Cody Watson Assistant Professor of Computer Science
Ph.D., Computer Science at The College of William & Mary
MS, Computer Science at The College of William & Mary
B.A., Computer Science at Wofford College
B.S., Biology at Wofford College
Research interests include software engineering, deep learning, SE4DL, and security of DL models.
Deep learning models have become a mainstream method to better represent data. Previously, Professor Watson had been studying how to apply deep learning models to software engineering tasks that can aid in the software development process. His current research direction has transitioned into learning how to verify the results of deep learning's black-box solutions. This verification process can make use of traditional software engineering testing methods applied to deep learning-based software.
Professor Watson teaches a variety of courses at W&L including introductory courses, computer architecture and software engineering. Aside from these core courses, he also teaches several electives including modeling and simulation, introduction to machine learning and introduction to deep learning.
Cody Watson, Nathan Cooper, David Nader Palacio, Kevin Moran, Deny Poshyvanyk. A Systematic Literature Review on Deep Learning in Software Engineering. IEEE Transactions on Software Engineering (TOSEM '21) (Under Review).
Cody Watson, Michelle Tufano, Kevin Moran, Gabriele Bavota, Denys Poshyvanyk. On Learning Meaningful Assert Statements for Unit Test Cases. In Proceeding of the 42nd International Conference on Software Engineering (ICSE '20), pages 1389-1409.
Michele Tufano, Cody Watson Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk. Learning How to Mutate Source Code from Bug-Fixes. In Proceedings of the 41st International Conference on Software Engineering (ICSME '19), pages 301-312.
Michele Tufano, Cody Watson, Gabriele Bavota, Massimiliano Di Penta, Martin White, Denys Poshyvanyk. An Empirical Study of Learning Bug Fixing Patches in the Wild via Neural Machine Translation. ACM Transactions on Software Engineering and Methodology. (TOSEM '19), pages 832-837.