DORA/DevOps Metrics

In the previous article, I talked about the importance of metrics and touched upon different useful metrics that can help us track project quality, progress, and performance. In this post, I will focus more on DORA Metrics .

Let’s recap what is Metrics? Metrics provides us quantifiable data that can be provide useful information which can help one to make informed decisions, track progress, and ensure that software development projects are on track and the end product is of high quality meeting its objectives.

Through six years of research, the DevOps Research and Assessment (DORA) team has identified four or five key metrics that can help to assess and measure the effectiveness of DevOps practices within an organization.

As you know, DevOps, stands for Development and Operations, is a set of practices, principles, and cultural philosophies that aim to improve collaboration and communication between development (software development) and operations (IT operations) teams. The primary goal of DevOps is to enable organizations to deliver software more rapidly, reliably, and with higher quality. DORA metrics provide a set of key performance indicators that go beyond assessing DevOps practices, it also provides the insights indirectly on the the performance of a software development process and product quality.

1. Deployment Frequency

 Deployment frequency is one of the key performance indicators (KPIs) used in DevOps to measure how often new changes or features are deployed to production or other target environments

Some of its significance are:

• Allows organizations to deliver value to customers more quickly and respond to changing requirements or issues more effectively.
• It provides insights into an organization’s ability to release software updates rapidly and frequently.
• Frequent deployments often result in smaller batches of changes being released at once. This reduces the risk associated with large, complex deployments and makes pinpointing the source of issues easier.

For example, if an organization deploys changes to production 20 times in a month, the deployment frequency for that month would be calculated as:

Deployment Frequency = 20 deployments / 1 month = 20 deployments per month

2. Lead Time for Changes

• • It measures the time it takes for a code change, feature, or bug fix to go from the initial idea(from when the work item was created) until it goes into production.

  • The calculation for lead time for changes is straightforward:

    Lead Time for Changes = Timestamp of Deployment – Timestamp of Change Request or Idea.

  • To calculate this, you can take the average value of Lead Time changes of all the Features for the time period you are interested in.
  • Here are the key aspects and significance of lead time for changes:
  1. Customer Value: Short lead times enable organizations to deliver customer value more quickly. Reducing lead time means that features, enhancements, and bug fixes reach users faster, resulting in improved customer satisfaction and a competitive advantage.
  2. Early Feedback: A shorter lead time allows organizations to gather feedback from users and stakeholders sooner. This early feedback loop is crucial for making adjustments, improvements, and course corrections, leading to better product outcomes.
  3. Continuous Improvement: By tracking lead time for changes, organizations can identify bottlenecks, delays, and inefficiencies in their software delivery pipeline. This data-driven approach facilitates continuous improvement efforts and helps teams optimize their processes.
  4. Predictability: Understanding lead times helps teams and organizations become more predictable in their software delivery. Predictability is essential for release planning, resource allocation, and meeting customer expectations.
  5. Reduced Waste: Long lead times often result from delays, handoffs, and manual processes. Identifying and reducing these bottlenecks can lead to reduced waste, improved resource utilization, and cost savings.

• 3. Cycle Time for Changes:

  • It measures the total time it takes for a single change or task to move through the entire development process i.e. from the moment the work item is picked for development till it is closed.
• • a. Cycle time talks more about the development and testing efficiency while Lead time includes time it takes from the moment work item was created till it goes into production . Important is that it also includes waiting and queue time.
  • b. Identifying cycle time inefficiencies like  waiting time/ bottlenecks
    can lead to reduced waste in the software development process.
  • c. If cycle time is more, you may want to check if WIP (work in progress) count , i.e, the number of work items that a team does in parallel, is correct or not. More WIP can be one cause of higher cycle time.
   

  4. Change Failure Rate

• • The percentage of deployments causing a failure in production. In simple terms, this implies that the There are different ways you can implement the same in your project.
  • Change Failure Rate=(Total Number of releases / Number of Failed releases)×100

    In this formula:

  • Number of Failed releases: The total count of releases that did not meet the desired outcome or caused issues.

  • Total Number of releases: The overall number of releases during a specific period.

  The change failure rate is usually expressed as a percentage. A higher change failure rate may indicate issues with the change management process, suggesting that changes are not being adequately tested, documented, or communicated before implementation.

  5. Time to Restore Service

• • It talks about how long it takes an organization to recover from a failure in production
  • It measures the amount of time it takes to restore a service to normal operation after an incident or disruption has occurred. 
  • TTRS is a critical indicator of how quickly an organization can recover from service interruptions and minimize the impact on users and business operations.

    The formula for calculating Time to Restore Service is:

    TTRS=Time Service Resumed−Time Incident Reported

    In this formula:

  • Time Service Resumed: The point in time when the affected service is restored to normal operation.

  • Time Incident Reported: The point in time when the incident or service disruption was initially reported or detected.