It takes no brainer to know that companies that don’t wield the DevOps model will not compete in our fast-paced world. They will be easily outpaced, outperformed, and most certainly: they will barely feed on their competitors’ leftovers. By encouraging developers and operators to work together, and using DevOps metrics to improve their merger, the DevOps model helps the teams of a company deploy products and applications faster. And thanks to the benefits of DevOps, teams become agile and ready to prey on the market’s changes and customers’ needs.
How DevOps Works
What Exactly Is DevOps
DevOps is a set of practices and procedures that integrate developers and operators to work on projects closely and simultaneously. This enables companies to automate the production system. And it helps increase the velocity and frequency of code deployment and app delivery.
Newly integrated teams may not perform as desired. But it’s a given fact that once they pile up the necessary DevOps skills, the Devs and Ops will be unrecognizable.
Under some other DevOps models, most of the production teams are no longer siloed. Different departments, including but not limited to the quality assurance and security, may also join a DevOps crew. But when the gears shift from focusing on development and operations to security DevOps, the new crew structure is known as DevSecOps.
DevOps in Practice
The typical production model consists of numerous unplugged steps. At first, the developers write codes for new products, apps, features, updates, and bug fixes, etc. Then the codes are deployed to the testing environment where bugs and problems burst out of the ether.
This means the whole code must be pulled back to the developers to fix the newly found issues. And they might already be busy writing a new feature or application’s code. The outcome is a bunch of frustrated and drained developers who are prone to making more mistakes.
Added to the unhappy developers are the operators. The operators carry the responsibility of controlling the workflow and granting products and apps delivery within a scheduled deadline. But in the light of unpredicted and accumulated problems, they feel helpless in the face of delay.
DevOps was developed to solve this very problem! Developers in this paradigm write codes in small chunks rather than a whole project at once. These small codes are continuously integrated, monitored, and then deployed in hours rather than weeks or even months.
Problems will be spotted faster, and the developer will know exactly what to fix and how. The workflow will be seamless, and the production frequency will increase. Companies, thanks to the benefits of DevOps, are now capable of staying competitive and fulfilling the market’s needs on time.
The Four Key DevOps Metrics
There is an unlimited number of metrics on how to measure DevOps success. While each company may have to come up with its own DevOps model depending on its infrastructure and production nature, there are four essential key metrics that help measure and improve the DevOps success for most -if not all- models.
The four key metrics of DevOps are:
1 - Deployment Frequency
Deployment Frequency refers to the frequency of deploying new codes to the production and testing environments. DevOps attempts to increase the velocity of this deployment and make it as frequent -on a daily or even hourly basis- as possible.
So, in order to do this, the batch sizes should be as small as possible. Or in simpler English: ship as many codes in small chunks to the production at a time as possible.
One might assume that this will result in prolonging the processing period and also increase the risk of creating more problems. But counter-intuitively, the more you work on small chunks and make minor changes on products, the better and more comprehensive understanding you have in mind. Consequently, solutions can be generated even before the problem grows and becomes uncontrollable.
In other words, if the developers ship a bunch of big code chunks at once and a problem occurs during tests, how easy would it be to identify the problem? Which developer is responsible for that error -if identified-? And in order to fix it, how many other coders should get involved?
So, what you’d rather do is to ship each small and individual change or written code at a time, and deal with it as a single brick of a wall. Now, all that’s left then is DevOps automation.
A successful team at deployment frequency is one that reduces the batch or code’s error frequency. By measuring this DevOps metric’s success, a specialist can identify repetitive and preventable mistakes, and then you can offer your teams training and alternative solutions to avoid these problems in the future. The specialist can even suggest slight adjustments to the automated DevOps process and run a new DevOps testing until the best version is discovered.
2 - Lead Time for Change
Lead Time for Change or Change Lead Time is the duration it takes to finish working on a new feature, bug fix, update, or any other change or improvement related to the code deployment process or production. Change lead time is measured from the moment the developer is assigned to work on a code, to the moment it’s shipped to production.
By accelerating it, deployment frequency is expected to increase, and thus the overall production of a company. But what is the best way to implement this KPI metric?
The best way to do it is, as you may predict, by splitting the overall process into smaller processes. For instance, you can split the production duration into: a) the time it takes developers to write code, b) the time it takes the deployment process to push a change to production, c) testing and getting the reports ready.
By observing each of these three basic processes, you’re going to point out which phase takes the longest period of time. And then you can work on optimizing it.
Most of the time it would be phase C: ‘the testing phase’. The best way to accelerate it is by integrating testers to work closely with the developers. They will be able to test everything as it’s being produced and make the necessary improvements even before shipping the code chunks to be deployed.
