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11/23/22, 6:36 PM 15 proven DevOps metrics and measurements to measure DevOps success
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15 proven DevOps metrics and measurements to
measure DevOps success
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How to measure DevOps success with DevOps metrics and measurements?
With businesses under more pressure than ever to deliver high-quality software faster, many are turning
to DevOps methodologies to achieve these goals.
But what does success look like in a DevOps organization?
How can you tell if your company is genuinely deriving benefits from its DevOps transformation?
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11/23/22, 6:36 PM 15 proven DevOps metrics and measurements to measure DevOps success
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No matter what, a DevOps approach can help you speed up product delivery, improve agility, and
increase customer satisfaction.
But have you ever wondered why measuring DevOps success matters?
It’s no brainer that tracking progress and understanding what’s working (and what isn’t) is essential to
any organization, but this is especially true for the teams dealing with software development.
As DevOps metrics and measurements don’t only provide you the numbers, they also help you get deep
insights into the performance of your team and processes.
Several metrics can be used to measure the success of a DevOps initiative, but we need to keep in
mind that not all of them are created equal.
These metrics offer insights into the overall success of the DevOps transformation and help you identify
various areas that need improvement.
Also, a company that offers DevOps services can help your business with all the resources and
expertise necessary for a successful transformation.
To help you get started, we’ve compiled a list of various DevOps metrics and measurements for
assessing software success.
15 proven DevOps metrics and measurements to measure DevOps success [hide]
1 15 essential metrics to measure DevOps success:
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1.1 1. Mean Time to Detection (MTTD)
1.2 2. Change in Mean Time to Resolution (MTTR)
1.3 3. Deployment Frequency
1.4 4. Restoration of services
1.5 5. Automated test pass percentage
1.6 6. Application traffic and usage
1.7 7. Number of customer tickets
1.8 8. Defect escape rate
1.9 9. Application performance
1.10 10. Commit count
1.11 11. Employee satisfaction
1.12 12. Code coverage
1.13 13. Service level indicator
1.14 14. Repository speed
1.15 15. Error rates
2 Final thoughts
15 essential metrics to measure DevOps success:
As businesses grow and change, so do their IT needs.
But what never changes for businesses and customers is the need to deliver software quickly and
efficiently.
DevOps is a set of practices that combine software development (Dev) and information technology
operations (Ops) to shorten the time it takes to deliver customer updates and features.
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11/23/22, 6:36 PM 15 proven DevOps metrics and measurements to measure DevOps success
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So, let’s look at 15 essential metrics for measuring DevOps success.
1. Mean Time to Detection (MTTD)
One of the most critical metrics for DevOps success is the Mean Time to Detection (MTTD).
This is the average time it takes to identify a problem or any new issue in the software.
Undoubtedly, as soon as the problem is identified, the sooner it can be fixed. And, of course, it will lead
to a better end-user experience.
In order to reduce MTTD, DevOps teams need to have visibility into all aspects of the software lifecycle
(SDLC).
Businesses need to identify issues early on and try to fix them rapidly.
Achieving this requires a shift in mindset from traditional waterfall development methods to agile
methodology.
It also requires a focus on automation and collaboration.
When everyone on the team is aware of potential issues and has compatible tools and processes in
place to fix them quickly, MTTD will naturally decrease.
As a result, this will lead to better DevOps success.
2. Change in Mean Time to Resolution (MTTR)
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DevOps services help improve the mean time to resolution (MTTR).
This is the time taken to fix a problem once it’s detected.
The shorter the MTTR, the less downtime will be for the businesses, and the quicker they can resume
their everyday business operations.
DevOps can help improve the MTTR by automating tasks, such as deployments and monitoring.
This means that problems can be identified and resolved more quickly, with less need for manual
intervention.
MTTR can be calculated by keeping track of the number of changes made over a period of time.
This can be done by monitoring the commits made to the source code repository.
By tracking the MTTR, businesses can see how quickly their DevOps team is able to fix issues.
This will help them identify any bottlenecks in the process and make necessary changes to improve
efficiency.
3. Deployment Frequency
The deployment frequency is the count of code deployment to production in a given time.
This helps businesses know how often new code is being deployed into production and can be a good
indicator of how quickly changes are being made.
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By measuring deployment frequency, organizations can get a better understanding of their DevOps
pipeline and find out particular areas where they need to continue to work on improvements.
In order to measure deployment frequency, businesses need to track the number of deployments made
over a period of time.
This can be done manually or using tools such as Jenkins or Azure DevOps.
4. Restoration of services
Undoubtedly, technology is not perfect, and things may go wrong even with the best-laid-out plans for
the future.
So, one of the main goals of DevOps is to quickly and efficiently restore services whenever there’s an
outage or incident.
To measure the success of this, organizations can track the time it takes to restore services.
The shorter the time, the more successful the DevOps initiative.
5. Automated test pass percentage
In order to increase the velocity of deployments, it’s important to have a high degree of confidence that
the code changes will not break the existing functionality.
This is where automated testing comes in.
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By running tests automatically, organizations can get feedback on the code changes more quickly and
avoid potential problems.
