15 DevOps Metrics and KPI’s: Measuring DevOps Success

With the rising wave of using DevOps in an organization, everyone wants to try it out and implement it to make the software deployments faster and more efficient. Without a doubt, the proper implementation of DevOps provides guaranteed results. However, taking the right decision at the right time is equally crucial in achieving the stipulated outcome. 

Even implementing the strategy that has worked previously may not provide the outcome the experts were looking for. With that in mind, monitoring the DevOps performance is highly pivotal to ensure that the results are never compromised and you always help boost the software development lifecycle. This article will elaborate on some essential metrics and key performance indicators of successful DevOps that will allow you to determine whether your DevOps culture is providing optimum results or not.  

Devops Metrics and KPIS

Key DevOps Metrics:

#Metrics 1: DORA Metrics:

The DevOps Research and Assessment, aka DORA, with their six years of research, came up with four key metrics that will indicate the performance of DevOps. These metrics are also known as The Four Keys. They rank the DevOps team’s performance from low to elite, where low signifies poor performance and elite signifies exceptional performance towards reaching their DevOps goals. Deployment Frequency, Lead time for Changes, Change Failure Rate and Mean time to Restore Service are the four pillars of DORA metrics, and these are explained in detail below. 

  1. Deployment Frequency: The deployment frequency metric gives an insight into how frequently an organization releases software to production successfully. With the implementation of CI/CD in teams, deploying the software has become more frequent than ever. Teams release software even several times a day, leading to improvements in the existing software, pushing bug fixes, and adding new features. Moreover, the frequent deployment also expands the scope of quickly attaining real-time feedback, allowing the developers to work on the next release quickly. However, the reason why deployment frequency is measured is to measure the short-term as well as long-term efficiency of the DevOps team. Tracking this metric will allow the teams to identify any underlying issues that may be causing delays in release or service. To fall under the elite category, the median number of days per week should be a minimum of three where deployment has been made. Akin to that, high, medium, and low rank’s most deployments lie between once per week, once per month, and once every six months respectively.
  2. Lead Time for Changes: Evaluating the time consumed for a committed code to move into production is the lead time for changes. Calculating lead time for changes allows the DevOps teams to understand the time taken by the team to push the committed code into production, allowing them to determine their average response time for tackling issues. Furthermore, it also depicts their effectiveness in handling the issues.The general rule of thumb is that the shorter lead time for changes is better. However, this does not apply to every project. Complex projects may consume more than the average time. The team’s more time on a complex project does not necessarily mean that the team is ineffective. It simply showcases that the complex nature of the project made the team spend more than usual time. The difference between the commitment and deployment is the lead time for changes. If it is less than one day, it is ranked as elite. However, having lead time for changes is between one day and one week, one week and one month, and one month and six months, it is ranked as high, medium, and low, respectively.
  3. Change Failure Rate: The change failure rate is the ratio of failure and successful deployments in production. With this Azure DevOps metric, the team can analyze the efficiency of their DevOps process. There are two values required to calculate this metric; the number of deployments attempted and the number of failures in production. The number of deployments can be extracted from the deployment table, ultimately providing the incidents. These incidents can be in the form of spreadsheet pipeline, bugs, labels on GitHub incidents, or any other. By using these two numbers, analyze the change failure rate percentage. Elite teams score 0-15% in this metric, whereas high, medium, and low teams’ change failure rate lies within 0-15%b, d, 0-15%c, d, and 40-60%, respectively.If the team ranks low in this DevOps performance metric, they need to make several changes in their deployment process to minimize the probability of failures and enhance efficiency. Furthermore, they need automation in the DevOps process, leading to reliable production and deployment.
  4. Mean time to Restore Service: Calculating the time taken by the organization to recover from failure in production is the meantime of restoring service. One of the most crucial DevOps quality metrics, calculating MTTR, should be a standard practice in every DevOps environment. This metric allows the team to determine the stability of the recovery process. To calculate MTTR, the DevOps team needs to know when the incident happened and when it was resolved.Elite ranked teams have a mean time to restore service of less than an hour. However, high, medium, and low ranked teams take less than a day, less than a week, and between a week and a month. In most cases, if the team is capable of resolving the issues within a day is considered optimum. Any team consuming more than that time needs to make specific changes in the recovery process, like deploying automated monitoring solutions and deploying software in small increments.

