Deep Learning & Software Engineering: State of Research and Future Directions

TL;DR

This paper summarizes a workshop on the intersection of Deep Learning and Software Engineering, highlighting high-priority research areas and proposing a roadmap for future exploration in this transformative field.

Contribution

It provides a comprehensive overview of key research directions and a strategic roadmap for advancing DL and SE integration based on expert discussions.

Findings

Identified high-priority research areas in DL and SE

Outlined future directions for cross-cutting research

Serves as a roadmap for future work in DL & SE

Abstract

Given the current transformative potential of research that sits at the intersection of Deep Learning (DL) and Software Engineering (SE), an NSF-sponsored community workshop was conducted in co-location with the 34th IEEE/ACM International Conference on Automated Software Engineering (ASE'19) in San Diego, California. The goal of this workshop was to outline high priority areas for cross-cutting research. While a multitude of exciting directions for future work were identified, this report provides a general summary of the research areas representing the areas of highest priority which were discussed at the workshop. The intent of this report is to serve as a potential roadmap to guide future work that sits at the intersection of SE & DL.