The digital landscape is changing at an increasingly fast pace as customers across industries demand products and services of the highest quality with shorter delays. This has created a need for enterprises to prioritize a test-as-you-build model with enhanced customer experience as the key area of focus.
You can transform your traditional approach to Quality Assurance (QA) into the more efficient Quality Engineering (QE) model by considering six factors.
You need to evaluate your existing testing teams to reskill them to make a successful leap from QA to QE. This involves changing the team’s mindset, updating their skills and tools, and transforming the culture as a whole. By doing this, you will effectively move from testing as a people activity to testing as a service.
Testing itself will have to be flipped on its head. While the traditional way involves testing the product once it has been developed, the Agile way brings in testing right from the beginning of the software development lifecycle, at the ideation stage itself. This is done by incorporating feedback and continuous integration during the development lifecycle.
Having an automation framework in place is key to making the transition from QA to QE. It is already common practice to have automation on routine testing processes but there is scope for greatly expanding the role of automation in testing with tools like Selenium, QTP, TOSCA, QFTest, cognitive AI and Machine Learning.
A Continuous Integration (CI) and Continuous Deployment (CD) pipeline helps save a lot of time and effort spent on manual, error-prone deployment work. It works in tandem with the upstream testing lever to drive quality from the start of the software development lifecycle.
Crowdsourcing platforms like Topcoder allow you to crowdsource work to public, private and certified communities via the Hybrid Crowd extension. Crowdsourcing allows organizations to expand their spectrum of digital services, meet last-minute requirements and accelerate design, coding, testing and data science. Organizations using crowdsourcing benefit by getting access to the best ideas and pay only for output that fits their requirement.
Adopting an outcome-based model increases efficiency while reducing operational costs. Using measurable performance metrics, you can align your QE process with the outcome you desire and move away from managed capacity models which cost more. With outcome-based models, you will pay for outcomes instead of individuals, bringing in transparency and making it a win-win situation that increases your rewards and decreases the risks.
To sum up, you need to look at these factors while embarking on the shift from traditional testing to quality engineering. And depending on the maturity of your organization, the applicability of these will vary.