STARCANADA 2019 - Big Data, Analytics, AI/Machine Learning for Testing

Software continues to revolutionize the world, impacting nearly every aspect of our work, family, and personal life. Artificial intelligence (AI) and machine learning (ML) are playing key roles in this revolution through improvements in search results, recommendations, forecasts, and other predictions. AI and ML technologies are being used in platforms for digital assistants, home entertainment, medical diagnosis, customer support, and autonomous vehicles. Testing practitioners are recognizing the potential for advances in AI and ML to be leveraged for automated testing—an area that still...

Data is the most valuable commodity in the world, and testers generate the most valuable data in the product development organization. When effectively tracked and presented, that data can inform release schedules, aid in decision-making, and shape the direction of the product. Connor Dodge will explain how testers can create and use real-time dashboards to analyze the status of agile projects and prove their quality to the organization. He'll discuss what metrics are important, how to pull the data to power those metrics from existing popular applications such as Jira and TestRail...

Uncertainty has always been a key challenge for testers. But testing a chatbot adds a completely new level of uncertainty. There are a lot of platforms and tools available for chatbot development, but what we lack is a standardized chatbot testing strategy. The way testing is performed on chatbots differs a lot from "traditional" testing (like for an app or web portal) due to the apparent randomness of a conversation with a chatbot. From testing numerous clients' chatbots and her company's own, named Ginger, Rajni Singh has experienced that it is impossible to anticipate all the...