Tag: AI

With the growth and evolution of software, the need for effective testing has grown exponentially. Testing today’s applications requires an immense number of complex tasks, as well as a comprehensive understanding of the application’s architecture and functionality. A successful test team must have strong organizational skills to coordinate their efforts and time to ensure that each step of the process is efficiently completed. To thoroughly test an application, teams must perform a variety of tasks to check the functionality of the software, such as scripting and coding tests, integrating systems, setting up and running test cases, tracking results and generating

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. It’s a transformational way for a test platform to recognize web elements that makes the traditional means of using accessors, or locators, arguably almost obsolete. What are accessors and how do they work? Accessors are the way that a test system can recognize an action,

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 done 20 years ago. Even on your computer, you can easily get distracted waiting for the screen to fully load, when you get an important email, and you forget about what you were going to buy. This is also true on mobile. In fact, it’s

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 query and what it’s learned previously. This is different than many other forms of AI which for example might look for patterns in large data sets. In order for generative AI to work well, it has to be trained on incredibly large sets of data,

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 out to the public or even internally could harm that brand by billions of dollars. Not necessarily hard dollars, but soft dollars, and those are dollars. QA people play an incredibly important role in ensuring application quality that, in turn, transfers to brand equity. This

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 know.” When it comes to testing software, we have the same set of scenarios. The known knowns We can estimate the quality of the parts of the application we have tested because we have data (test results), experience (we’ve tested this before), and instinct (we

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 would make software development teams more productive and cost-effective. But as we have learned, open source tools come with shortcomings that need to be understood with eyes wide open when assessing them for adoption into your DevOps. New models, technologies, and approaches to QA automation

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 built. And there may be value in doing this. But we did not find that it would add much to our knowledge. Domain experts know how actions work in modern libraries just as testers do. While this is interesting work, it seems more interesting to

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 situation is dramatically different when creating regression tests, i.e., the testing of existing functionality. By definition, users have used that functionality before, ideally at scale and often in surprising ways. After all, users are people and people are unpredictable.  Well, people are unpredictable in advance.

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 both unfortunate and unnecessary, as testing can be kicked-off at every build by most CI tools, including Jenkins, TeamCity, Travis CI, Go CD, Bamboo, GitLab CI, CircleCI and Codeship. Or course, this assumes your test automation system integrates with your CI tooling, as Appvance IQ