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DevOps disrupted the SDLC ecosystem. What started as a software engineering practice has become a philosophy that fosters digital transformation and cultural change. By employing automation, DevOps bridges the gap between development and operational teams, enhancing collaboration and communications. This benefits engineering processes, supports real-time deployments and reduces downtime and outages.
For those implementing the practice, measuring DevOps is a crucial task to enable them to improve their workflows and create a stable, innovative environment. Monitoring its effectiveness is done using the DevOps Research and Assessment (DORA) metrics, adapted from lean manufacturing principles. These are Deployment Frequency (DF), lead time to changes (LTTC), mean time to recovery (MTTR) and change failure rate (CFR).
Waydev understands the importance of automating DORA monitoring and offering tech leaders instant access to data that enables them to assess DevOps velocity and performance. This is why our DORA Metrics Dashboard report provides visibility into the four key Accelerate metrics, including Deployment Frequency, considered by many tech leaders the most relevant measurement of DevOps success.
This article will cover everything you need to know about Deployment Frequency, its role in the software development landscape, the importance of measuring it, and what best practices you may employ to enhance it consistently.
Deployment Frequency is also a data-driven Agile metric that fosters one of the main DevOps goals: CI/CD. DF measures how often code is deployed into production, taking into account everything from bug fixes and capability improvements to new features.
The metric is a strong indicator of an organization’s response time, efficiency, and capabilities, showcasing how much value end-users receive and how quickly they get it. This is why Deployment Frequency is considered a velocity metric.
Companies adopt DevOps because they want to increase their Deployment Frequency to support a continuous delivery environment. Many tech leaders see DF as an element that showcases the efficiency of DevOps practices within the organization.
According to 2020 Atlassian research, 75% of tech leaders in large enterprises considered better Deployment Frequency as the key indicator of DevOps success.
Measuring it also helps them assess the existing processes and see how different team or technology changes affect the working environment and productivity.
An excellent way of measuring DF is by tracking production and non-production deployments separately so that you can better view your team’s activity and overall deliverables. Even more, you can now measure this metric automatically, without the need for manual input. Our solution does just that, as it ensures engineers spend time fulfilling tasks, instead of reporting on their activity.
A tool like Waydev helps you monitor Deployment Frequency, connecting all valuable information under one easy-to-use, intuitive dashboard. Our platform automatically tracks and displays DORA metrics, showcasing DF visually, and clearly, without requiring users to insert or aggregate data manually.
DevOps teams know they should constantly increase their number of deployments, but, since there is no one-size-fits-all approach, there also isn’t a right answer on how often you should deploy.
To set a benchmark, in 2021, Google’s DevOps Research and Assessment Team analyzed data from over 32,000 professionals worldwide, examining their software delivery capabilities and techniques. When it came to Deployment Frequency, the research split the responding companies into four categories: Elite, High, Medium, and Low performers, each with their ranges of deployments.
According to the 2021 State of DevOps report, the performance levels of DF are:
→ Elite: on-demand, multiple times per day.
→ High: between once per week and once per month.
→ Medium: between once per month and once every six months.
→ Low: fewer than once per six months.
As the research shows, the top performers have an average of 4 deploys per day (which means 1,460 deploys per year) and less than 2 deploys per year for low performers, as they usually ship between one and two changes per year.
The same analysis points out that elite companies deploy code 973 times more frequently than the organizations at the bottom of the charts.
Low Deployment Frequency is a symptom of other elements that interfere with the development process.
Low Deployment Frequency can suggest that the changes introduced in the code are extensive, thus requiring complex development and testing efforts. As an engineering leader, it is crucial to see whether changes are significant from the start or whether your team combines changes into bigger portions, thus causing delays.
Focusing on smaller increments helps teams deploy faster, thus ensuring that end-users benefit from value more frequently.
Lack of human resources, skills, or changes in the team structure may result in a decreased DF. For example, if one engineer leaves and is replaced by another one, a low Deployment Frequency will showcase that the team needs additional support to get back on track.
Successful tech leaders use the metric as an indicator of team success and take advantage of opportunities to assess the combination of people, skills, and talent that they bring to the table. Sometimes, bringing new forces to the table might just get the job done.
Deployment Frequency is negatively impacted by bottlenecks, dependencies, and the usage of inappropriate tools that don’t support team members in completing their tasks.
Using the right technology, DevOps companies enjoy higher throughput and velocity.
Some processes include unnecessary steps that make the route to production more complicated and complex. A low Deployment Frequency might point in that direction. Hence, it is always an excellent idea to take a step back and look at the overall process to identify ways of simplifying and automatizing it.
There is a misconception about DF, as some believe that frequent deployments are associated with higher risks, seeing the two as directly proportional. Still, things are the other way around. Frequent deployments in small batches are a sign of healthy delivery because they involve less code and, thus, fewer risks.
Shipping less complex changes into production but more consistently makes it easier for engineering teams to identify errors and bugs and solve them rapidly before they escalate.
When it comes to improving an organization’s Deployment Frequency, the first thing you have to do is work with smaller changes so that developers benefit from rapid feedback and have the capacity to resolve issues quickly and effectively. Apart from that, there are several other best practices you can take into account.
To have continuous deployment, you need continuous delivery, so a steady CI/CD pipeline is crucial for increasing delivery speed. Elite organizations work with dedicated engineers, ensuring pipelines function to their maximum capacity, thus keeping output consistent.
These companies track and review how much time projects spend in the CI/CD pipelines, verifying how much each step takes so that it remains in the margins.
Sometimes, teams that work on larger monoliths feel that they can’t deploy at the same pace as those working on microservices because their changes are more complex. While this is partially true, following similar processes, they may still deploy several times daily.
This is done by using release trains that combine multiple changes and coordinate their release. While the process is not a simple one, it brings significant value to the end-users, who enjoy new features and fixes with a higher frequency.
Automating the deployment process is key to improving throughput. The fewer human interventions necessary, the higher the speed with which teams deploy into production.
Introducing automated tests early in the CI/CD pipeline enables teams to identify issues in time and solve them before they cascade and affect the overall delivery. Knowing this, high-performing teams use builds and tests for every code commit, thus managing to mitigate incidents and minimize bugs.
Automating code review is also an excellent way to improve a team’s throughput. Waydev’s Code Review feature enables you to view a map of pull request activity so that you may identify long running PRs, PRs merged without review, or those which have been declined or closed.
Technical debt left unmonitored and unresolved is an essential factor that slows down the software deployment process. Constantly reducing technical debt and removing bugs helps you increase deployments, contributing to enhancing your DF values.
Deployment Frequency is a crucial metric that shows you how successful your organization is at DevOps. This is why tech leaders everywhere pay much attention to it, creating strategies around it.
If you are committed to increasing your DF value, you have to start by investing in visibility. Analyzing data enables you to benefit from a baseline that you may use to set objectives and compare results.
Waydev is the new Data-Driven method of tracking the output of engineering teams directly to Git repositories without requiring manual input. The highly flexible approach offers you real-time insights and metrics to measure, compare and improve Deployment Frequency, together with all your complementary DORA metrics.
Contact us and we’ll walk you through our platform, so you have full visibility on your team’s Deployment Frequency.
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