From Reactive to Predictive: Using Generative AI to Anticipate Quality Risks
Most quality engineering focuses on finding existing bugs, but what if we could predict where bugs will appear before writing code? This talk introduces a framework for using generative AI to analyze code changes, requirements, and historical bug patterns to forecast potential quality issues. Discover how to build a predictive quality model that helps teams prioritize testing efforts and implement preventative measures before problems emerge.
Leonardo Pace
With over 8 years of experience at Globant, Leonardo Pace leads the AI testing practice, focusing his efforts on ensuring the reliability and accuracy of AI-driven solutions. His team develops cutting-edge quality assurance strategies, collaborating with data science and software engineering specialists to integrate practices and methodologies that set new standards in the field.
His technical expertise encompasses machine learning frameworks and computer vision techniques, enabling him to drive the adoption of advanced technologies in software testing. At MagnifAI, as the Chief Technology Officer, he focuses on incorporating artificial intelligence and quantum computing techniques to revolutionize software development and testing, always staying at the forefront of technological innovation.