STARCANADA 2018 - AI and Data Analytics
Wednesday, October 17
What’s Our Job When the Machines Do Testing?
PreviewAfter its highly hyped introduction decades ago and followed by a long, quiet “winter,” artificial intelligence (AI) has slowly crept back into our consciousness. While our Siri and Alexa assistants entertain us, machine learning (ML) has brought new conveniences into our lives, with solutions such as Nest and Netflix. Today, AI brings us to the tantalizing brink of the autonomous vehicle. The sea change of this fourth Industrial Revolution has begun to disrupt industry after industry. The emerging capabilities of these fascinating machines demand our attention as AI starts to be...
How AI Is Transforming Software Testing
PreviewArtificial Intelligence (AI) and machine learning concepts are rapidly being integrated into IT systems. Companies like Apple, Tesla, Google, Amazon, and Facebook have started investing more in AI to solve different technological problems in the areas of health care, autonomous cars, search engines, predictive modeling, and much more. Applying AI is real, it’s coming fast, and it’s going to affect every business, no matter how big or small. So, how do we as testers adapt to this change and embrace AI? Where should we start? And once we get to the era of wanting to automate...
Use BDD and Product Analytics to Change Your Vision of Quality
PreviewDevOps teams struggle to ensure quality in multiple daily deployments. Traditional testing approaches have often failed in this context, but there are exciting new ways to test. Laurent Py and Vincent Prêtre will explain how, at Hiptest, DevOps teams combine behavior-driven development (BDD) techniques with product analytic analysis to continuously assert the quality of their product. BDD scenarios align teams to a common goal, and users provide feedback to ensure their needs are met. The team transforms usage scenarios into tests that enable developers to deliver the functionality...
Improve Testing of AI Systems with "Grey-Box" Testing Technique
There are two main challenges to testing systems that incorporate elements of artificial intelligence. First, the same input can trigger different responses as an AI system learns and adapts to new conditions, and second, it is difficult to understand what the correct response really should be. Such behavior violates one of the main principles of traditional testing: the repeatability of test case execution. It's like shooting a moving target and not knowing whether you missed. Testers lose confidence in the outcome of their testing when traditional approaches no longer apply. Yury...