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.
@article{arxiv.2009.08525,
title = {Deep Learning & Software Engineering: State of Research and Future Directions},
author = {Prem Devanbu and Matthew Dwyer and Sebastian Elbaum and Michael Lowry and Kevin Moran and Denys Poshyvanyk and Baishakhi Ray and Rishabh Singh and Xiangyu Zhang},
journal= {arXiv preprint arXiv:2009.08525},
year = {2020}
}
Comments
Community Report from the 2019 NSF Workshop on Deep Learning & Software Engineering, 37 pages