Businesses interact with customers across multiple touchpoints, making end user experience a vital denominator of success. Add to this a customer-driven proliferation of applications, and businesses have on their hands the huge task of continuously adding enhanced features. They also need to deliver a top-notch user experience every time. But traditional execution methodologies with infrequent releases and sporadic quality checks won’t work.
The need for quality on the go
You need a quality check process that supports continuous releases and fully usable products that are available anytime, scalable, and secure, at the end of each sprint. Continuous functional and non-functional quality assurance helps QA teams support accelerated releases and “quality at speed”, while keeping costs low.
What’s needed is automation-led Quality Engineering that can ensure digital transformation and product centricity
Why the QA to QE switch is worth it
Simply put, while QA assures the quality of the product, quality engineering (QE) drives the development of quality products and processes. Testing is done parallel to development for faster identification of bugs and quality releases. Here are some way that QE trumps traditional QA:
- Enables anytime testing for Anytime releases
- Shift left in testing and test automation
- Defect prevention rather than defect detection
- Enhanced automation for complete test coverage
- 100% availability of test data and test environment ensuring more consistent and predictable products
- Continuous end-to-end functional and non-functional assurance
Automation Run Centre for Assured Sustenance
A tester takes on the new role of a Software Developer in Testing, or SDET, and works closely with the development team by contributing towards requirements gathering, elicitation, monitoring and validating the production. The QA analyst’s role expands to include cross-skilled testing, multiple technology and cross-domain capabilities, storage gate analysis etc. Competency building becomes the primary focus for organizations as opposed to hiring expensive skilled resources from the market
Adoption of robust QA processes and governance such as well-defined roles and responsibilities, collaboration models, timely review mechanisms, and cause and effect analysis etc., to ensure best business outcomes. Metrics-based governance helps improve forecast, predictability, and business experience, while standardized and exhaustive metrics drive enterprise-wide outcomes, organizational maturity, and collaboration.
Technology evolution today is rapid with shrinking release cycles. This requires intelligent automation across heterogeneous platforms and a test life cycle powered by script-less and AI-based test automation tools. Organizations use AI to enable faster response to code changes, and support functionalities such as Test Case Prediction, Defect Prediction, Automated Test Data Generation, Regression Test Optimization, Smart Dashboards to name a few. Non-functional assurance like performance testing and security testing evolves into performance engineering and security engineering. Maximized use of intelligent technologies, especially machine learning, predicts performance issues and prevents future disruptions. Testing teams adopt continuous load testing simulations and predictive datasets for real-time decision-making, and build a a self-managing, self-learning, and self-adapting testing function
The constant technology evolution and growing customer demand is compelling organizations to focus more on business outcomes. While this shift on focus does not eliminate testing, it demands the transformation of QA from predominantly manual testing to automation-led QE. Organizations are therefore focusing on a robust transformation framework involving people, process, and technology transformation, powered by intelligent automation, AI, and machine learning.
Get in touch with us today for a result-driven QE transformation
Authored by Hena M P