Tag: AI

Using artificial intelligence (AI) in testing to visually expand the accessor pool increases accuracy, productivity, and almost completely eliminates maintenance. The number one reason test cases get re-written is that an accessor has changed.  Using AI and image recognition provides more ways to recognize that accessor, which improves the stability and reliability of the test.

For the better part of 20 years, the e-commerce QA test industry has known that every one-second delay in response, they can lose up to half the page audience. Not because the user bought somewhere else, but because they became distracted. Today’s distractions are probably much higher than they were when those original studies were

Generative AI is a type of artificial intelligence (AI), one of many, where it is trained on a very large set of data. After training, if you give it some direction, it generates something for you. It can generate an answer, text, a picture, or it might generate code. It generates things based on your

Quality matters. It has an impact on the value and equity of your entire corporate brand. So it’s everyone’s job, from the C-level to QA and in between, to protect your corporate brand and its brand equity. For instance, if your brand is worth half a trillion dollars, a single QA mistake that is pushed

Don Rumsfeld, former U. S. Secretary of Defense, famously said in 2002 “There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t

The Current State of Open Source Test Automation Tools Open source automation tools such as Selenium have been the workhorses of QA automation for decades, especially for web apps. With the birth of open source in the 1990’s, there was an explosion of activity around the potential of open code to deliver high-value tooling that

The advances in AI and ML now make it possible to create expert systems that know both how applications are designed and how they behave. These systems can absorb the domain-specific instructions that enable them to replicate the behaviors of experienced QA testers with years of application-specific knowledge. With the capacity to deploy artificial intelligence

How Appvance Used AI to Successfully Deliver Level 4 Autonomy When Appvance began working on true testing autonomy we knew there were many directions one could take with machine learning to attack the problem. One direction would use neural nets to scan thousands of applications and build a database of how they work and are

We test software so users don’t experience bugs. It follows that all testing should be user centric. This requires intuition and an understanding of design intent when creating tests for new functionality, since users have yet to engage with the new features in a meaningful way. (More on that in a future post.)  Fortunately, the

CI/CD Testing Table Stakes taken Next Level Testing is often ignored when talking about agile, CI/CD and DevOps. And yet, testing is often a major bottleneck in these endeavors. To be successful in any of the above, test must be part of the culture, something done continuously at every build. Ignoring testing in CI/CD is