environment Provisioning Time

Local environment provisioning time is essentially the time developers spend setting up or resolving issues in their local development environment. This metric brings attention to inefficiencies in onboarding and local environment maintenance that can hinder productivity.

Using tools like Docker or infrastructure as code (IaC) solutions such as Terraform will not only automate the provisioning process but also simplify environment setup and minimize delays. Using observability tools to monitor environment setup time further optimizes this process. Gathering developer feedback (via surveys or time-tracking) can also provide further insights into bottlenecks.

Post-Commit Test Failure Rate

Post-commit test failure rate, a metric that measures the percentage of tests that fail in an integrated environment after a local commit, tracks the effectiveness of local testing environments and indicates if the local setups are in compliance with integrated environments.

You can achieve and maintain consistency between local and integrated environments by leveraging tools like Docker to mirror the integrated environment in local setups and running local tests before committing changes to catch issues early. If this metric keeps trending down, it’s a sign that local and integrated environments are becoming more consistent.

For Software Component Management

Average Branch Lifespan

Another key aspect to consider is the average branch lifespan, a metric that tracks the time a feature branch has to wait before being merged into the main codebase. Teams strive to minimize branch lifespan to ensure fast feedback and integration since a shorter lifespan means an agile development process and the effectiveness of continuous integration practices.

For shorter feature branch lifespans, encourage frequent commits and use trunk-based development. This, along with integrating smaller, incremental changes and using automated CI pipelines for testing, can speed up the process. Moreover, if you adopt clear coding standards, you can align branches with the main codebase and reduce merge conflicts.

Open-Source License Violations

When it comes to measuring open-source license compliance, this metric tracks the number of open-source components used that do not align with approved license lists to mitigate potential legal risks.

You could significantly improve open-source license compliance through automation, which is achieved by integrating Software Composition Analysis (SCA) tools into your CI/CD pipeline for automatic checks. In addition to automation, regular audits of dependencies and maintaining an up-to-date inventory of all used components can reduce the risk of violations. It makes sense to educate developers on the importance of license compliance, as it will prevent the introduction of unapproved dependencies.

Average Time to Resolve Vulnerabilities

This metric estimates the amount of time required to address vulnerabilities and security risks are addressed and mitigated. Logically, a lower average time corresponds with more proactive security, less exposure, and greater reliability.

For effective vulnerability management, focus on weak spots based on their severity and impact, and ensure that security is integrated into the development lifecycle. Automated vulnerability scanning tools can significantly improve security, while clear security protocols will allow developers to identify and tackle security issues quickly. Encouraging collaboration between development and security teams and providing security training will enhance security even further, as well as improve response times.

Software Component Health

Software component health, a metric that tracks the age, reuse frequency, and contribution to technical debt of components in your codebase, can impact software quality and maintenance. What it mainly helps with is identifying outdated components and tracking the accumulation of technical debt, aspects that are crucial for maintaining a healthy codebase.

You can reduce technical debt by updating or removing old components and improving reusable ones. Static code analysis tools that calculate the technical debt score can help bring attention to where exactly these updates and improvements are most needed. Furthermore, adopting the mentality that focuses on component reuse and ensuring components are well-documented will enable developers to build healthier and more reliable software components.

For Everything as Code

Infrastructure Code Coverage

Infrastructure code coverage is a metric that measures the ratio of infrastructure components managed by Infrastructure as Code (IaC) to the total infrastructure, and it is often expressed as a percentage. The higher the coverage, the easier it is to scale, modify, or replicate environments, as it indicates a higher level of automation, reproducibility, and manageability for your systems.

You can improve infrastructure code coverage by relocating more of your infrastructure to IaC with tools like Terraform or AWS CloudFormation. To keep your infrastructure code up-to-date, regularly review and update it to reflect the current state of your system. This coverage could be further enhanced by increasing automation and consistency in your infrastructure provisioning process.

Configuration Drift Rate

Configuration drift, a metric that estimates the percentage of infrastructure components that deviate from their intended configuration over time, can expose system instability, security vulnerabilities, or performance issues. It is, therefore, critical to keep this metric under supervision and minimize it.

Configuration management tools like Ansible, Puppet, or Chef can help enforce desired states across infrastructure and detect drift early. Furthermore, drift detection, as well as corrective actions, could be automated through integration with your CI/CD pipeline, which will allow you to detect issues and resolve them as they arise. These measures could be complemented by regular audits and periodic reviews of configuration files to prevent drift from escalating.

Documentation Update Frequency

Documentation update frequency is a metric that measures how often documentation is updated and reflects changes in code or system. This metric is indispensable for maintaining accurate records, which are a vital source of data when it comes to problem resolution, onboarding new team members, and preventing operational inefficiencies.

