The best teams are able to go from code committed to code running in production in less than one day, on average. Deployment Frequency measures how often code changes are deployed to production. A high deployment frequency can indicate that the team’s development process is efficient and they are delivering features and functionality pretty quickly to customers. DF defines the final results of your SDLC, as it directly impacts how buyers/users consume a product. Executives need to understand the benefits of tracking DORA metrics and how it can help the organization improve its software delivery performance.

Some ecosystem tools like GitLab are beginning to include integrated support too. Eventually you’re going to run into an issue that causes pain to your customers. The fourth DORA metric, Time to Restore Service, analyzes how effectively you can respond to these events.

What are DORA Metrics and Why Do They Matter?

The DORA Metrics, a research program conducted by industry trailblazers Dr. Nicole Forsgren, Gene Kim, and Jez Humble, would redefine what we know of software delivery performance. Their innovative ideas became an industry benchmark for identifying what’s needed to understand potential pitfalls and practical ways of improving software delivery performance. Their proposed models have proven to optimize OKR for DevOps teams’ performance and drive the success of tech organizations across all industries. If you are involved in software development, you’ve probably heard of DORA Metrics.

The book was thus published in 2018, and it provides the data collected from their State of DevOps annual Reports starting from their early research in 2014 up to the moment of publication. You can also send out alerts through a variety of channels, allowing you to integrate into existing ticketing or messaging systems to record the MTTR in a way that makes sense for your organization. For our purposes we integrate with the Pub/Sub message bus channel, sending the alerts to a cloud function that performs the necessary charting calculators.

Change Failure Rate:

When the time elapsed between the first commit and release is too long, this can be an indication of certain issues, such as bottlenecks that delay deployment or an inefficient workflow. A long LTTC can have a negative impact on your organization, resulting in customer dissatisfaction and low competitiveness in the market. And as with all metrics, we recommend to always keep in mind the ultimate intention behind a measurement and use them to reflect and learn.

A DevOps culture encourages continuous innovation, feedback and collaboration between developers, operations, and other stakeholders. This involves celebrating successes, fail-forward learning, experimentation, and embracing a ‘fail fast’ attitude. Moreover, encouraging team training, sharing of knowledge, respect, and transparency boosts motivation and engagement. The metrics can work wonders when the system is breach-proof, even reducing downtimes, and in turn, MTTR.

Balancing Speed with Stability: How Velocity Metrics Contextualize DORA Metrics

Over time, the lead time for changes should decrease, while your team’s performance should increase. In GitLab, Lead time for changes is measure by the Median time it takes for a merge request to get merged into production (from master). Change Failure Rate is simply the ratio of the number of deployments to the number of failures. This particular DORA metric will be unique to you, your team, and your service. The common mistake is to simply look at the total number of failures instead of the change failure rate.

• Learn how to measure the success of your DevOps initiatives using deployment frequency, lead time for changes, cycle time and more.
• It’s usually expressed as the number of deployments per unit of time, such as deployments per day, week, or month.
• A DevOps culture encourages continuous innovation, feedback and collaboration between developers, operations, and other stakeholders.
• If one or more incidents occur after deployment, that is considered a “failed” deployment.
• It is calculated as the number of issues divided by the total number of changes attempted in a given period.
• He explains what DORA metrics are and shares his recommendations on how to improve on each of them.

For software leaders, Time to restore service reflects how long it takes an organization to recover from a failure in production. Low Time to restore service means the organization can take risks with new innovative features to drive competitive advantages and increase business results. For software leaders, tracking velocity alongside quality metrics ensures they’re not sacrificing quality for speed. In the Four Keys pipeline, known data sources are parsed properly into changes, incidents and deployments. For example, GitHub commits are picked up by the changes script, Cloud Build deployments fall under deployments, and GitHub issues with an ‘incident’ label are categorized as incidents.

Increased delivery speed

Mean time to resolution (MTTR) is a measure of the time it takes from initially detecting an incident to successfully restoring customer-facing services back to normal operations. This is a measurement of the overall effectiveness of an organization’s Incident Response and Problem Resolution Process. For IT operations teams, MTTR is an important metric that can provide insight into how efficiently they can identify and fix problems as soon as possible.

This way, potential issues can be identified and managed faster before they are released to production. Measuring Change Failure Rate means measuring code quality, and this is a crucial point to cover when it comes to DevOps teams’ performance. Lead time for changes is a measure of how long it takes between receiving a change request and deploying the change into production.

DORA metrics in GitLab One DevOps Platform

To minimize the impact of degraded service on your value stream, there should be as little downtime as possible. If it’s taking your team more than a day to restore services, you should consider utilizing feature flags so you can quickly disable a change without causing too much disruption. If you ship in small batches, it should also be easier to discover and resolve problems. Middleware helps collect crucial performance data from various sources, while Zenduty’s incident management platform provides a centralized system for incident tracking and resolution.

Sometimes there’s a particular repo/developer falling off the guardrail which should be spotted and rectified. B.Low deployment frequency means lower iteration which might not be suited if you’re a fast growing team. It would seem natural to look at daily deployment volume and take an average of deployments throughout the week, but this would measure deployment volume, not frequency.

Myth #3: DORA metrics are too difficult to understand

When a merge request gets merged on staging, and then on production, GitLab interprets them as two deployed merge requests, not one. Metrics are how your team knows how well they’re progressing towards those goals, so don’t focus on the metric, focus on your team and its goals. Give them the tools they need to succeed because your developers are going to be the ones to be able to make the best changes to help your team reach its goals. It’s about the developers wanting to improve their team’s efficiency and using metrics to know whether they’re successful in their improving efforts.

According to DORA’s research, high performing DevOps teams are those who optimize for these metrics. Organizations can use the metrics to measure performance of software development teams and improve the effectiveness of DevOps operations. Lead Time for Changes is a key indicator of the efficiency and effectiveness cloud security companies of an organisation’s development and deployment process. By identifying and reducing bottlenecks in the process, organisations can make changes and deliver them to customers faster. Lead Time for Changes measures the time that it takes from when a code change is committed to when it is deployed to production.

The goal of measuring this DORA metric is to understand how quickly an organisation can restore service. A lower MTTR is generally considered better, as it indicates that the organisation can minimise the impact of incidents on customers. The best DevOps implementations will facilitate quick changes and regular deployments that very rarely introduce new errors. Any regressions that do occur will be dealt with promptly, minimizing downtime so customers have the best impression of your service. Tracking DORA trends over time lets you check whether you’re achieving these ideals, giving you the best chance of DevOps success. You can calculate your change failure rate by dividing the number of deployments you’ve made by the number that have led to an error.