Leveraging AI in QA

3 min readNov 25, 2020


The current fast-evolving markets call for delivery of high-quality products to the market at great speed, to have a competitive advantage Organizations in the technology verticals need to be really fast and accurate both at the same time.

This may give rise to various complexities, therefore, increases the proneness to error.
Quality Assurance must evolve to help ensure customer experience and also meet the constant demands of speed. In a time of increased application complexity, QA helps create a bottleneck to success as most agile testing is geared towards manual testing and intensive creation of automated test scripts.

Quality Assurance Automation for Better Business

Though Quality Assurance automation has existed for many years, the benefits were not impactful enough for businesses to notice.

In the first-gen of automation, the focus was largely cantered on regression and UI-based. The objective was to build a framework that would accelerate automation using commercial tools.
Software automation evolved to include keyword-driven, data-driven and later, business process-driven frameworks that brought significant savings to clients. But the savings were limited largely to regression and did not make much difference to the business.

The next evolution of automation included the function side of the business in the form of API, test data automation and more, this helped a lot with test executions. The focus now has moved from UI-based automation to multi-tier /multi-stack automation that made impact o time-to-market and efficiency.

This further evolved with an increased focus on continuous testing. Behaviour Driven Design and Test-Driven Design are forcing automation solutions to join the mainstream and not limited to testers alone. Businesses at the User Acceptance Stage and Developers at Unit Test stage are also using automated scripts to test functionalities, save time and enhance the experience. Testing has expanded to white box testing resulting in better Quality Control (QC) of the code.

Wide adoption of open source automation solutions, continuous and agile testing, TPS integrations and solutions around digital and mobile testing has helped the evolution of automation in the execution phase.

AI and Quality Assurance

Artificial Intelligence led cognitive solutions to bring out the superior results by combining the best of automation with AI.

It is a 3-dimensional focus
· To eliminate test coverage overlaps
· Optimize efforts with more predictable testing
· Lastly to move from defect detection to defect prevention

Pattern analysis and processing volumes of data have become simpler with better machine learning algorithms. AI can work round the clock, thus tests can be executed as often as required. All of this can be done in real-time, efficiently with higher chances of correctness.

QA specialists can then use these results to prioritize further investigations and corrections.
They will be in a better position to analyze results and communicate results faster.

RPA & Robotics solutions are being used for various automation needs that go beyond traditional testing activities. Tech companies are building robots as testers to execute testing on physical devices too, the use of AI bots produces many positive results, including increased profitability, increased customer satisfaction, quicker time to market, early detection of high-risk areas for regression test, to a decrease in the overall cost.