Deep Learning & Software Engineering: State of Research and Future Directions
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Sep 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 3; 2022: 2; 2023: 4.
9 papers.
- 2022Automating Code Review Activities by Large-Scale Pre-trainingcited by 98
- 2021NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identificationcited by 42
- 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
- 2023Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Visioncited by 11
- 2021Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phasescited by 4
- 2022NeuDep: Neural Binary Memory Dependence Analysiscited by 3
- 2023What are the Machine Learning best practices reported by practitioners on Stack Exchange?cited by 3
- 2021Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Modelscited by 2
- 2023Good Tools are Half the Work: Tool Usage in Deep Learning Projectscited by 0
- 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit30 Dec 2023
- 2023Good Tools are Half the Work: Tool Usage in Deep Learning Projects29 Oct 2023
- 2023Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision13 Mar 2023
- 2023What are the Machine Learning best practices reported by practitioners on Stack Exchange?25 Jan 2023
- 2022NeuDep: Neural Binary Memory Dependence Analysis4 Oct 2022
- 2022Automating Code Review Activities by Large-Scale Pre-training17 Mar 2022
- 2021NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification25 Dec 2021
- 2021Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models5 Dec 2021
- 2021Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases3 May 2021