When it comes to software delivery, the present era is highly competitive where every enterprise is in a race to deliver the software as quickly as possible. With that in mind, enterprises take all such measures to deliver the software quickly without compromising its functionality. One such approach is implementing DevOps culture.
DevOps is the collaborative approach of the development and operations team to reduce the software development lifecycle. Though the motive of its implementation is clear, calculating its actual performance is also essential to identify the areas where improvement is required. In order to fulfill this task, engineers worldwide use the DevOps Research & Assessment program (DORA) as the benchmark. This article elaborates everything about DORA metrics, their elements, and why it is the foremost method of analyzing DevOps performance.
DORA metrics are a set of measuring metrics used by legions of DevOps engineers to measure their performance. The results are divided into four different categories depending on their performance naming; low, medium, high and elite where low is the poorest performance and elite depicts the best performance.
Under this program, four different metrics are calculated and those are deployment frequency, lead time for changes, mean time to recovery, and change failure rate. But the question that arises here is why only these four metrics are used. The answer to this is the in-depth and six-year-long research and surveys done by the DORA team. While these metrics allow the engineers to track the DevOps performance, the main goal is to identify the progress they have made from the last year. All in all, these metrics will track the progress of their overall goals including better quality, enhanced performance, and quicker release cycle of the software program.
Prior to moving forward with the metrics, it is essential to understand the ways how they will aid the entire DevOps culture. Here are all the pros of using these metrics to track the improvement of DevOps.
The process of improvement begins by analyzing the current performance. Every measurement attracts some modification; be it positive or negative. In several situations, this change can be negative, making the measurements useless. However, when it comes to DORA metrics, they are created and decided after years of research. All these metrics also bring some change in the culture, these changes are deliberate and every engineer wants to have these modifications. Every metric in this program puts light on inefficiencies and motivates the team members to make them better. Furthermore, it brings in positive change rather than negative, making the optimization better and effective.
The entire notion of using these metrics is to improve the software development process. However, you need to find out the vulnerabilities in the existing process and determine the areas where you need to focus. DORA metrics will provide you with clear tracking of the process and allow you to make a better decision. These decisions will not only eradicate any hindrances but enhance the development process as well. Rather than just relying on other details, DORA will provide you with detailed information on the process, providing better decision-making.
The core motive of every business is to deliver exponential value to their customers. DORA metrics analyze these values and help you in determining whether your team is at par with the expectations or not. When the metrics are on the positive side, you can continue working with the same goals as the team is putting in sheer effort to deliver value to the customers. Cutting it short, these metrics will provide you with a clear chart of the actual performance of your team.
As mentioned above, four different metrics were determined as the outcome of the six-year-long research. Each of these metrics covers an essential aspect of the DevOps culture, allowing you to track its performance. Without further ado, below explained are all these four metrics.
The first on the list is the deployment frequency and as per its name, it calculates the frequency of the code deployment in an organization for a program. In simpler terms, it is the calculation of how regularly an enterprise deploys a code for an application. The frequency of the deployment influences the frequency of the changes deployed to the users directly. However, it should be understood that the important part is the size of the deployment, rather than just its frequency.
Due to this factor, several tech goliaths intend to prefer frequent yet lighter deployments. The general benchmark is a single deployment every week. However, it solely relies on the organization and the type of program. For instance, the number of deployments for a SaaS solution will be much higher than that of a smartphone application.
The reason why this metric is highly significant is that the number of successful deployments depicts the confidence of the team in their process along with their efficiency while working on the code. Any team working efficiently will surely be confident in their work, resulting in frequent but effective updates. When it comes to its calculation, the number of deployments done by an enterprise in a single day is calculated. If the team has a CI/CD tool that offers API to its actions, this action can be automated, allowing calculation of deployment frequency with ease. Sometimes, the results may not come as expected, leading to disappointment. However, there are several ways to improve the deployment frequency results including minimizing error recovery time, integration of CI/CD tools, and enhancing automated test coverage, among others.
Lead time for changes in the metric which measures the mean time consumed from committing the code to its release. This metric allows the engineers to understand the process from its velocity perspective, allowing them to get a clear picture of the cycle time of the team along with the handling of the rise in requests. Measuring this metric is uncomplicated, only the time of commitment and deployment of the code is required and the average time is used to calculate the results. The lesser the team scores in the results, the better performers they are.
The lesser average time depicts the team’s coding and deployment efficiency. However, not every team can score the highest results. Considering that factor, actions like creating an excellent code reviewing process, implementing automation to the deployment, segregating projects into smaller yet easy to handle projects, and making CI/CD process efficient will help in improving the overall score on this metric.
In the IT industry, failures are inevitable, but recovery from those failures is possible. With that being said, the next metric to measure is the meantime to recovery where the average time taken by the team to recuperate from a system failure. Though the term failure does not mean the entire system failure but can be underlying bugs in the code as well. Without a doubt, interruption in the system function can hamper the performance and may even cost hefty expenses to the organization. Due to this reason, the average time to recovery is calculated to get the exact idea of how the team performs when they face any such issue.
This metric will allow the teams to create reliable systems, keep an eye on such failures and create a robust plan to tackle them to avoid higher time consumption. Moreover, it will contribute to making the team cautious about the quality of their development process. The average time taken from receiving the failure report to its actual resolution is measured in this metric. The lesser time a team takes to deploy the fix, the better results it will receive. To improve the score, the teams have to ensure that they build a robust CI/CD system that reports any failure with the least time, reduce the solution deployment time, take instantaneous action on any failure, and put their focus on failure recovery rather than diverting on any other task.
Undeniably, the code has to be changed at some point to keep the program stable and implement new features. However, this change in code does not always go as planned, resulting in program failure. The change failure rate metric calculates the frequency of occurrence of failure upon implementing a modification in a code. Sometimes, these changes result in a rollback, bug, or production failure and are considered in change failure rate. Consideration of this metric is essential as it measures the stability and quality of the program.
To calculate the change failure rate, the number of deployment failures is divided by the number of total deployments done on the program. The lower results are achieved, the better the team is performing. If the result does not come as anticipated, improving the automated testing, reviewing the code before deployment, and covering the code by automated unit tests are some of the ways to enhance the team performance and attain a better score in this metric measurement.
No matter the results, one thing is sure in IT is that nothing comes without challenges and the same applies to DORA metrics as well. There are certain hindrances that the engineers have to overcome to attain an accurate outcome and those are explained below.
There is no denying the fact that DORA metrics are probably the most effective and reliable way to analyze DevOps performance. Relying on just one method for evaluation is not just the right solution to take for the betterment of the team. In order to get a crisper picture of the actual performance, you need to keep into account some additional considerations as well.
Usage of metrics is necessary to track performance and DORA metrics are proven to be highly effective in tracking DevOps performance. These metrics are a result of years of research and survey and ensure that they target the key performance areas, allowing engineers to get an accurate depiction of their DevOps performance. In addition, the results from these metrics can be used to improve the efficiency of the team and ensure that the program comes with excellent quality code in the minimum time.