If you have been following our blog then you might be aware that we aired our web seminar on the digital transformation benefits of using Intelligent Test Automation recently. The TechWell webinar sponsored by QMetry was presented by Senior Director at Infostretch, Siva Anna.
Now here’s a topic that has generated a lot of buzz and interest in the tech community: The use of AI-and-ML-backed automation to solve some of the agility and coverage challenges in the digital landscape. Not surprisingly, we witnessed a high turnout of quality and tech professionals as our audience for this webinar.
Digital transformation is the key driver for changes, new tools and techniques in the software development landscape. To realize the organizational goals for digital transformation, the main gatekeeper is the ability to deliver software faster without compromising the quality.
But speed, quality and efficiency are dependent on various parameters. Such as optimal coverage within the huge backlog of test cases, putting best practices into action for risk management, finding the right tools for automation and agility and optimizing the quality lifecycle at every stage.
Where many organizations are already using test automation and CI/CD to reduce time-to-market, the challenge lies in getting real-time actionable insights. The business need is to improve agility, user experience and quality, and reduce time to market. And here’s how efficient and smarter software testing can make it happen:
- Test Case Optimization
- Test Case Generation
- Defect Prediction
- Defect Prevention using Prescriptive Analysis
- Result Analysis
- Test Automation and Execution
- Infrastructure Optimization and Test Data Management
Role of Intelligent Test Automation
This is where Intelligent Test Automation comes in. Using the vast amounts of data already in your test automation suites, Intelligent Test Automation can help you focus on high-risk error categories. Such that you can set priorities defined by business needs, providing direction to focus on and execution results by platform.
Furthermore, it provides a reusable framework with data-driven testing and the adequate amount of test coverage and test depth for leaner management. With service layer and GUI layer automation you get access to end-to-end stability. This is cognitive QA at its best that leverages Machine Learning and Artificial Intelligence to continuously learn from your data and improve your automation cycle with prescriptive analytics.
At the conclusion, we received some very interesting questions from the attendees. What are some of the tools that you can use if you want to start with Intelligent Test Automation? Will my test automation team need AI-specialists to work with intelligent automation tools?
How can I integrate intelligent automation into my existing automation suite?