Abstract: When embracing new technology like LLMs, are we “throwing the baby out with the bath water”? What are we forgetting, from past research, that it is still relevant and useful? For example, I firmly believe that deep learning and generative AI methods such as ``chain of thought’ will dramatically change the nature of science (in general) and SE (in particular). But moving forward away from generative tasks to classification, regression, and optimization tasks, my experimental results strongly suggest that other non-neural methods can be just as effective, particularly when combined with hyperparameter optimization. This is an important point since the non-neural methods can yield the succinct symbolic models that humans need to review and understand a model.

Timothy Menzies (IEEE Fellow, Ph.D., UNSW, 1995) is a full Professor in CS at North Carolina State University where he teaches software engineering, automated software engineering, and foundations of software science. He is the directory of the RAISE lab (real world AI for SE) and the author of over 280 referred publications. In his career, he has been a lead researcher on projects for NSF, NIJ, DoD, NASA, USDA (funding totalling over 13 million dollars) as well as joint research work with private companies. Prof. Menzies is the editor-in-chief of the Automated Software Engineering journal and associate editor of IEEE Transactions on Software Engineering (and other leading SE journals).

Full prof, ex-nurse,rocketman,taxi-driver,journalist (it all made sense at the time).