#Metrics 2: Test Case Coverage:-

Test case coverage is the preference of several veteran DevOps engineers. It assists in eradicating defects in the early stages, eliminates unwanted cases, provides better control, and ensures smoother testing cycles. Test case coverage is the method through which the team can understand whether their test cases cover the application code or not. Moreover, test case coverage will also allow them to determine how much code is exercised upon running those test cases.

For instance, if there are 25 requirements and the tests created are 250, 225 tests are executed. In that case, the test case coverage will be 90%. The team can build other test cases for underlying tests through this number. 

Test case coverage is measured based on the lines of code. If there are 1500 lines of code, among which the executed lines are 600, then the test case coverage would be 80%. The test case coverage DevOps success metric is divided into code-level, feature testing, and application-level metrics.

#Metrics 3: Code Level Metrics:

The code level metric is based on the test coverage percentage method, which showcases the percentage of executed tests out of the total tests. Several experts prefer this metric as it provides an overview of testing progress. However, there is a limitation that counting code lines do not necessarily mean that it will perform as desired. 

  • Feature Testing: Feature testing is further divided into requirements coverage and test cases by the requirement. The requirements coverage helps understand the efficacy of the test cases in covering software requirements. To calculate this metric, you must divide the number of requirements covered by the total number of requirements and multiply it by 100.
    The other one is test cases by the requirement, which is used to determine the tested features and the tests aligned with the requirement. In most cases, a requirement will have more than one test case, so it is crucial to know about any failed test cases. Afterward, the test cases for a failed requirement should be rewritten as per the requirements.
  • Application Level Metric: The application-level metric is also divided into defect density and requirements without test coverage. Defect density helps the team identify the areas where automation is required. It is measured by dividing the number of known defects by the size of the software entity. The other part of the application-level metric is requirements without test coverage. Once the required coverage is calculated, some experts may witness a few uncovered requirements. This metric allows the team to identify and eradicate the requirements not covered from all the requirements before sending them for production. This metric is essential as the team needs to know the covered requirements and which are left behind.

#Metrics 4:Mean Time to Failure: 

Mean time to failure is the average time gap between two failures. This metric is often used to determine the frequency of software failures by the DevOps team. Every team’s goal is to keep MTTF as low as possible. However, there are stances when this DevOps maturity metric may show high results. Such results indicate specific underlying issues with the development team or software quality. It may also indicate a lack of testing by the testers on the software before releasing the software update.

#Metrics 5: Mean Time to Detect:

Before fixing the issue, the team should be able to detect the same as quickly as possible. Mean time to detect is the average time consumed by the time to diagnose an issue with the software. An inexperienced or poorly skilled team may take longer than usual to diagnose an issue, whereas the MTTD should ideally be less inexperienced. Teams with poor MTTD lack monitoring on software and a significant amount of data that will help them detect the underlying issue.

#Metrics 6: Mean Time Between Failures:

The mean time between failures is the average time between two failures of a single component. Often engineers are confused between MTTF and MTBF. Even though they are quite similar as they both are about the average time between failures, MTTF is about the failure in deployment by the team, whereas MTBF is about failures in a single component. Many DevOps engineers use this DevOps quality metric to determine the stability of a particular component in a codebase. If MTBF shows less time, it signifies some issues with the component which require immediate attention. This metric identifies components with significant issues, allowing the DevOps team to fulfill their primary goal of having less failure rate.

#Metrics 7: Deployment Success Rate:

The deployment success rate is the measurement of the number of successful and failed deployments by the DevOps team. The team can determine their deployment success rate through this DevOps efficiency metric. An efficient team will have a high deployment success rate. A team with a low rate needs to have an automated and standardized deployment process, allowing them to increase their deployment success rate.   

#Metrics 8: Availability and Uptime:

Every organization aims to attain its software’s utmost quality and speed, but downtime is an inevitable factor for an application. Getting to know about the availability and uptime of the software is a necessary DevOps productivity metric that will allow the DevOps team to plan maintenance. Measuring the acceptable downtime of an application is available, which can be measured through read-only and read or write availability. 

The goal of every DevOps team is to minimize downtime and increase the uptime of the software. If the team cannot maintain the balance between these two factors, they need to plan the downtime for maintenance. By taking this action, they foresee what can be done during that downtime and the actions necessary to reduce the outage. 

DevOps Key Performance Indicators(KPI’s):

Key performance indicators are sure signs or factors that should be monitored to analyze DevOps’ performance. With that in mind, here are all the primary DevOps KPIs that every organization and DevOps team should be aware of. 

