DevOps is perceived differently by people in an organization. Some think it’s a culture change, some think it’s just a fusion of Dev and Ops, while others say it’s all about automation and tools. With so many opinions clouding DevOps, the ultimate understanding of DevOps success has become incomprehensible.
Once you’ve deployed effective DevOps methodologies and tools, measuring success using these metrics will be an easy walk. Let’s dive into the details:
Key indicators of DevOps success
Velocity metrics apply to all major elements of a go-to-market drive.
Build Time: The time it takes to build the package. Time should include components related to automated and manual processes. This makes it possible to improve speed and reliability.
Deployment Time: The total time it takes to deploy the build package. This helps you understand if deployments are causing excessive delays or if you need bulk worksheets or manual processes.
Deployment frequency: The number of deployments for one application over a given period of time. It indicates how often code is deployed to SIT during the pre-production and production phases. Based on the data from this approach, you can decide how to increase the frequency of your deployments.
Environment Prep Time: The time it takes to prepare a new environment such as Dev and set up integrated testing. It helps you understand the effort required to develop an environment, the time it takes to create a stable environment, and the impact of the environment on the performance of a project.
Code Change Scope: The number of new user stories or code changes deployed in a single build. This helps evaluate the end-user value of the deployment.
Commit Frequency: The number of commits per assembly. This shows how often developers put code into the source base.
Merge Frequency: The number of mergers that occurred for a branch in a given period. This helps to ensure that developers are frequently consolidating their work and that development is on par with the latest set of code.
The success of DevOps isn’t just about focusing on production speed. You also need to pay attention to quality. Publishing changes every two weeks is pointless if the quality of the code is terrible. Instead, try moving your QA checks early in development and make sure the package checks all the boxes.
Quality metrics focus on all the key elements that affect code quality.
Test coverage: Automated test scripts execute a percentage of the codebase. Generally, the higher the test coverage, the lower the risk of refactoring.
Passed Test Percentage: The percentage of passed tests for each build. This includes the success rate for unit tests, functional tests, performance tests, and security tests. Orchestration tools like Jenkins or testing tools like Junit and Selenium will receive this data.
Defect Scope: The number of defects per story points delivered in a release. It is calculated per thousand lines of code or KLOC. If the defect density is low, the first thing to do is to check if the test and prod environments are configured differently, or if only the tested and uploaded package is deployed.
Defect Leakage: The percentage of defects leaked from the current testing phase to the next phase. This helps determine the effectiveness of a local developer’s unit testing.
Aging of a defect: The time during which a defect exists. This helps to understand how quickly and how the defect was fixed. Tracking tools such as JIRA, ServiceNow, or Remedy can collect defect and obsolescence data.
When speed and quality are combined with design, operational issues need to be addressed. In short, DevOps is about bridging the gap between Dev and Ops with shared responsibility and shared goals.
To embrace this culture of collaboration, we must assess whether our processes and automation are stable, reliable, and repeatable using the following metrics:
Indicators of stability
Deployment Success Rate: The percentage of successful deployments for each release.
Success Rate: The percentage of environments successfully created for each release.
MTTR (Mean Time to Recovery): The time is taken to recover from an incident due to a defect. This is also the time it takes to eliminate the defect and transfer it to production.
Deployment Downtime: The time during which the service is down during deployment. This will help you estimate the cost of deployment versus a loss of service.
Opening hours: the time during which the service is fully available. Downtime and uptime data can be collected from data centers or cloud platforms.
Once you’ve covered the three main components of delivery, namely speed, quality, and consistency, you can start rolling out the releases to production.
Release readiness metrics
Release Cycle Time: The time from the start of code development to a successful deployment to a production environment. This indicates the effectiveness of software development processes. It also helps identify automation needs and redundant processes.
Issue Size: The number of story points per issue.
DevOps adoption can be difficult. The results often fall short of expectations. To avoid issues and to do DevOps correctly, you need to focus on the DevOps metrics mentioned. Measuring these metrics will help you identify bottlenecks in advance and develop a kaizen culture.
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