3 - Mean Time to Restore
Mean Time to Restore, also known as Mean Time to Recovery or MTTR, is a critical step in the incident response process. MTTR focuses on how quickly teams can ship changes out once a problem or an incident is detected. The time it takes is regarded as downtime; the time during which the workflow is interrupted by the incident. And it’s measured from the moment of detecting the problem to the final click in the process of fixing it. This process by itself includes a bunch of other small processes.
In order to measure the MTTR, you should add up all the time spent dealing with incidents in a specific period of time. Assuming a system of production was down ten times in one week for a total of two hours, the MTR or downtime then is two hours split on ten! 12 minutes that is.
As solving occurring problems is a repetitive task for your team, limiting the downtime is essential to improving the overall production score of your company. To do so, you will need a lot of more accurate data about what goes on during the downtime.
How long does it take between a failure and alerting the team about it? How quickly are problems identified? What processes or phases can be improved and accelerated?
As you see, these are very detail-oriented DevOps measurements that may be regarded as futile by average players. Why bother improving it if it’s only one or two hours in a workweek? But professionals know how much can be accomplished in two hours, and they know that it could be the separating line between getting a big client or losing him.
4 - Change Failure Rate
The Change Failure Rate metric calculates the percentage of deployments failing to reach the production step, and what risk they might bring to the development of new products. This metric highlights the efficiency of your overall DevOps model, and by measuring its success, you can find more ways to benefit from DevOps.
Failing deployments and processes will always be a part of any DevOps team’s day. But the goal is not to eliminate but rather limit them as much as possible. So, for instance, if 5% of the deployments fail and cause an incident (downtime), a DevOps agent will create and suggest new improvement procedures based on the causes of the 5% failure rate.
Other Successful DevOps Metrics and KPIs
Optimizing the four key metrics of DevOps gives companies a world-class line of production. But the sky is the limit. There’s always more space for growth and better performance. The following are more DevOps metrics and key performance indicators to make your company an elite in the industry.
1 - Customer Ticket Volume
Often, some bugs and errors can sneak through the testing phase and then get discovered and reported by end-users. Customer Ticket Volume (CTV) measures the volume of reports issued by the end-users about bugs they find. It also measures to what degree they are satisfied with the applications they have in hand in the light of bypassing bugs and errors.
The volume of customer tickets -and their nature- indicates how successful a team was in producing an app. Studying this feedback provides precious insights on how you can produce better apps and get higher satisfaction levels in future projects.
2 - Defect Escape Rate
No matter how forward you push, and no matter how perfect the production infrastructure, nor how professional your employees are, some defects and bugs will always find their way to bypass the testing phase and reach the end-users.
Defect Escape Rate measures the defects found in products during and after deployment. The measurements include software cracks, development process problems, and bypassing errors and problems.
End-users' reports can also harm the reputation of a company if many are being issued per one app, or the same issue occurs in more than one app. And on the other hand, bugs and issues found during the testing phase can slow the production down.
To avoid that, continuously check the nature of feedback tickets you get and spot what part of the production pipeline is most responsible for recurring errors.
Intensifying the tests being made on apps is pretty beneficent. As we mentioned earlier, when testers work closely with developers, fewer issues are found. But even when doing so, a deep testing phase should be waiting for the app once produced.
3 - Error Rate
Errors are unavoidable. But high rate points of an error indicate that there’s a problem in the workflow, which leads to a dwarfish performance. Generally speaking, production errors and bugs are the most commonly faced errors in software and applications development.
Error Rate is then the frequency of facing a specific error in the development and deployment of a code or a whole application. This indicator is considered a problem when 2 or more errors are detected in 100 transactions (2% Error Rate). In this case, analyses and upgrades of the overall production system are a must.
4 - Automated Tests Pass Rate
Maintaining an upward -or at least a stable- deployment velocity requires automating and scaling the processes involved in code deployment and software production. Automation creates routine, and a routine creates production and stability in terms of production.
Automated Tests Pass Rate indicates how many processes were successfully automated with little to no flaws. Increasing the rate will result in an increase in production, but it may also increase the bugs and errors found by end-users.
To avoid that, continuously test and upgrade the automated processes of the DevOps system you follow and give priority to top-notch code writing rather than hasty product delivery.
In a nutshell
Adopting a suitable DevOps model will transform your company. Not only will it increase your ROI but it also will keep your team organized and ready to take over new products deployment. But there’s always room for improvement, and that’s where DevOps metrics and KPIs kick in and make your life better.