Tag: Test Strategy
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
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.
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 cost of underperformance in delivering quality software is steep. In addition to interrupted in-app experiences, bugs often contribute to high rates of customer churn and can lead to a damaged brand image. Users expect highly functioning apps with no bugs or issues, and apps that don’t provide these qualities are quickly deemed irrelevant and often fall forgotten. As such, software testing has become increasingly important in the rollout process. Since software testing can be a bottleneck in organizations striving for rapid release cycles, teams must have some objective measures to know when they have tested enough in order to
This post is the second in a 2-part series. As we discussed in my prior post, the Autonomous Software Testing Manifesto, AI provides you, the Automation Engineer, the power to test broadly and deeply across your application. This furnishes you with the opportunity to reevaluate your test strategy and determine what will be most effective in finding bugs. So now with AI in the picture, how we plan for and execute against the testing requirements changes significantly. Human Written tests (ML-backed) The first decision we need to make is which tests need to be written by human testers and which