# A Systematic Literature Review on the Use of Deep Learning in Software Engineering Research

arXiv: https://arxiv.org/abs/2009.06520 · HTML: https://mlsanity.com/paper/2009.06520/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 30 of 35)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [Automating Code Review Activities by Large-Scale Pre-training](https://mlsanity.com/paper/2203.09095/) (2022)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Trustworthy and Synergistic Artificial Intelligence for Software Engineering: Vision and Roadmaps](https://mlsanity.com/paper/2309.04142/) (2023)
- [Benchmarking Causal Study to Interpret Large Language Models for Source Code](https://mlsanity.com/paper/2308.12415/) (2023)
- [Toward a Theory of Causation for Interpreting Neural Code Models](https://mlsanity.com/paper/2302.03788/) (2023)
- [Green AI: A Preliminary Empirical Study on Energy Consumption in DL Models Across Different Runtime Infrastructures](https://mlsanity.com/paper/2402.13640/) (2024)
- [Towards More Trustworthy and Interpretable LLMs for Code through Syntax-Grounded Explanations](https://mlsanity.com/paper/2407.08983/) (2024)
- [Evaluating and Explaining Large Language Models for Code Using Syntactic Structures](https://mlsanity.com/paper/2308.03873/) (2023)
- [Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey](https://mlsanity.com/paper/2310.17903/) (2023)
- [Automating Code-Related Tasks Through Transformers: The Impact of Pre-training](https://mlsanity.com/paper/2302.04048/) (2023)
- [Deep Configuration Performance Learning: A Systematic Survey and Taxonomy](https://mlsanity.com/paper/2403.03322/) (2024)
- [Machine Learning for Software Engineering: A Tertiary Study](https://mlsanity.com/paper/2211.09425/) (2022)
- [On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code](https://mlsanity.com/paper/2305.04106/) (2023)
- [Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting](https://mlsanity.com/paper/2401.10359/) (2024)
- [How Propense Are Large Language Models at Producing Code Smells? A Benchmarking Study](https://mlsanity.com/paper/2412.18989/) (2024)
- [Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora](https://mlsanity.com/paper/2205.13022/) (2022)
- [AI for DevSecOps: A Landscape and Future Opportunities](https://mlsanity.com/paper/2404.04839/) (2024)
- [Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models](https://mlsanity.com/paper/2502.16071/) (2025)
- [Meta-RAG on Large Codebases Using Code Summarization](https://mlsanity.com/paper/2508.02611/) (2025)
- [What are the Machine Learning best practices reported by practitioners on Stack Exchange?](https://mlsanity.com/paper/2301.10516/) (2023)
- [Smaller = Weaker? Benchmarking Robustness of Quantized LLMs in Code Generation](https://mlsanity.com/paper/2506.22776/) (2025)
- [End to End Software Engineering Research](https://mlsanity.com/paper/2112.11858/) (2021)
- [Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables](https://mlsanity.com/paper/2209.01566/) (2022)
- [Improving Retrieval-Augmented Deep Assertion Generation via Joint Training](https://mlsanity.com/paper/2502.10696/) (2025)
- [UniGenCoder: Merging Seq2Seq and Seq2Tree Paradigms for Unified Code Generation](https://mlsanity.com/paper/2502.12490/) (2025)
- [Energy-bounded Learning for Robust Models of Code](https://mlsanity.com/paper/2112.11226/) (2021)
- [An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code](https://mlsanity.com/paper/2307.02443/) (2023)
- [Towards Automatically Addressing Self-Admitted Technical Debt: How Far Are We?](https://mlsanity.com/paper/2308.08943/) (2023)
- [Investigating Reproducibility in Deep Learning-Based Software Fault Prediction](https://mlsanity.com/paper/2402.05645/) (2024)

## Abstract (arXiv)

An increasingly popular set of techniques adopted by software engineering (SE) researchers to automate development tasks are those rooted in the concept of Deep Learning (DL). The popularity of such techniques largely stems from their automated feature engineering capabilities, which aid in modeling software artifacts. However, due to the rapid pace at which DL techniques have been adopted, it is difficult to distill the current successes, failures, and opportunities of the current research landscape. In an effort to bring clarity to this crosscutting area of work, from its modern inception to the present, this paper presents a systematic literature review of research at the intersection of SE & DL. The review canvases work appearing in the most prominent SE and DL conferences and journals and spans 128 papers across 23 unique SE tasks. We center our analysis around the components of learning, a set of principles that govern the application of machine learning techniques (ML) to a given problem domain, discussing several aspects of the surveyed work at a granular level. The end result of our analysis is a research roadmap that both delineates the foundations of DL techniques applied to SE research, and highlights likely areas of fertile exploration for the future.