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

arXiv: https://arxiv.org/abs/2009.08525 · HTML: https://mlsanity.com/paper/2009.08525/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 9 of 9)

- [Automating Code Review Activities by Large-Scale Pre-training](https://mlsanity.com/paper/2203.09095/) (2022)
- [NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification](https://mlsanity.com/paper/2112.13214/) (2021)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Challenges and Practices of Deep Learning Model Reengineering: A Case Study on Computer Vision](https://mlsanity.com/paper/2303.07476/) (2023)
- [Quality Assurance Challenges for Machine Learning Software Applications During Software Development Life Cycle Phases](https://mlsanity.com/paper/2105.01195/) (2021)
- [NeuDep: Neural Binary Memory Dependence Analysis](https://mlsanity.com/paper/2210.02853/) (2022)
- [What are the Machine Learning best practices reported by practitioners on Stack Exchange?](https://mlsanity.com/paper/2301.10516/) (2023)
- [Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models](https://mlsanity.com/paper/2112.02542/) (2021)
- [Good Tools are Half the Work: Tool Usage in Deep Learning Projects](https://mlsanity.com/paper/2310.19124/) (2023)

## 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.