AI Coding Tools 25% Error Rate: Waterloo Study | AcademicJobs
Explore the University of Waterloo's study on AI coding tools failing 25% of basic tasks, implications for Canadian universities, academic integrity, and adapting CS curricula.
No reviews yet. Be the first to rate Daniel!
Daniel M. Berry is a Professor in the Cheriton School of Computer Science at the University of Waterloo. He holds a B.Sc. from Rensselaer Polytechnic Institute (1969) and a Ph.D. in Computer Science from Brown University (1974). Berry has served as a professor of computer science for over fifty years, with appointments at institutions including the Technion in Israel and the University of Waterloo. His research focuses on software engineering in general and requirements engineering in particular, with emphasis on human behavior issues that affect the adoption and effectiveness of requirements engineering in the software development lifecycle, as well as why requirements engineering and software engineering do not always function as intended. He has contributed to editorial boards of journals including the Requirements Engineering Journal, Empirical Software Engineering Journal, and Science of Computer Programming. Berry maintains resources on ambiguities in requirements specifications and legal contracts and has been involved with the International Requirements Engineering Conference series.
Berry participates in international organizations such as IFIP Working Group 2.9 on Requirements Engineering and has delivered lectures on topics including the inevitable pain of software development and lessons from house building for requirements engineering. His professional email address is publicly listed on the University of Waterloo website.
Explore the University of Waterloo's study on AI coding tools failing 25% of basic tasks, implications for Canadian universities, academic integrity, and adapting CS curricula.