AI in Software Testing
We’ve reached a tipping point that’s prompted organizations to start actively exploring how artificial intelligent (AI) can help them achieve their digital transformation goals. AI requires data + computing power + algorithms. We’ve had the algorithms for a long time. Now, big data and colossal computing power have made AI such a distinct reality that CIOs rank it as their top strategic investment.
We’re here to help you survey the landscape of AI software testing technologies and understand how it can deliver the greatest value to your organization.
Here’s a quick overview of Tricentis’ recent innovations that leverage AI and machine learning to solve top software testing challenges.
Testing AI Systems: Not as Different as You’d Think
AI-based tools have transformed from a vague, futuristic vision into actual products that are used to make real-life decisions. Still, for most people, the inner workings of deep-learning systems remain a mystery. If you don’t know what exactly is going on while the input data is fed through layer after layer of a neural network, how are you supposed to test the validity of the output? It’s not magic; it’s just testing.
Introducing NEO, AI-Driven Test Automation
Over the past 2 years, Tricentis has been investing in a new approach to Artificial Intelligence for software testing. In this session, we will be unveiling NEO, the Neural Optical engine. Join this session to understand how NEO enables coders, businesspeople and testers to all reach stable automation through the next generation of test automation technology. We will be discussing how NEO works, how it achieves self-healing, how we work across technologies, and how anyone—from coders to attorneys—can all use it in the way they feel most comfortable.
Reduce Testing Costs With AI and Automation
In today’s environment, many companies are under pressure to automate more and spend less. Join this webinar to learn how an intelligent approach to software testing that leverages AI-powered impact analysis and test automation can help you significantly reduce software testing effort, timelines and costs. Chris Trueman, SVP Product at Tricentis, shares how Tricentis customers are focusing their testing efforts on the highest-risk areas and rapidly scaling test automation to cut testing costs in half and reduce timelines by as much as 90%.
You will learn:
- Strategies you can implement now to lower your testing costs
- How impact analysis and a risk-based approach can focus resources on what needs testing
- How you can quickly scale test automation even if your team has limited or no coding skills
The Great Debate: The Role of AI in Software Testing
AI in software testing is not a panacea that magically eliminates all your testing problems—but there are many ways that it can deliver business value today by helping you test smarter and more efficiently. Watch a lively panel discussion in which Wolfgang Platz (Tricentis Founder and Chief Strategy Officer), Tom Murphy (Senior Director Analyst), Jeff Wilkinson (Managing Director of Accenture), and other software industry leaders debate these and other AI-focused topics that are currently polarizing the software testing industry.
Beyond Continuous Testing with AI
Digital Transformation is forcing enterprises to innovate at lightning speed. While DevOps delivery cycle time is decreasing, the technical complexity required to deliver a positive user experience and maintain a competitive edge is increasing—as is the rate at which we need to introduce compelling innovations.
We’ve turned to Continuous Testing to bridge the gap in software test automation today, but how do we test when these trends continue and the gap widens tomorrow? We need “digital testing” to meet the quality needs of a future driven by IoT, robotics, and quantum computing. Artificial intelligence (AI) in software testing, imitating intelligent human behavior for machine learning and predictive analytics, can help us get there.
Learn how AI in software testing can take it to the next level, including:
- Why AI is now more feasible—and critical—than ever
- What AI really is and how it’s best applied
- How AI can help us test smarter, not harder