ACM SIGSOFT Webinar: Deep Learning & Software Engineering - A Retrospective and Paths Forward

Title: ACM SIGSOFT Webinar: Deep Learning & Software Engineering - A Retrospective and Paths Forward Kevin Moran Date: July 14, 2021 ABSTRACT Bridging the abstraction gap between concepts and source code is the essence of Software Engineering (SE). SE researchers regularly use machine learning to bridge this gap, but there are two fundamental issues with traditional applications of machine learning in SE research. Traditional applications are typically reliant on human intuition, and they are not capable of learning expressive yet efficient internal representations. Ultimately, SE research needs approaches that can automatically learn representations of massive, heterogeneous, datasets in situ, apply the learned features to a particular task, and possibly transfer knowledge from task to task. Improvements in both computational power and the amount of memory in modern computer architectures have enabled new approaches to canonical machine learning tasks. Specifically, these architect