As organizations continue to adopt DevOps best practices, they’ve sought out ways to track and improve their software delivery efforts. This has led many DevOps teams to utilize a data-driven approach with metrics to measure performance and drive improvements. And these same metrics can also be useful for quality assurance and quality engineering teams.
In this post, we’ll take a deep dive into the four DORA metrics and why they matter for quality assurance and engineering teams. We’ll also discuss how these metrics can be used to drive improvements in manual and automated software testing.
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. Here’s a breakdown of these metrics.
Deployment frequency measures how often new code is successfully deployed to a production environment. This metric helps organizations create an overall benchmark for development velocity. If a company uses a lot of manual processes or has a slow error recovery time, they’ll likely have a low deployment frequency.
Lead time for changes is the time that it takes from an initial code commit to the code change being deployed. This is important for understanding whether there are any delays in development or throughout the continuous integration and continuous delivery (CI/CD) pipeline. For example, there could be a slow build process, time-consuming manual testing, and other inefficient processes that delay deployments.
Change failure rate is the percentage of code changes that break the system once deployed to production. This metric is the most obvious indicator of code quality, which helps organizations understand whether new releases are improving the user experience. Tracking the failure rate is important for balancing the need for quality with the previous two metrics that are focused on development speed.
Mean time to recovery is the average amount of time it takes for a system to become available again after an outage or failure. Although unplanned downtime is inevitable, a low mean time to recovery indicates that the organization is adequately prepared to restore the system in a timely manner.
The DORA metrics are important for quality assurance and engineering teams because they offer valuable insight into the entire software development life cycle, including manual or automated software testing processes. In many ways, putting a focus on the DORA metrics can help break down the barrier between development and testing teams, promoting collaboration, and leading to the delivery of high-quality software faster than before.
Now that we’ve covered why the DORA metrics are valuable to quality engineering teams, let’s take a closer look at how they can drive software testing improvements.
When many organizations adopt DevOps processes and begin tracking the DORA metrics, they quickly discover that the old approach to software testing with legacy solutions isn’t purpose built for today’s fast-paced development approach. They find that deployment frequency and the lead time for changes are being impacted by manual testing processes, or the change failure rate and mean time to recovery metrics reveal a lack in software quality.
A low-code test automation platform like mabl can help alleviate these delivery bottlenecks while also improving software quality. Mabl can be easily embedded in your CI/CD pipeline and as a native cloud solution it automatically scales to meet growing test coverage needs. Using mabl’s in-depth release test coverage reports, organizations can identify any gaps in their software testing strategy that could be impacting quality. And the low-code approach empowers anyone to create, run, and manage automated tests to improve quality without slowing development or straying from best practices.
The DORA metrics offer valuable insights for DevOps and quality engineering teams, but organizations still need to take action to improve their software quality and delivery speed. With mabl, organizations can lower the barrier to software testing and scale their quality engineering efforts to capture the full potential of DevOps.
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