Today’s enterprises have clear visions around innovation, agility, profitability, higher-quality deliverables, and supercharging customer experiences. They understand that large-scale transformations and digital upscale adoptions require a strong, resilient, and optimized QA plan to ensure uncompromised standards. A robust QA plan is necessary to overcome traditional testing challenges and move to new-gen QA technologies that offer faster time-to-market solutions. These days, the need for traditional QA space to evolve into Quality Engineering (QE) is higher. AI and RPA are critical levers in QE to allow organizations to deliver highly agile QA solutions and enable faster deployments.

An enterprise must employ Shift Left and Shift Right methodologies as part of an overall quality plan. These strategies achieve maximum coverage and better-quality results. When designed with the complete lifecycle in mind, such solutions are far more important and necessary than the old method of QA enablement through a few levers.

Evolution of QA

The diagram below shows how QA has evolved through time. And how the recent focus on Quality Engineering is helping organizations to improve agility and ensure accurate improved predictability while keeping an enterprise’s cost low.

Break the tradition 

Traditional test automation can no longer keep up with the agile development process for QA teams. 

The following are some of the challenges that businesses face today, emphasizing the necessity for a transition.

1. Time to delivery 

Market dynamics force organizations to chase high-speed deliverables with a shorter turnaround time to assure business continuity. This pressure adds to the demand for QA and results in several quick releases with little time for testing.

2. Identifying the right scenario 

The traditional QA approach is ineffective since it requires a long time and effort to prepare a robust test plan. Hence, to improve QA coverage, most agile releases necessitate quick test planning based on previous sprint experiences and production data.

3. Predicting the next 

Due to the lack of scientific algorithms during traditional test planning, it is challenging to identify defective modules ahead of time, resulting in late detection of problems.

4. One-click E2E automation 

With complex technical architecture across multiple applications involving various tools and technologies, it’s difficult for QA teams to have a single platform to enable end-to-end test execution.

5. Unattended automation execution 

Traditional automation approaches have always had difficulties enabling unattended automation execution. These executions still require manual intervention in complex scenarios despite massive automation efforts.

The above challenges highlight the need for a comprehensive test solution across the entire test lifecycle through an intelligent automation framework that ensures a significant reduction in efforts and cost.

Next-gen QA with AI and automation

Accelerated planning and prediction with AI

AI in QA improves test coverage by using the “Predict the next” approach, optimizing existing test scenarios, and identifying use cases where testing is more necessary. These AI-led QA solutions consider all possible past production issues and predict the kind of scenarios that may be needed while keeping in view any additional critical module to be tested.

Agility with RPA

RPA complements AI-led QA by swiftly converting identified scenarios into quick run-test automation solutions. These powerful solutions are executed in real-time without any manual intervention using self-configured bots. RPA can help with sanity checks, standalone test validations, and complex end-to-end flows. RPA allows you to conduct all of these validations across multiple environments in a single click using self-configured automated bots.

The figure below depicts a typical view of how this approach works. The automation of activities across each test stage saves business time and money. Enterprises are witnessing considerable savings in capital expenditures (CapEx) and operating expenses (OpEx) due to this adoption.

Conclusion

The transformation of QA to QE as a practice yields highly beneficial results, and many enterprises have profited from QA teams’ new approaches. It is necessary to develop a strategy that goes beyond traditional QA and incorporates next-generation QA technologies such as AI and RPA as part of a QE solution right from the beginning. This approach leads to an intelligent test solution that boosts the efficiency of the QA team.