#KPI 1: Feature Prioritization-

Every software comes with numerous features that can fulfill the everyday tasks of specific users. However, effective software has certain primary features which define the software. The DevOps team put in all their efforts to create new code to add new features to the software. Sometimes, the newly added or existing features may decline in usage. Keeping an eye on every feature’s usage will help the DevOps team prioritize the features and ensure that they always remain bug-free. If the team notices a reduction in usage of a particular feature, it is time to reassess the priorities and focus on features in demand by the users. Doing so will allow the DevOps team to enhance engagement and make the program more beneficial for the users. 

#KPI 2:Customer Ticket Volume-

Issues and bugs in software are inevitable but can be avoided too much extent by rigorous testing by the team. Sometimes, a few bugs can bypass all the tests and may reach the end consumer. In that case, the consumer will be reporting such issues to the developer, increasing the customer ticket volume. Having many new tickets indicates an underlying issue with the program that should be fixed immediately. Developers can use this KPI to find and fix bugs that were not identified during the testing stage.

#KPI 3:Defect Escape Rate-

As stated before, every software will have certain defects during its lifetime. An effective testing team will detect the issues during the testing or development stage of the pipeline. Specific bugs may go through this testing and may reach the direct consumers. Defect escape rate is the measurement of all such issues that bypass the testing phase and reach the end-user. A high defect escape rate indicates loopholes or inefficiency in testing by the DevOps team. A high rate team should optimize the testing protocols and increase the testing capabilities as well. 

#KPI 4: Unplanned Work-

As the name suggests, this Azure DevOps KPI is about analyzing the time spent by the DevOps team on unplanned works. To measure this KPI, the team must calculate the work aligned in the pipeline at the commencement of the DevOps cycle and compare the same with the work necessary to finish the release. Moreover, analyze the unplanned work done during that time and the ongoing progress in the process. 

If the developers are spending more than necessary time on unplanned work, it showcases the lack of stability or issues in the DevOps approach. Apart from that, inefficient testing or incapable test and production environments can also be the reason behind unplanned work. Spending too much time on such work will reduce the team’s productivity and compromise the overall software quality.

#KPI 6: Process Cycle Time-

Process cycle time is the overall time consumed by the DevOps team from the conceptualization stage to the final step of attaining feedback from the users. Using this DevOps flow metric, the team can calculate their software development cycle time. In general, the longer process cycle time signifies a lack of efficiency within the team and vice versa. However, a short cycle time should be achieved by compromising the code quality. The time consumed in a single project by a DevOps team should be justified. 

#KPI 7: Application Performance-

An application should perform well before and after deployment so that the user can make the most out of it. Post-testing the application, the DevOps team should analyze the application’s overall performance before final deployment. While analyzing the performance, the DevOps team can identify any hidden errors or underlying bugs, allowing the program to become more stable and efficient with its features. DevOps metrics tools can also be used in examining the application’s performance. 


With all this information, now you have a better understanding of different DevOps CI/CD metrics and KPIs. Every DevOps team should utilize these key metrics and KPIs for the betterment of the team and the software so that they can enhance the software development life cycle. Without a doubt, there are dozens more DevOps KPIs and metrics, but calculating every factor is not an efficient way of working. Rather than doing everything, it is better to do what is best for the team and the organization. ThinkSys Inc will help your organization create the proper process for implementing DevOps KPIs and metrics. Our experts will understand your overall goals and your current and upcoming projects to provide you with an entirely customized roadmap for your DevOps. Furthermore, our team is proficient in using some of the industry-leading DevOps KPIs tools. 

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Frequently Asked Questions

A single DevOps metric cannot provide an accurate depiction of the performance. Several metrics should be used, and their collaborative result will be the right display of results. When it comes to measuring DevOps metrics, then multiple metrics, depending on the project and its requirements should be measured.

DevOps metrics provide a clear and unbiased overview of the DevOps software development pipeline’s performance, allowing the team to determine and eradicate issues. With these metrics, DevOps teams can identify their technical capabilities. Apart from that, these metrics help the teams assess their collaborative workflow, achieve a faster release cycle, and enhance the overall quality of the software.

DevOps KPI is a way to evaluate the performance of DevOps projects, practices, and products. Depending on the KPI, it provides in-depth information on the effectiveness of the DevOps team and project, along with the steps that should be taken to raise the quality standards.

Related Blogs:

  1. Software Development KPI’s and Metrics.
  2. Software Testing Metrics and KPI’s.
  3. Azure DevOps Pipeline Guide 2022.
  4. DevOps On Cloud.
  5. Multi-tenant Architecture For Cloud Apps.

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