By defining documentation as code and integrating it into your CI/CD pipeline, one can make the documentation updates automated. This automation could be further supported by ensuring that regular updates are a part of the development process and the developers are constantly reminded about them. Finally, to ensure complete synchronization, you could implement tools that can track changes in infrastructure and code.

Time to Provision Infrastructure

Time to provision infrastructure is a metric that measures the amount of time required for deployment of new infrastructure components or environments using IaC. The shorter the provisioning times, the faster the deployments, the better the scalability, and the more improved the system responsiveness, which is especially helpful when it comes to matching business demands.

To reduce the provisioning times, one had better consider refining your IaC practices, minimizing manual steps through automation, and using more efficient provisioning scripts. Specifically, this can be achieved through using cloud-native tools and services that accelerate provisioning (e.g. AWS CloudFormation or Terraform) and ensuring that your infrastructure components are modular for quicker reuse.

Mean Time to Recover (MTTR)

MTTR, another time-tracking metric, measures how long it typically takes to restore a system after a failure and further indicates the effectiveness of incident response and recovery processes. The lower the MTTR, the greater the system reliability, as downtime and service disruptions are reduced.

The key to reducing MTTR is to ensure that your infrastructure is easily recoverable through automated rollback mechanisms. To ensure these mechanisms are effective, teams must arrange regular tests of recovery plans regularly and keep them relevant to account for changes in your infrastructure. Finally, proactive monitoring empowers you to detect issues as they arrive and resolve them promptly.

For Code Review

Review Time to Merge (RTTM)

Review time to merge (RTTM) measures how long it takes for code to be merged into the main branch, from the start of the code review process. This metric brings attention to bottlenecks in the review process that could be caused by inefficiencies in the process itself or in feedback cycles.

You could improve RTTM and save time by automating trivial checks (e.g. formatting or basic syntax), which will also streamline the review process. Another key factor is acquiring relevant and actionable feedback from reviewers to avoid unnecessary delays. Lastly, a well-defined and manageable review workflow with optimal reviewer workloads is crucial for reducing RTTM.

Reviewer Load

Reviewer load is a metric that tracks the number of open pull requests (PRs) assigned to each reviewer. The higher the reviewer load, the greater the likelihood of bottlenecks and delays in the review process, while a lower load with high review times might be a sign that the team doesn’t review changes thoroughly enough.

The key to an optimal reviewer load is finding the right balance of PR assignments among team members without overwhelming them. One way to achieve this balance is to distribute responsibility for specific sections between code owners. To ensure ongoing optimization and improvements, you should regularly monitor the number of pull requests per reviewer and adjust assignments as necessary to ensure that reviews are prompt and efficient.

Code Ownership Health

Code ownership health is a metric that tracks how well the codebase is covered by designated code owners. This metric is useful for various reasons: it ensures that there is a sufficient number of domain experts responsible for reviewing changes, maintains code quality, and prevents delays caused by bottlenecks in reviews when critical sections are not covered enough.

You can improve code ownership health by assigning every section of the codebase to its designated code owner according to their expertise. To avoid overloading them, track the number of pull requests and compare it to the ideal number of PRs a code owner can handle. You might want to occasionally review the CODEOWNERS file and adjust ownership to match the team’s evolving expertise and focus.

Merge Request Type Distribution

Merge request type distribution is a metric that evaluates the nature of pull requests (e.g., new features, bug fixes, maintenance, etc.) and slots them into different categories. This feature helps DevOps teams focus on the most important areas and distribute their resources and time for various types of changes accordingly.

If you conduct reviews about the distribution of merge requests on a regular basis, you will be able to identify trends, such as a higher number of bug fixes compared to feature development that might be a sign of quality issues in the codebase. The insights you gain from the reviews could be used for optimal resource allocation and will allow you to focus on areas that require it most in order to balance the workload across different types of changes.

Change Failure Rate

Another metric, change failure rate, evaluates the percentage of code changes that result in errors or bugs after being merged into production. The change failure rate serves as a diagnostic tool: the higher the rate, the greater the likelihood of issues in the review process or testing pipeline, and accordingly, the lower the rate, the more effective the quality control process.

There are a number of ways you can reduce the change failure rate. One of them is through intensive testing before code is merged, automated unit tests and integration tests included. The process of code review could be enhanced by incorporating detailed checks for edge cases and potential weak areas. Finally, adopting a mentality of continuous improvement and learning from past failures might also help minimize the change in failure rate.

For Continuous Integration

Frequency of Integration

The next metric, frequency of integration, estimates how often developers integrate their code into the main codebase. The more frequent the integration, the better the collaboration and the faster issues are identified, due to the continuous merging of code changes into the shared codebase.

