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

• URL: http://arxiv.org/abs/2009.08525v1
• Date: Thu, 17 Sep 2020 20:46:08 GMT
• Title: Deep Learning & Software Engineering: State of Research and Future Directions
• Authors: Prem Devanbu, Matthew Dwyer, Sebastian Elbaum, Michael Lowry, Kevin Moran, Denys Poshyvanyk, Baishakhi Ray, Rishabh Singh, and Xiangyu Zhang
• Abstract summary: 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. This report provides a general summary of the research areas representing the areas of highest priority which were discussed at the workshop.
• Score: 37.45171549466233
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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