Head of research projects in the area of quality assurance in software development. Practitioner, academic teacher, scientist, whose research interests revolve around issues related to the quality of code and ways of their elimination. Since 2012, as a leader of research team in one of the world’s leading automotive companies, he conducts research on the application of the artificial intelligence in quality assurance. Author and co-author of several books, publications and presentations regarding artificial intelligence, information security and quality assurance in computer science.
Topic: Machine learning-based prediction of a defective code in practice
The use of a machine learning makes it possible to indicate potentially defective areas of the computer code as soon as it emerges. This makes it possible to plan quality assurance activities already at the code writing stage. It is well known that the earlier quality assurance practices are applied, the better and cheaper, but what can be achieved with artificial intelligence and is it worth trying? Development team in one of the world’s leading automotive companies decided to check it. The results were surprising.