This specific indicator may be improved through regular code integrations, which can be achieved by educating developers on the benefits (e.g. faster feedback and reduced integration issues). To further encourage these regular integrations, implement automated reminders or tools that encourage developers to integrate changes after a specific number of modifications or a set amount of time. Finally, to monitor the effectiveness of these strategies and gain insights into integration frequency, use version control system logs to track this metric and identify any trends or gaps.

Build Success Rate

Build success rate is a metric that indicates the percentage of successful builds compared to the total number of builds attempted. The percentage it provides demonstrates the stability of the build process, the quality of the code changes, and the effectiveness of automated tests.

In order to increase the percentage, you could work on improving the build success rate by focusing on writing high-quality, well-tested code. This code quality is further supported by comprehensive coverage (e.g. unit, integration, and functional tests) to identify defects early in the development process. Monitoring is another way to improve this metric, as it allows teams to track progress and identify issues related to build failures as they arise.

Pipeline Stability

Pipeline stability is a metric that tracks the percentage of build failures that are caused not by code errors, but by errors like infrastructure or configuration issues. Primarily, this metric estimates the overall reliability and consistency of the continuous integration pipeline.

You could improve pipeline stability by arranging regular audits and maintaining the CI pipeline to guarantee the reliability of build environments. Then, to further reduce potential points of failure, reduce the number of external dependencies that result in instability, and ensure automated checks for environment configurations. To be constantly aware of arising issues regarding this metric and resolve root causes of failures efficiently, ensure constant supervision and analysis of pipeline logs, as it will ensure a smoother and more reliable CI pipeline.

Mean Time to Build (MTTB)

Mean time to build (MTTB) assesses the average time required to complete a successful build cycle, from the moment the build process is triggered by a code change to when it is fully verified. The purpose of this metric is to detect potential bottlenecks in the build process and areas for optimization.

Optimizing dependencies, using build caching, and running tasks in parallel – if you want to improve MTTB, these actions might prove to be efficient. What might speed up the process even more is leveraging more powerful or distributed build systems. Lastly, constant analysis of this metric will enable your team to identify slow or inefficient areas in the build cycle and integrate strategies to improve build times.

For Continuous Delivery

Pipeline Stability

Pipeline stability, another metric that works with percentage, indicates the number of deployments that encounter failures, such as failed deployments, rollbacks, and incidents directly linked to deployments. This metric provides data that gives DevOps teams a better understanding of the reliability and efficiency of the continuous delivery (CD) pipeline, particularly its configuration, infrastructure, and the quality of the code being deployed.

Pipeline stability can be maintained through regular supervision of the pipeline logs in order to identify recurring failures or patterns. The next step is to refine the configuration, automate tests, and establish a consistent environment, which will also reduce deployment issues. As a final point, to ensure long-term stability and efficient deployments, consider minimizing dependencies on external services and working to improve the infrastructure.

Mean Time to Production (MTTP)

MTTP is a metric that measures the average amount of time required from when a code change is merged to when it is deployed in the production environment. This metric is important because it demonstrates how quickly features, fixes, and changes reach end users, and provides insight into the efficiency of your deployment processes.

If you aim to lower this indicator, consider streamlining your deployment of automated testing, decreasing human interventions, and optimizing infrastructure provisioning. In order to work with relevant data, ensure regular assessments of the time between code merge and production deployment, and strive for more efficient workflows that minimize delays.

Operator Interventions

This metric is used to track the number of deployments that require human intervention and can’t be fully automated. A high rate of operator interventions directly correlates with a greater number of areas where automation should be improved to then ensure reliability and reduce manual work.

You could improve this indicator by increasing automation in the deployment pipeline and its stages (automated testing, validation, and deployment steps). Reliable automation will allow you to be less dependent on manual interventions and make deployments smoother. Lastly, monitoring deployment logs will provide you with insights into your progress and bring your attention to areas that require further automation.

Number of Changes per Release

This metric demonstrates the number of changes (such as code, configurations, or other components) that are included in each release. A higher number of changes per release can be a sign that work is being batched, which can result in delays and a greater risk of defects.

The metric could be improved if you switch from large releases to smaller, more manageable and frequent ones: smaller releases make it easier to gather feedback, reduce risk, and troubleshoot issues. Finally, to optimize the release scope, ensure that you possess a thorough understanding of your organization’s needs and your system’s features to assess the ideal number of changes per release.

Deployment Frequency

Another metric, deployment frequency, indicates how often code is deployed to the production environment. This indicator allows DevOps teams to assess how quickly they can deliver changes, enhancements, and fixes to users. Faster delivery, reflected in a higher frequency of the number this metric reflects, often correlates with faster feedback loops and a more mature DevOps process.

