Dora metrics
DevOps Intelligence supports a broad array of performance metrics, enabling Development Managers, Developers and Testers to assess and their performance and fine tune processes to optimize that performance.
DORA is one such metric framework supported by DevOps Intelligence.
Access DORA metrics
Follow this path to access DORA metrics:
- Navigate to DevOps Intelligence → DevOps Performance Metrics
- Click the DORA Metrics tab.
Key metrics identified by Google DORA (DevOps Research and Assessment) for measuring the performance of software development teams include Deployment Frequency, Change Lead Time, Change Failure Rate and Mean Time to Recovery.
They provide a comprehensive overview of team performance and are vital for evaluating and enhancing your software development process.
DORA comprises four metrics:
- Deployment frequency metrics: The frequency with which an organization successfully releases to production.
- Change lead time metrics: The amount of time required to effect a release to customers.
- Change failure rate metrics: The percentage of deployments causing a failure in production.
- Mean time to recovery metrics: The amount of time required for an organization to recover from a failure in production.
Data for these metrics is retained for 180 days. Users can choose to view data for any time within the last 180 in the following graduations:
- 1 day
- 7 days
- 14 days
- 30 days
- 60 days
- 90 days
- 180 days (all retained data)
- Custom date range
Using the Select duration feature located on the right directly above the graphic displays, the user can select the data history range from a dropdown menu containing these selections.
Deployment frequency
Deployment frequency is a key DevOps performance metric that measures how often new code or software releases are successfully deployed to end users in a production environment. More frequent deployments can indicate a faster and more responsive development cycle.
Each successful deployment to production is counted as one deployment, even when the same release is deployed to multiple production environments.
Deployment frequency graph
- Displays the total and average number of deployments for the selected time period.
- Uses the selected application and history period to display deployment data.
- Requires the application to be mapped across all applicable technical services to ensure accurate data.
View deployment frequency details
Select the launch icon to open a detailed view. From this view, you can:
- View the deployment frequency graph and corresponding details table.
- Adjust the time frame for the displayed data.
- Search for technical services and applications by name.
- Sort the data by any column.
The details table displays:
- Technical service
- Application name
- Status
- Duration
- Deployment date
- Release
- Environment
The table displays data based on the time frame and application selected in the graph.
Change lead time
Change lead time is a key DevOps metric that measures the average time between when a code change is committed and when it is successfully deployed to production. It reflects the efficiency and responsiveness of the development pipeline, including code integration, testing, and deployment.
Change lead time graph
- Displays the total and average number of deployments for the selected history period.
- Provides a summary of change lead time data for the selected application and time frame.
View change lead time details
Select the launch icon to open the detailed view. From this view, you can:
- View the detailed graph and corresponding table.
- Adjust the time frame for the displayed data.
- Search for an application by name.
- Sort the Application column.
The details table displays:
- Application name
- First commit
- Deployment date
- Change lead time
- Epic details, including a link to the associated epic
The Change Lead Time table displays data based on the application and time frame selected in the graph.
Change failure rate
Change failure rate (CFR) is a DevOps metric that measures the percentage of deployed changes that fail in production and require an immediate fix. Failures can include service degradation, system outages, functionality issues, or other problems that affect the end-user experience or system stability.
Change failure rate is calculated as Deployments Failed / Total Number of Deployments.
Change failure rate graph
The interactive graph:
- Displays the total and average change failure rate for the selected period.
- Uses the selected application and time frame to display change failure rate data.
View change failure rate details
Select the launch icon to open the detailed view. From this view, you can:
- View the detailed graph and corresponding table.
- Adjust the time frame for the displayed data.
- Search for an application by name.
- Sort the Application column.
- Select available links to access related incident information.
The details table displays:
- Application name
- Change failure rate
- Incident details, including links to related information
Mean Time to Recovery (MTTR)
Mean time to recovery (MTTR), also known as Mean Time to Repair, quantifies how rapidly a team can restore service following a failure that impacts customers. It is the elapsed time between the CreationTime and ResolvedTime where Creation Time is the time at which an incident was created and Resolved Time is the time at which the incident was promoted to the Resolved state. MTTR is mathematically defined as
Resolved Time — Creation Time
The graph represents the total and average number of deployments for the selected history period. This graph allows you to filter data by priorities (P1 through P4), by default, All priorities is selected. MTTR trends help in evaluating the resilience of the system. A decreasing MTTR indicates that the team is improving its ability to recover from failures quickly, enhancing overall system reliability.
As more applications are added to DevOps Intelligence, users can track and compare the performance of different applications. Performance can also be measured against industry standards, categorized as High, Medium or Low according to Google’s standards.
For a more in-depth analysis, click on the launch icon. This action will open a detailed view of a graph and a corresponding table. Similar to the summary graph, you can adjust the time frame for the data in this detailed view.
The details table includes the Technical service, Application, Number of Incidents, Mean Time To Recovery and Incident details. For easy navigation, redirecting links are incorporated within the table.
Mean time to recovery table displays all data based on the time frame selected. All columns in this table can be sorted except the Incident column. Above this table, you will find a search box that allows searching by Technical services and Applications by name. In the case of ServiceNow (NOW), MTTR is calculated from the SNOW Start Time.
Benchmarking DORA metrics
DORA metrics enables the practice of benchmarking. Benchmarking is the practice of comparing your software development team's performance to industry standards or best practices using the previously identified four key metrics against four threshold standards. These standards are cataloged according to the following table:
DORA benchmark categories
Performance level | Deployment Frequency | Change Lead Time | Change Failure Rate | Mean Time to Recovery |
|---|---|---|---|---|
Elite | More than once each day | Less than one day | Up to 5% | Less than one hour |
High | Once each day to once each week | Between one day and one week | 5% – 10% | One hour to one day |
Medium | Once each week to once each month | Between one week and one month | 10% – 15% | One day to one week |
Low | Nore than once each month | More than one month | More than 15% | More than one week |
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