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.
Have you ever wondered if you want/need/able to test in production? Is it even possible without bringing harm to your users? Does it bring any value?
Automated testing and test automation far ago have ceased to remain options or choices for the development projects. Now as the DevOps and Agile approach getting huge traction across projects of all types and lengths, automation seems to have the key development methodologies.
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.
Testing involves two types, one will be functional and the other is the non-functional testing. These types of testing are different – continue reading to learn about both of them.
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.
Building automated testing is always a delicate balancing act, with the best course of action heavily reliant on the type of testing, scale of the business and a host of other factors to boot. However, the choice between OSS and proprietary tools to safely build automated testing is a whole question in itself – we take a look at the key pros and cons to help you make the right choice…
In this session, Jennifer Bonine and Rick Faulise will explore new shifts in testing paradigms. Demonstrate an AI-first testing method that integrates with your current manual and automation testing, and understand AI that aids your app teams. Re-think where you want to spend time and money in your testing team in a challenge that plagues most companies of too much to test and too little time.
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.