# Deep Learning in Software Engineering

arXiv: https://arxiv.org/abs/1805.04825 · HTML: https://mlsanity.com/paper/1805.04825/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2018.

## Built on by (most cited first, 6 of 6)

- [CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2104.02443/) (2021)
- [Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities](https://mlsanity.com/paper/2212.10017/) (2022)
- [PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code](https://mlsanity.com/paper/2103.12778/) (2021)
- [Old but Gold: Reconsidering the value of feedforward learners for software analytics](https://mlsanity.com/paper/2101.06319/) (2021)
- [Investigating Reproducibility in Deep Learning-Based Software Fault Prediction](https://mlsanity.com/paper/2402.05645/) (2024)
- [Memory visualization tool for training neural network](https://mlsanity.com/paper/2110.13264/) (2021)

## Abstract (arXiv)

Recent years, deep learning is increasingly prevalent in the field of Software Engineering (SE). However, many open issues still remain to be investigated. How do researchers integrate deep learning into SE problems? Which SE phases are facilitated by deep learning? Do practitioners benefit from deep learning? The answers help practitioners and researchers develop practical deep learning models for SE tasks. To answer these questions, we conduct a bibliography analysis on 98 research papers in SE that use deep learning techniques. We find that 41 SE tasks in all SE phases have been facilitated by deep learning integrated solutions. In which, 84.7% papers only use standard deep learning models and their variants to solve SE problems. The practicability becomes a concern in utilizing deep learning techniques. How to improve the effectiveness, efficiency, understandability, and testability of deep learning based solutions may attract more SE researchers in the future.