The pass percentage of automated tests is an excellent metric to measure the success of this process.
It can be calculated by dividing the number of tests that pass by the total number of tests run.
A higher percentage indicates that the code changes are more likely to be successful when deployed to
production.
6. Application traffic and usage
After the deployment, it’s crucial to monitor the application traffic and usage.
If you suddenly see a huge spike or drop in the traffic, it might indicate that something is wrong with the
new code.
By monitoring the application traffic and usage, you can quickly identify any potential problems and take
preventive measures against them.
Moreover, the last thing you would ever want to see is no traffic at all, which would mean that your
application is not being used at all.
7. Number of customer tickets
Customer happiness is one of the driving forces behind a successful business.
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A flawless user experience is what keeps the customers coming back for more.
One of the best ways to measure customer happiness is through customer support tickets.
If you see a sudden increase in the number of customer tickets, it might indicate that there’s a problem
with the application.
By tracking this metric, you can quickly identify any potential issues and take steps to resolve them.
Moreover, a decrease in the number of customer tickets might also indicate that the customers are
happy with the application and are not facing any issues.
8. Defect escape rate
No matter how forward-thinking and well-planned an organization is, there will always be some defects
that make it to production.
The goal is to minimize the number of defects and to fix the ones that go live quickly.
The measurements include development process problems, testing process problems, and production
defects.
Moreover, reports by end users can also harm the reputation of the company.
So, to avoid any such thing, it is essential to track the defect escape rate.
9. Application performance
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Before you even do a deployment, you need to ensure that the application is performing well.
This measures the performance of the application under different loads and stress levels.
By monitoring the application performance, you can identify any potential bottlenecks and take steps to
resolve it.
Moreover, a good performance metric will help you determine the capacity of the application and how it
will scale in the future.
10. Commit count
Whenever there’s a change made to the codebase, it needs to be committed.
This measures the number of commits made to the codebase.
A higher commit count indicates that the codebase is being actively developed and that there are many
changes that are being made.
By tracking this metric, you can gauge the activity level of the development team and see how quickly
they are making changes.
11. Employee satisfaction
Needless to say, happy employees lead to a more productive workplace.
One of the primary ways to measure employee satisfaction is through regular surveys.
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As employee satisfaction is affected by various factors, like compensation, workload, and company
culture.
So, it’s much more important to ask different questions to get a comprehensive view.
These questions may include the following:
How satisfied are your employees with their current job?
Do they feel a good work-life balance?
Are your employees compensated relatively?
Is your employee’s workload manageable?
Do your employees have the right tools and resources to do their job?
Do you feel like your employees are able to provide feedback openly?
12. Code coverage
Code coverage is a measure of the quality of tests.
It helps understand how much of the code is executed when the tests are run.
A high code coverage means that most of the code is covered by tests, and a low code coverage means
that only a small part of the code is covered by tests.
A good practice is to have a minimum code coverage of 70%.
Code coverage can help measure DevOps success in several ways like
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it can help identify areas of the code that need more testing
it can help ensure that new features are adequately covered by tests
it can help assess the effectiveness of automated testing scripts
it can provide an objective DevOps metric and measurements for comparing the quality of different
DevOps teams’ testing practices
By understanding how code coverage works and how it can be used to measure DevOps success,
organizations can help improve their testing practices and increase their chances of delivering quality
software products.
13. Service level indicator
Businesses signing (SLAs) service level agreements need to pay careful attention to the service levels
they agree to.
An SLA is a contract between two parties that outlines the level of service that one party will provide to
the other.
The aim of an SLA is to ensure that both parties understand the level of service that is being provided
and that there are clear expectations around what will happen if the service is not delivered as agreed.
One of the most critical aspects of an SLA is the service level indicator (SLI).
An SLI allows a business to know actual application performance or uptime against the desired or
contracted levels.
There are various factors that can impact an SLI, such as:
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application complexity
number of users
amount of data being processed
and the type of workload
14. Repository speed
The pull request is one of the most commonly used DevOps metrics and measurements.
It can be considered as one of the ways for developers to share their code with others and receive
feedback before merging it into the main codebase.
The repository speed score is based on the time it takes for a pull request to be merged in Github.
A high repository speed score indicates that the codebase is being actively developed and that there are
many changes that are being merged quickly.
However, a low repository speed score may indicate that the development team is not working together
as effectively or that the codebase is not being actively developed.
15. Error rates
With businesses trying to move at a faster pace, it’s important to track and monitor errors.
Tracking error rates can help identify SDLC bottlenecks, optimize processes and enforce quality
standards.
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There are various types of errors that can occur during the software development process:
syntax errors
compilation errors
run-time errors
logic errors
and functional errors
Monitoring error rates can help prevent errors from happening in the first place by identifying areas of
the code that are prone to errors.
Final thoughts
Now that you know about some of the critical DevOps metrics and measurements, it’s important to
remember that no single metric can give you a complete picture of the success of your DevOps
initiative. Instead, it’s important to track various metrics and use them to identify areas that can be
improved.
Moreover, it’s essential to have a way to track these metrics over time so that you can see how your
DevOps initiative is progressing.
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