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Software testing teams are increasingly expanding their reach across the enterprise by becoming enablers of DevOps. To do so, they’re embracing quality engineering, which integrates testing across development pipelines in order to create positive user experiences that help build customer loyalty. Like DevOps, quality engineering seeks to improve development processes so that companies can gain a competitive advantage in the marketplace. DevOps helps companies beat their competition to market, while quality engineering ensures they do so with a seamless customer experience. 

Embracing this new role as DevOps enabler requires a perspective shift around software testing. Whereas software testing success has been traditionally measured by the end result through metrics like test coverage and bugs in production, aligning quality with DevOps means identifying metrics that capture the impact of software testing on an organization’s ability to confidently build new products at DevOps speeds. 

Using DORA Metrics to Measure Software Testing Success 

The DevOps Research and Assessment (DORA) team at Google analyzed DevOps practices across many organizations and identified four key metrics for measuring software development and delivery performance. At high level, these metrics are:

  • Deployment Frequency: How often an organization successfully releases to production
  • Lead Time for Changes: The amount of time it takes a commit to get into production
  • Change Failure Rate: The percentage of deployments causing a failure in production
  • Mean Time to Restore Service: How long it takes an organization to recover from a failure in production

These metrics are informing how engineering and company leadership teams are measuring DevOps success. To help elevate the reach of quality engineering and best contribute to collaborative DevOps practices, quality teams should consider how they can connect their work to the DORA metrics. 

Measuring Quality Engineering Contributions to Pipeline Velocity

The first two DORA metrics, deployment frequency and lead time for changes, are focused on measuring pipeline velocity. More dynamic development pipelines unlock faster innovation, continuous improvement, and help companies gain an advantage over slower-moving competitors. By measuring how software testing and quality engineering impact development pipelines, rather than just focusing on the output, quality teams can better convey their impact on the development organization as well as the customer experience. 

To start understanding their impact on the DORA metrics measuring pipeline velocity, quality teams should understand what level of test coverage their organization needs to confidently deploy releases, including functional and non-functional aspects. 

Once they've identified the level of coverage needed, quality teams should focus on a testing strategy, including tools and platforms, that will allow them to embrace test automation for maximum efficiency. Identifying solutions that help software organizations execute entire smoke and regression test suites in minutes is key to improving deployment frequency and lead time for changes without negatively impacting the other two DORA metrics, which focus on pipeline stability.  

Read more about how quality engineering can improve pipeline velocity. 

Measuring Quality Engineering Contributions to Pipeline Stability

The second set of DORA metrics focus on pipeline stability, or how often development teams accidentally disrupt customers and how long it takes them to rectify the issue. Without understanding this side of DevOps adoption, development organizations risk alienating customers with defective releases that degrade the user experience. 

As the safety net for development pipelines, stronger software testing strategies have a proven impact on helping software teams deploy more confidently and reduce change failure rates.

If the change failure rate is too high, it’s likely that existing regression testing isn’t providing high enough test coverage. Quality teams should consider if their test coverage targets accurately reflect the user experience and cover non-functional aspects of quality like performance and accessibility. As deployment velocity accelerates, it’s essential to realize that effective test coverage is a moving target, and that development organizations need test automation solutions that help them continuously evaluate and increase test coverage in order to reduce change failure rates. 

If change failure rates are focused on helping teams reduce the amount of bugs in production, mean time to resolution (MTTR) is about being prepared for the (hopefully) rare times issues arise in production. Quality teams can have a profound impact on this DORA metric by investing in processes that make it easier to identify the root cause of defects and quickly communicate important information across development teams. Sharing test results directly in popular collaboration tools like Jira, Slack, or Microsoft Teams makes it easy to surface test results to the right people and teams, especially when those messages include comprehensive diagnostics data (DOM snapshots, network activity, performance logs, etc.) gathered from every step of each test.

See how Wurl reduced the time needed to run critical regression testing for faster development cycles. 

Overcoming DevOps Challenges with Quality Engineering 

Just 11% of software development teams consider themselves fully DevOps, meaning that many organizations are looking for high-impact ways to support DevOps maturity. When quality teams can connect their work to DevOps transformation, they reinforce the importance of collaborative software testing and set themselves up for a leadership role in DevOps transformation. The DORA metrics offer a proven and respected roadmap to measuring DevOps and quality engineering success for quality engineering, development, and leadership teams alike. 

With mabl, quality teams can expand their reach by improving their organization’s DORA metrics. Improving testing efficiency with low-code and AI not only improves pipeline velocity measurements like deployment frequency and lead time for changes, but also supports better pipeline stability by reducing change failure rates and MTTR. See how your quality team can unleash DevOps transformation with our 14 day free trial.