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Deep Learning & Software Engineering: State of Research and Future Directions (2009.08525v1)

Published 17 Sep 2020 in cs.SE, cs.AI, and cs.LG

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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Authors (9)
  1. Prem Devanbu (9 papers)
  2. Matthew Dwyer (8 papers)
  3. Sebastian Elbaum (12 papers)
  4. Michael Lowry (1 paper)
  5. Kevin Moran (66 papers)
  6. Denys Poshyvanyk (80 papers)
  7. Baishakhi Ray (88 papers)
  8. Rishabh Singh (58 papers)
  9. Xiangyu Zhang (328 papers)
Citations (20)

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