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?
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.
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.
Are you tired of spending endless hours maintaining your automated test cases? Do you dread looking at test automation reports because of the tedious tweaks you know you’ll need to make from the latest updates from dev? Has your team lost its faith in test automation?
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.
In this Webinar, we will show you the fundamentals to calculating how effective your team is at finding bugs in your software. Using ServiceNow and Micro Focus ALM to demonstrate, we’ll show you how to automate the flow of information between your ALM and ITSM tools to monitor DDP in real time.
You can start painting without learning how to draw, but a good artist can tell — just by looking at a painting— if a work of art was done by someone who was an experienced artist, or a novice. Similarly, you can start automation without much training, but an engineer, well versed in automation, can quickly tell whether someone is practiced or not.
The value of machine learning is rooted in its ability to create models that guide future actions and to discover patterns missed by the naked eye. Machine learning methods are vastly superior in analyzing potential customer churn across data from multiple sources such as transactional, social media, and CRM sources.