How to Measure and Improve Deployment Frequency

Spotting all the process bottlenecks in time

Deploying changes and updates rapidly is crucial because it allows teams to quickly respond to user feedback, fix bugs, and release new features. It also helps in reducing the time to market, ensuring that the software can adapt to changing requirements and emerging trends efficiently.

However, teams often struggle to figure out how fast they are. We've been there, too. Now, we track a set of Agile and DORA metrics that help us to track the velocity of overall process and spot all the bottlenecks in time. One of the metrics we track is deployment frequency.

Measuring deployment frequency will help your team track their progress, identify risks, and improve continuously. Let's dive into the details.

What is deployment frequency in DORA metrics?

Deployment frequency is a software development metric that measures how frequently new changes or updates are deployed to production. It's one of the key DORA metrics that reflects the speed and agility of a development team in delivering software changes to end-users.

DORA (DevOps Research and Assessment) metrics are key performance indicators that measure software delivery performance, focusing on deployment frequency (how often they deploy), lead time for changes (how quickly they make changes), mean time to recovery (how fast they recover from failures), and change failure rate (how often changes cause issues). DORA metrics help teams assess their DevOps practices, optimize workflows, and enhance software development efficiency and reliability.

High vs low deployment frequency

Now, what does it mean to have a high or low deployment frequency?

Having a high deployment frequency means that your development team can quickly and efficiently deliver software changes. High-performing teams often deploy on-demand, sometimes multiple times a day, allowing them to rapidly respond to user needs and market changes.

<span class="colorbox1" fs-test-element="box1"><p>Key point: What is good deployment frequency? The more frequently you release new versions of your software, the more beneficial it is for its users.</p></span>

Conversely, a low deployment frequency indicates that your team struggles with getting changes into production efficiently. This can cause delays in addressing user needs, fixing bugs, and delivering valuable features.

<span class="colorbox1" fs-test-element="box1"><p>Key point: Teams with low deployment frequency might only release updates monthly, quarterly, or even less frequently.</p></span>

Why tracking deployment frequency?

Tracking deployment frequency helps your team to identify areas for improvement, optimize development processes, implement efficient deployment pipelines, and embrace agile practices like continuous integration and continuous deployment.

By monitoring deployment frequency, your team gathers feedback from users and stakeholders more quickly.

However, it's important to consider the team's deployment frequency alongside other metrics such as change failure rate and mean time for changes. This feedback allows you to identify bugs, receive suggestions, and make improvements promptly, leading to a more responsive and iterative development process.

Benefits of improving deployment frequency

Improving deployment frequency brings numerous advantages that will greatly impact the success and effectiveness of your development team. Identifying opportunities to increase deployment frequency can significantly enhance team productivity and engineering performance. Let’s explore five key benefits that arise from enhancing deployment frequency:

Addressing user needs in a timely manner

By increasing deployment frequency, software changes and updates reach end-users more swiftly. Engineering teams that focus on improving deployment frequency can deliver value to users more frequently. Therefore, users can benefit from the latest improvements and have their needs addressed in a timely manner.

Rapid feedback loops

Better deployment frequency allows your developers to quickly gather insights and respond to user needs. As a result, you will make necessary adjustments and improve your work promptly. Immediate user feedback on frequent releases helps you identify and fix bugs or issues promptly, reducing the impact on users. What's more, regular updates show users that their feedback is valued and acted upon, improving their overall satisfaction and trust in the product.

Enhanced quality assurance

Frequent deployments will encourage your team to adopt robust testing and quality assurance practices. Using automation testing in the deployment pipeline can help detect issues early and improve deployment frequency. By having smaller, focused releases, it becomes easier to identify and minimize errors. Hence your team ensures a higher level of quality in their work.

Risk mitigation

More frequent deployments promote smaller and more manageable changes. Smaller releases reduce the risk associated with larger, complex deployments, minimizing the potential impact of errors or disruptions. Consequently, your team will control risks, identify issues early, and mitigate any negative consequences more efficiently.

Risks of focusing on deployment frequency 

Well, it can't be that easy. Putting too much attention to increasing deployment frequency can also have its downsides.

Let’s explore five potential pitfalls that your product development team should be mindful of:

Sacrificing quality

Focusing solely on deployment frequency may lead to compromising quality. The pursuit of frequent releases might encourage your team to bypass rigorous testing. This will  result in the release of software with unresolved bugs or inadequate functionality. Therefore, prioritizing frequency over quality can leave your users dissatisfied and damage your product's reputation.

Neglecting user value

Depending exclusively on deployment frequency may divert attention from delivering valuable features. Your team might prioritize frequent updates over meaningful enhancements, resulting in a product that lacks significant user value. This can leave your users unimpressed and unsatisfied.

