13 DevOps Metrics for Increased Productivity
DevOps improves software delivery speed and quality through a list of practices that pursue an agile mindset. The terms that first come to mind when you mention DevOps are continuous integration, continuous delivery and deployment, collaboration, automation, and monitoring.
DevOps means different things to different teams. Some teams are all about automation, while others do things manually and still consider that they are doing DevOps. Some consider it a culture and a mindset-shaper.
As DevOps revolves around continuous delivery and fast code shipping, it’s crucial to act quickly without any significant errors. That’s why it’s vital to track the DevOps metrics that can help you achieve this.
To succeed in DevOps, teams use many different tools. That’s why different DevOps metrics are essential for different dev teams.
So, before even beginning with DevOps, your team should determine what DevOps means for them. What is more, teams should also detect their biggest DevOps challenges. Then, it will be easier for them to decide which DevOps metrics they need to monitor more actively to improve and create a more quality software delivery process.
Here are the critical DevOps metrics most teams find important:
It is important to develop and sustain a competitive advantage to offer updates, new functionalities, and technical enhancements with greater quality and accuracy. The opportunity to increase delivery intensity contributes to increased flexibility and better adherence to evolving consumers’ requirements.
The aim should be to allow smaller deployments as frequently as possible. When deployments are smaller, software testing and deployment are much more comfortable.
Regularly measuring deployment frequency will offer greater visibility into which improvements were more successful and which segments require change. A rapid drop in frequency can indicate that other tasks or manual actions are disrupting the workflow. For sustainable growth and development, deployment frequency indicators that suggest minor yet constant changes are optimal.
Going one step further and making testing more manageable can measure both production and non-production deployments. This way, you’ll be able to determine the frequency of your deployments to QA and optimize for early and smaller deployments.
Adding this metric is in Microtica’s roadmap. As Microtica provides a build and deployment timeline, we’re planning to add a feature to show you your build and deployment frequency.
This metric measures how long you need to perform a deployment. Even though it might initially seem irrelevant, measuring deployment time is one of the DevOps metrics that can indicate potential problems. For example, if your deployment takes an hour, there must be something wrong. That’s why it’s better to focus on smaller but more frequent deployments.
This metric is also on our roadmap. We’re currently only capturing build time.
Percentage of automated tests pass
It is strongly recommended that the team make effective use of unit and operational testing to maximize velocity. Since DevOps depends heavily on automation, a useful DevOps metric is to measure how well the automated tests perform. It is useful to know how many code adjustments cause the tests to break down.
This metric counts the number of commits the team makes to the software prior to implementing it into production. This acts as a measure of both the speed of development and the accuracy of the code. The team should come up with a standard range of code commits every team member should follow.
A large number of commits can mean poor code quality or lack of clear development goals. On the other hand, when the number is lower than the standard range, the team could lack productivity or good organization. It is necessary to discover the cause behind the drop or raise of the number of commits to retain efficiency and project pace while still maintaining maximum happiness among the team members.
Defect escape rate
No matter how experienced you are in DevOps, mistakes occur — especially when you often make adjustments. Software development involves experimenting and, as part of the process, you should always anticipate errors.
The defect escape rate metric shows your ability to catch software defects before they go to production. This is especially important if you want to deliver code fast. In order to succeed in this goal, you need to be efficient in detecting defects.
Although the cloud is a great solution for cutting infrastructure costs, some unplanned errors and events can result in very high costs. That’s why you should focus on capturing unnecessary costs and trying to cut them down. Visualizing your spending sources can play a big role in understanding the actions you pay most expensively. The ideal scenario would be to use a tool that can automate your sleep cycles and wake up your environments only when you’re actually using them to cut costs.
Failed deployments & environment health
Deployments often cause problems for your users and sometimes, we have to reverse failed deployments. Even though it’s not something we want to have in our activities, we should always be aware that there is a possibility it happens. Frequently failed deployments are indicators of our environmental health, which leads us to the next metric.
Time to detection
Although reducing or even eradicating failed changes is the optimal approach, it is important to rapidly capture faults if they arise. Time for KPIs to be identified will decide if current response efforts are appropriate. The high detection time could trigger constraints that could disrupt the whole workflow.
This is the amount of time you spent on tasks that weren’t in the initial plan. In standard projects, the UWR (unplanned work rate) shouldn’t be over 25%. A high UWR could expose efforts that were wasted on unexpected mistakes that were obviously not noticed early in the workflow. Together with the rework rate (RWR), which refers to the attempt to fix concerns posed in tickets, the UWR is also an important indicator.
Mean time to failure (MTTF)
Mean time to failure (MTTF) is the average time a flawed system will manage to run until it fails. The duration begins when a significant flaw in the system happens and finishes when the mechanism finally collapses.
MTTF is used to track the state of non-repairable system components and to evaluate how long they can work before they fail. This metric also lets a DevOps team maintain the condition of components used in mission-critical systems when identifying failure.
Before performing the deployment, you should check for performance faults, unknown bugs, and other problems. You can also watch for changes in the overall program output throughout and after the deployment.
After a release, it would be normal to see significant adjustments in the use of certain SQL queries, web server calls, and other program requirements. To detect them, you can use monitoring tools that will show you the changes precisely.
Mean time to detection (MTTD)
When issues do emerge, it is important that you easily recognize them. You don't want to have a severe partial or large machine outage and not be aware of it. Setting up robust application monitoring can help you easily spot bugs.
Mean time to recovery (MTTR)
MTTR is a metric of success that tests the efficacy of the enterprise in solving problems. The ability to analyze the business and customer experience’s effect creates the perspective required to comprehend and prioritize concerns thoroughly.
MTTR calculates the total response time from failure to resolution and offers information over whether clients have lost control, encountered delays, or abandoned the system. Improving MTTR decreases the influence of these issues maintaining the happiness of users.
It is crucial to reduce the MTTR by getting practical application management tools in place to detect problems quickly and painlessly execute the patch.
An important metric for measuring workflow and efficiency is estimating the average time it takes for a project to go from a concept to implementation. Lower lead times suggest that the team is flexible, responsive, and can rapidly answer feedback.
DevOps-related agile methodologies can allow a quick processing period for framework improvements, allowing the business to satisfy consumer demands and focus on changing trends. You can use tools like Jira and Trello to capture your lead time efficiently.
As DevOps is all about frequent changes, you have to measure the rate of change between deployments to support your deployment frequency numbers. The end purpose should be to concentrate on impactful improvements that provide less inconvenience and lead to a smoother experience. For each deployment, monitoring the volume of change makes for a more precise depiction of development. You can get this information from tools like GitHub, Bitbucket, and Jira.
Positive customer experience is crucial to the survival of the product. Satisfied customers and good customer service result in increased sales volumes. That’s why customer tickets indicate the level of customer satisfaction, reflecting the quality of your DevOps processes. The lower the number, the better the service.
To sum things up
The aim of DevOps is to promote coordination and collaboration between dev and operations teams in a way that supports the rapid execution of applications while minimizing outages, delays, and problems that have a negative effect on the experience of the end-user.
It depends on your market’s particular problems and needs to select the specific DevOps metrics to track. Choosing the right success indicators to monitor will help direct strategic decisions related to development and technologies while supporting the execution of current DevOps activities.