This site is designed to help you survey the landscape of AI software testing technologies and evaluate which ones can deliver the greatest value to your organization.
We’ve reached a tipping point that’s prompted CIOs to start actively exploring how 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.
With the rise of DevOps and Continuous Delivery, the business is now looking for real-time risk assessment throughout the various stages of the software delivery cycle. AI is undeniably valuable—and necessary—for transforming testing to meet these new expectations. Nevertheless, it’s important to realize that not every AI-driven software testing technology is the panacea it’s cracked up to be. While some are poised to deliver distinct business benefits in the clear and present future, others don’t seem ready to live up to the hype.
Beyond Continuous Testing with AI
Today’s “digital disruption” is forcing enterprises to innovate at lightning speed. While 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 today, but how do we test when these trends continue and the gap widens? We need “digital testing” to meet the quality needs of a future driven by IoT, robotics, and quantum computing. Artificial intelligence (AI), imitating intelligent human behavior for machine learning and predictive analytics, can help us get there.
Learn how AI can take software testing 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
• The role of smart testing technologies that aren’t technically “AI” (e.g., self-healing technologies)
Reality Check: The Best (and Worst) Uses of AI in Software Testing
Although Artificial Intelligence (AI) is nothing new, applying AI techniques to software testing started to become feasible just the past couple years. Inevitably, AI will soon become part of our day-to-day quality engineering process. But before we get caught up in the exuberance of the technology, let’s take a step back and assess how AI can help us achieve our quality objectives.
It’s been suggested that AI could be applied to actions such as prioritizing testing and automation, generating and optimizing test cases, enhancing UI testing, reducing tedious analysis tasks, and helping to determine pass/fail outcomes for complex and subjective tests. However, should AI be applied in these cases? And where else could it assist?
AI Software Testing Innovations at Tricentis
Here’s a quick overview of Tricentis’ recent innovations that leverage AI and machine learning to solve top software testing challenges.
Automated Test Design
Impose business-related rules on combinatorial methods to avoid repetitive, cost-effective, and manual clean-ups of your automatically generated test sets.
Risk Coverage Optimization
Find optimal test sets to maximize business risk coverage and defect detection under given time, resource, and budget constraints to optimize test execution.
Optimal Control Recognition
Elevate static image-based control recognition to dynamic pattern-based control recognition to make test automation more resilient to changes.
Test Failure Prioritization and Healing
Automatically identify and resolve test failures that don’t indicate a problem in the application under test.
Track flaky test cases, unused test cases, test cases not linked to requirements, untested requirements, etc. to indicate weak spots in test case portfolios.
How Intelligent Automation is Transforming Software Testing
“New IT—artificial intelligence, predictive analytics, intelligent automation, liquid delivery, design thinking, DevOps and agile, to name few—can transform the role of “testing” to “quality engineering.”
Explore how you can embrace the latest testing innovations to increase your business agility and gain a competitive advantage:
- Why software testing must evolve to meet changing business and customer expectations for digital transformation and New IT
- Strategies for extending testing beyond the boundaries of traditional manual processes
- How specific Accenture and Tricentis innovations can make testing more intelligent, continuous, automated and future-ready
General AI (or true AI, strong AI) can be understood as a computer system with the ability to apply intelligence to any problem. That’s the holy grail of AI research, the real new black. Although it’s been…
There are two main approaches to implementing AI: rule-based techniques and machine learning techniques
The decision whether to go for a rule-based system or learning system depends on the problem you want to solve…
AI is more an opportunity than a threat. Don’t see the difficulties; see the opportunities, accept the challenge, and take the chance. AI is not only an imperative for enterprises. It’s also a business imperative for you…
Stephen Hawking, Bill Gates, and Elon Musk have something in common, and it’s not wealth or intelligence . They are all terrified of the so-called AI apocalypse, a hypothetical scenario…
The question that plagues most of us testers in the era of AI is, “Do we need to change our current test approaches to test learning systems?” In short, the simple answer is, “Yes, but…