Lack of stability

Overemphasizing deployment frequency can also cause a lack of stability and reliability in your software. Frequent deployments may introduce an unstable environment with increased chances of errors and  disruptions. Therefore, frequent deployments without proper stability measures can negatively impact your users and hinder smooth operations.

What's the solution?

Try not to focus on deployment frequency only. Compose a set of metrics that will help your team spot the necessary bottlenecks in the process but without compromising quality. You can start with measuring the whole set of DORA metrics (deployment frequency, lead time for changes, mean time to recovery, and change failure rate).

How to measure deployment frequency?

In GitLab, deployment frequency is measured by the average number of deployments per day to a given environment, based on the deployment’s end time (its finished_at property). GitLab calculates the deployment frequency from the number of finished deployments on the given day. Only successful deployments (Deployment.statuses = success) are counted. - GitLab Docs

Calculating deployment frequency is crucial as it provides insights into the efficiency of your deployment process, with different perspectives on whether to include unsuccessful deployment artifacts and how it relates to business KPIs. Let’s explore the steps involved using a simple example:

Define the time frame

​​To measure deployment frequency, you need to establish a specific time frame for analysis, for instance a one-month time frame.

Count deployments

Within the defined time frame, count the number of deployments that have been released to production. For our example, let's say there were 15 deployments made during the month.

Calculate frequency

Calculate the deployment frequency by dividing the total number of deployments by the duration of the time frame. In our case, the calculation would be 15 deployments divided by 30 days, resulting in a deployment frequency of 0.5 deployments per day.

Consider granularity

Deployment frequency can be measured at different levels of granularity. It can range from measuring deployments per day, week, or month, depending on the context and your team's goals. 

Analyze trends

Tracking deployment frequency over time will enable you to identify trends and patterns. By comparing the deployment frequency between different time frames, your team can assess if they are improving their delivery speed or facing challenges. For instance, if the deployment frequency increased from 0.3 deployments per day in the previous month to 0.5 deployments per day in the current month, it indicates improvement.

Contextualize the results

While deployment frequency is an essential metric, you should evaluate it in conjunction with other factors, such as user feedback, quality, and value delivered. Using CI/CD tools to automate deployment processes can save time, reduce errors, and accelerate deployment frequency. A high deployment frequency alone does not guarantee success if the quality is compromised or if the delivered features lack value.

Alternatives to deployment frequency

You can find alternative metrics and approaches that can provide you with valuable insights in different contexts. Let's explore a few of these alternatives and when to consider each option:

Lead time

Lead time measures the duration it takes for a feature or change to move from the idea stage to production deployment. It includes all the activities involved in the development process, such as analysis, coding, testing, and deployment.

Choose lead time as an alternative when you want to understand the end-to-end efficiency of your development process and identify bottlenecks.

Mean time to recover (MTTR)

MTTR measures the average time it takes to recover from a failure or incident and restore normal operation. It focuses on minimizing downtime and quickly restoring service. MTTR is especially relevant in situations where stability and system reliability are crucial, such as critical production environments. 

Choose MTTR as an alternative when you need to prioritize stability and rapid recovery from failures.

Value delivered

Value delivered focuses on measuring the impact and value of the features or changes delivered to users. It involves assessing how well the software meets user needs, achieves business objectives, or generates desired outcomes. Value delivered can be measured through metrics such as user adoption, revenue impact, or user feedback on the value received.

Choose value delivered as an alternative when you want to emphasize the impact and business value of your software.

Customer satisfaction

Customer satisfaction score is a qualitative measure that indicates how satisfied users are with the software or its features. It involves gathering feedback directly from users, either through surveys, interviews, or other feedback mechanisms. Customer satisfaction provides insights into the perceived value, usability, and overall experience of the software. 

Choose customer satisfaction as an alternative when you want to focus on user-centric metrics and assess the impact of your software on users' needs and expectations.

Defect density

Defect density measures the number of defects or issues found in the software per unit of code. It helps assess the quality of the software and identifies areas that require improvement. You can measure it by counting the number of bugs, issues, or customer-reported problems.

Choose defect density as an alternative when you want to emphasize the quality aspect of your software and identify areas that need attention to improve overall reliability.

Next steps

Deployment frequency is a vital metric in software development that showcases a team's ability to deliver valuable changes to users. By increasing deployment frequency, teams can enhance their agility, responsiveness, and ultimately, the satisfaction of their users. However, remember that every team needs to tailor a set of metrics that best suit their unique circumstances.

Delve into the world of software development metrics exploring our articles:

• Technical Debt Ratio
• Cyclomatic Complexity
• Mean Time Between Failures