The problem becomes the solution: Neural software analyses

Funded by an ERC Starting Grant, Prof. Michael Pradel from the Institute of Software Engineering at the University of Stuttgart is exploring how artificial intelligence can make software more reliable. [Picture: Pixabay] At least since the start of the Covid19 pandemic, software has been ubiquitous. Neural networks, deep learning, and artificial intelligence have been an important part of the boost to innovation in recent years - be it in the form of autonomous vehicles, speech recognition, or clever suggestions for our next vacation. The incredibly large amount of today's software systems as well as their complexity overtax not only the individual software developers, but also the development tools used to date. The result of this are software errors that may have costly, and sometimes also harmful, consequences. Against this background, Prof. Michael Pradel and his team the of the resarch group "Software Lab: Program Analysis" at the Institute of Software Engineering at the University of Stuttgart are exploring how artificial intelligence can independently learn software development tools from the increasingly complex programs. Neural software analysis is the core of his research, which has been funded by the European Research Council since 2019 with an ERC Starting Grant valued at EUR 1.5 million.