Quality assurance =/= quality =/= software testing, but no matter how you define any of the above, testing and quality are closely intertwined. Testing, after all, is central to ensuring that software works as expected and meets customer requirements. But how quality teams think about testing is evolving as more software companies adopt DevOps and accelerate product velocity. The new reality of weekly (or even daily) releases and highly automated development pipelines requires a new mindset for quality engineers seeking to maximize the impact of software testing.
Unfortunately, quality assurance has often been perceived as a gatekeeper in software development. Testers were the final step in a linear development process that pushed code into production every few weeks, if not every few months. Testing was concentrated into a single stage, and the focus was primarily on making sure that each release filled precise customer requirements.
Today, the definition of quality has broadened, as has the window of opportunity for testing throughout the development pipeline. Making sure software “works” can now include everything from running comprehensive end-to-end tests to quickly checking API functionality. To fulfill this larger mandate, software testers are embracing a new mindset as DevOps enablers who practice quality engineering.
Quality engineering incorporates QA best practices and data-driven testing into the end-to-end customer experience, driving organizational growth. Rather than organizing itself around simply checking requirements, which was a common mindset for quality assurance, quality engineering focuses on empowering everyone in software development to participate in quality assurance.
To do so, the QA mindset shifts from “does this feature work” to “does this feature work for our users.” Quality engineers consider how different aspects of software quality impact the user experience, even for those who follow unconventional user journeys or have different access needs. This requires a data-driven mindset that adapts to actual customer use patterns, using customer data platforms like Segment to routinely update testing strategies as customer behavior changes. When testing is data-driven, it becomes more efficient, enabling quality engineers to integrate non-functional testing into their overall strategy to evaluate quality as every user sees it. Incorporating automated accessibility checks into routine testing processes also promotes an empathetic mindset to guide product quality, another critical evolution from the requirement-centered approach that previously dominated QA.
Quality engineers can make an impact on how software is built. The same investigative mindset that encourages data-driven testing empowers software testers to shift testing to the left, eventually enabling continuous testing in a culture of quality. When automated testing is embedded early and often within DevOps pipelines, development teams are significantly more likely to catch - and resolve - issues faster, making it easier to deliver new features with confidence.
As shown in the chart above, improving test coverage with a quality engineering mindset is strongly correlated with resolving issues faster. Nearly twice as many software development organizations can fix a bug in less than eight hours compared to teams with “good enough” test coverage. Faster discovery = faster fixes = faster innovation.
Adopting a new mindset is one thing, but actually expanding software testing with a quality engineering mindset is a much bigger challenge. To execute their vision, quality engineers need testing platforms that can help them embrace data-driven testing and perform non-functional tests like accessibility checks.
Mabl is the low-code, intelligent test automation solution designed for quality engineering. See how integrations with tools like Segment, Slack, and Jira make it easy to adopt a quality engineering mindset in our 14-day free trial.