Aiming to increase deployment frequency, consider optimizing the release pipeline to reduce manual processes and bottlenecks. This increase could be further enabled by automated testing, which will make deployments not only frequent but also faster and more reliable deployments. Constant reviews of deployment logs ensure that this indicator aligns with the pace you aim to achieve and strikes the right balance between speed and stability.

For Advanced Deployment Strategies

Rollback Frequency

Rollback frequency is a metric that assesses how often changes need to be reversed after deployment. A higher frequency might indicate two things: issues in the deployment process or gaps in quality assurance, or swift rollbacks automated by successfully utilized advanced deployment strategies.

In order to reduce rollback frequency, focus more on improving testing processes before deployment. Techniques like canary releases, blue-green deployments, and enhanced monitoring can help identify issues early and make rollbacks unnecessary.

Deployment Lead Time

Deployment lead time, a metric that measures the average time from deployment trigger to live production, is used to detect bottlenecks in the deployment process that could slow down the release of new features or bug fixes.

To reduce lead time, consider optimizing deployment strategies by leveraging automation, distributed architectures, or wave deployments to retain the balance between speed and safety. These steps will result in an improved pipeline with reduced manual interference.

Release Frequency

Release frequency is a metric that shows how often changes are made available to end users, which means it demonstrates a contrast between how often code is deployed into production versus when it is accessible to customers. Mature DevOps practices typically have high release frequency due to their ability to release small, incremental updates efficiently.

You can increase release frequency by automating deployments and focusing on small, frequent releases instead of greater ones. Keep in mind that in order for the pipeline to be efficient, it is crucial to optimize the release process to gain rapid feedback, validation, and automated rollouts.

Mean Time to Recover (MTTR)

MTTR is a metric that measures the average time required to recover from a failure in production. It provides insight into the team’s ability to quickly detect, address, and resolve issues without affecting users and increasing downtime.

To lower MTTR, consider implementing automated rollback mechanisms, and enhance monitoring and alerting systems for faster detection of issues. You can significantly speed up the recovery processes by establishing clear incident response playbooks and ensuring teams are competent enough to tackle issues promptly.

How to Prioritize the Right Metrics for Your Team

In order to choose the right metrics for your DevOps team to utilize and focus on, it is vital to understand the specific organizational goals, challenges, and stages of your DevOps journey. This is the kind of guidance a DevOps consulting provider can offer. Consider the following steps when choosing the right metrics.

Align Metrics with Team Goals and Business Objectives

An optimal starting point is defining your team’s primary objectives and the organization’s strategic goals and choosing the metrics accordingly. If your goal is to deliver faster releases, the most fitting metrics would be deployment frequency and lead time for changes. Alternatively, if your central aim is stability and reliability, change failure rate and MTTR would be a better choice. The metrics you prioritize in the end should be directly linked to your goals, whether it’s improved customer satisfaction, faster time-to-market, or enhanced product quality.

Focus on the Metrics That Impact Your Pain Points

Define what your team’s most critical issues are and choose the metrics that correspond to them. For example: your team struggles with slow feedback loops, and in that case, focusing on lead time or frequency of integration would be the best choice. On the other hand, if you struggle with errors in production, metrics like change failure rate or post-commit test failure rate should be the ones you prioritize. Overall, it’s crucial to be aware of and focus on the areas that require improvements the most, as it will further result in more effective use of resources and improvements.

Consider the Maturity of Your DevOps Practices

DevOps metrics may vary depending on the team’s maturity level. Here’s how it manifests:

• Early stages
  DevOps teams tend to opt for foundational metrics like build success rate, automated test pass percentage, and deployment frequency to ensure the pipeline is stable and automated.
• Mature teams
  These teams may be more concerned with keeping track of advanced metrics such as MTTR, lead time for changes, and release frequency to optimize processes, improve performance, and ensure constant growth.

Use a Balanced Set of Metrics

The right choice of metrics must provide a comprehensive view of your DevOps processes, so being overly focused on one area, like speed or stability, might not be the right choice. For example, if you concentrate on tracking deployment frequency but disregard measuring change failure rate, it might result in pushing out more features with increased risk. The most optimal approach is the one that strikes a perfect balance where none of the important facets is overlooked or underestimated.

Iterate and Evolve Your Metrics

The evolution of your business needs and technical challenges might push you to alter or shift focus from certain metrics. For that reason, it is important to constantly confirm that the metrics you’re tracking are useful through feedback, changing business priorities, and technological advancements. You might finish the optimization of deployment frequency and switch to scaling your testing infrastructure or improving system performance.