The Predictive analysis carries to extract valuable information from the data sets with the help of the statistical algorithms and machine learning to forecast the trends and performance patterns. When it comes to software testing, predictive analytics makes everything clear about what to test and predict the quality issues before and after the production.
When testing software, it is not uncommon to hear that one hundred percent coverage is unachievable. What happens, then when we are asked to do such a thing. If the software that is being tested is used in critical infrastructure (power systems, water, medical, banking, etc.) then an escaped bug is not a trivial thing.
In this webinar, Lisa Crispin will explore these two sets of principles, how they relate to each other, how teams can benefit from them, and how they might shape the future of testing and quality.
Shopping for new tools is not unlike dating. You need to ask yourself the same questions: “What am I looking for?” “Do they fit in with the picture I have for my future?” “Will it get along with my friends colleagues?” On top of that, the testing tool landscape has changed so much in just the past few years. What expectations should you have? Where do you even go to “meet” these tools?
In this webinar, Jennifer Bonine will discuss the impact and scope of AI in testing. She will address some of the most burning questions out there on the subject around its impact to testers and give practical advice for when and how to add AI to your testing practices.
Come join this session, where I cover the basics of AI, existing problems with testing, discuss the key ways software testing can benefit from AI and the challenges involved in implementing AI-based solutions. Attending this session will help anyone to get started with AI-based testing.
Just when we, as testers, got a handle on what Agile means for us, the landscape changed yet again to a DevOps culture. Words like continuous integration (CI), continuous deployment (CD), and pipelines are now ones we’re hearing on a daily basis. As a tester, I’ll admit, I had no clue of what these words meant, and how was I to change the way I tested to fit within this DevOps culture.
At the turn of the 19th century, the industrial revolution replaced many manual jobs and that resulted in a better quality of life. At the same time, it also led to the loss of a large number of jobs in the short term. Since then there has been a recurrent fear that technological change will spawn mass unemployment. However, the Artificial Intelligence and Machine Learning revolution, that the world has come to terms with, will be significantly different from the Industrial Revolution.
Are you worried about your organization’s ability to cope up with the complexity of delivering at high velocity with excellent quality in multi-speed IT landscape and hybrid environments? Read some thoughts here about some Quality Engineering paradigms in DevOps world that we, Digital Assurance Services- Tech Mahindra, have implemented successfully with our customers.
In this talk, Liz introduces the Cynefin framework to help make sense of different types of situations and how to approach them: the obvious ones, the complicated ones which require expertise, the complex ones in which outcomes emerge, and the chaotic ones that we’re usually trying to avoid. Find out how these simple concepts can help us counter our innate human desire for predictability, enabling change and innovation; not just in software development, but in every aspect of our lives.
The ultimate goal of a DevOps approach is to deliver high-quality features to your customers at the pace they need. High performing DevOps shops point to continuous testing and test automation as key contributors to their success.
Since AI driven test generation was first introduced in 2017, much has been learned. Millions of test steps were generated and executed, finding thousands of bugs. We will dig into how the technology works, where it works and doesn’t.