Category: Blog

My first programming job after college was for a garment maker in Slough, in the United Kingdom. We were a small team, and everyone had to do everything. My programming by day tasks were complemented by being “on call” one night per week and one weekend day per month. Arriving at the data center in the middle of the night, the first words I said to the operations team were always the same, “What changed?” I had learned, just as Newton had predicted, that software continued in its “uniform state of motion” unless acted upon by some external force. That

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

Generative AI is a rapidly growing field with the potential to revolutionize software testing. By using AI to generate test cases, testers can automate much of the manual testing process, freeing up time to focus on more complex tasks. One of the leading providers of generative AI for software QA is Appvance. Appvance’s platform uses machine learning to analyze code and generate test cases that are tailored to the specific application being tested. This allows testers to quickly and easily create a comprehensive test suite that covers all aspects of the application. In addition to generating test cases, Appvance’s platform

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,

As business becomes increasingly digitized, it’s critical for teams to produce better quality, even as the complexity of applications to run your business on increases. And now do so in an hour or less. And in fact, you are going to have to be 800 times more productive (from a QA standpoint) if you want a world-class quality product moving from Agile to DevOps. You actually have to test 10 times more than you have been to cover everything your users are doing (substantially more than just your age-old “test coverage”). And achieve all of this without undue increased risk.

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