# A Survey on Deep Learning for Software Engineering

arXiv: https://arxiv.org/abs/2011.14597 · HTML: https://mlsanity.com/paper/2011.14597/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 30 of 38)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [CodeNet: A Large-Scale AI for Code Dataset for Learning a Diversity of Coding Tasks](https://mlsanity.com/paper/2105.12655/) (2021)
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- [Stealthy Backdoor Attack for Code Models](https://mlsanity.com/paper/2301.02496/) (2023)
- [CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning](https://mlsanity.com/paper/2110.12925/) (2021)
- [CodeMark: Imperceptible Watermarking for Code Datasets against Neural Code Completion Models](https://mlsanity.com/paper/2308.14401/) (2023)
- [Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap](https://mlsanity.com/paper/2410.06107/) (2024)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Using LLMs in Software Requirements Specifications: An Empirical Evaluation](https://mlsanity.com/paper/2404.17842/) (2024)
- [Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps](https://mlsanity.com/paper/2309.04142/) (2023)
- [Towards an Understanding of Context Utilization in Code Intelligence](https://mlsanity.com/paper/2504.08734/) (2025)
- [Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities](https://mlsanity.com/paper/2212.10017/) (2022)
- [Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey](https://mlsanity.com/paper/2310.17903/) (2023)
- [Deep Configuration Performance Learning: A Systematic Survey and Taxonomy](https://mlsanity.com/paper/2403.03322/) (2024)
- [LibEER: A Comprehensive Benchmark and Algorithm Library for EEG-based Emotion Recognition](https://mlsanity.com/paper/2410.09767/) (2024)
- [Machine Learning for Software Engineering: A Tertiary Study](https://mlsanity.com/paper/2211.09425/) (2022)
- [CodeS: Towards Code Model Generalization Under Distribution Shift](https://mlsanity.com/paper/2206.05480/) (2022)
- [Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting](https://mlsanity.com/paper/2401.10359/) (2024)
- [Anatomizing Deep Learning Inference in Web Browsers](https://mlsanity.com/paper/2402.05981/) (2024)
- [Evaluation of large language models for assessing code maintainability](https://mlsanity.com/paper/2401.12714/) (2024)
- [A Survey of Source Code Representations for Machine Learning-Based Cybersecurity Tasks](https://mlsanity.com/paper/2403.10646/) (2024)
- [PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Code](https://mlsanity.com/paper/2103.12778/) (2021)
- [Fairness Concerns in App Reviews: A Study on AI-based Mobile Apps](https://mlsanity.com/paper/2401.08097/) (2024)
- [Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models](https://mlsanity.com/paper/2502.16071/) (2025)
- [A Systematic Literature Review on Explainability for Machine/Deep Learning-based Software Engineering Research](https://mlsanity.com/paper/2401.14617/) (2024)
- [Improving Retrieval-Augmented Deep Assertion Generation via Joint Training](https://mlsanity.com/paper/2502.10696/) (2025)
- [Can Hessian-Based Insights Support Fault Diagnosis in Attention-based Models?](https://mlsanity.com/paper/2506.07871/) (2025)
- [Feature-Guided Analysis of Neural Networks: A Replication Study](https://mlsanity.com/paper/2511.00052/) (2025)
- [Investigating Reproducibility in Deep Learning-Based Software Fault Prediction](https://mlsanity.com/paper/2402.05645/) (2024)

## Abstract (arXiv)

In 2006, Geoffrey Hinton proposed the concept of training ''Deep Neural Networks (DNNs)'' and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DNNs applied in SE. Few works to date focus on summarizing, classifying, and analyzing the application of deep learning techniques in SE. To fill this gap, we performed a survey to analyse the relevant studies published since 2006. We first provide an example to illustrate how deep learning techniques are used in SE. We then summarize and classify different deep learning techniques used in SE. We analyzed key optimization technologies used in these deep learning models, and finally describe a range of key research topics using DNNs in SE. Based on our findings, we present a set of current challenges remaining to be investigated and outline a proposed research road map highlighting key opportunities for future work.