Data to 7 Oct 2026

Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges

arXiv 2002.05442 ↗First posted 13 Feb 2020cs.SE cs.AI cs.LGcited by 55

Not on the map. Why?

Abstract (arXiv)

Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well.

Key facts. Cited by 55 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Feb 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 10; 2022: 15; 2023: 8; 2024: 15; 2025: 5; 2026: 2.

55 papers.

Sort:

By area:

2024Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineeringcited by 146
2022Large Language Models Meet NL2Code: A Surveycited by 135
2024Adaptive In-conversation Team Building for Language Model Agentscited by 38
2023SALLM: Security Assessment of Generated Codecited by 35
2022Execution-based Evaluation for Data Science Code Generation Modelscited by 31
2021Code Completion by Modeling Flattened Abstract Syntax Trees as Graphscited by 30
2024Sketch2Code: Evaluating Vision-Language Models for Interactive Web Design Prototypingcited by 29
2023Large Language Models of Code Fail at Completing Code with Potential Bugscited by 28
2021A Survey on Machine Learning Techniques for Source Code Analysiscited by 23
2021Improving Code Summarization with Block-wise Abstract Syntax Tree Splittingcited by 19
2021A Survey on Data-driven Software Vulnerability Assessment and Prioritizationcited by 18
2021Memorization and Generalization in Neural Code Intelligence Modelscited by 17

38 more

2022Assemble Foundation Models for Automatic Code Summarizationcited by 17
2024PromSec: Prompt Optimization for Secure Generation of Functional Source Code with Large Language Models (LLMs)cited by 17
2023FRANC: A Lightweight Framework for High-Quality Code Generationcited by 14
2021DeepCVA: Automated Commit-level Vulnerability Assessment with Deep Multi-task Learningcited by 11
2021Leveraging Unsupervised Learning to Summarize APIs Discussed in Stack Overflowcited by 9
2024Are Latent Vulnerabilities Hidden Gems for Software Vulnerability Prediction? An Empirical Studycited by 9
2024The Fault in our Stars: Quality Assessment of Code Generation Benchmarkscited by 9
2025LLMSecConfig: An LLM-Based Approach for Fixing Software Container Misconfigurationscited by 9
2022T5QL: Taming language models for SQL generationcited by 8
2023Automating Code-Related Tasks Through Transformers: The Impact of Pre-trainingcited by 8
2024Software Vulnerability Prediction in Low-Resource Languages: An Empirical Study of CodeBERT and ChatGPTcited by 8
2022Machine Learning for Software Engineering: A Tertiary Studycited by 7
2021Automated Security Assessment for the Internet of Thingscited by 6
2023LEAP: Efficient and Automated Test Method for NLP Softwarecited by 6
2024Mitigating Data Imbalance for Software Vulnerability Assessment: Does Data Augmentation Help?cited by 6
2024Automated Code-centric Software Vulnerability Assessment: How Far Are We? An Empirical Study in C/C++cited by 6
2024Automatic Data Labeling for Software Vulnerability Prediction Models: How Far Are We?cited by 6
2022On the Use of Fine-grained Vulnerable Code Statements for Software Vulnerability Assessment Modelscited by 5
2021PSIMiner: A Tool for Mining Rich Abstract Syntax Trees from Codecited by 4
2022Learning to Combine Instructions in LLVM Compilercited by 4
2022A Survey of Deep Learning Models for Structural Code Understandingcited by 4
2022MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentationcited by 4
2023Neuron Patching: Semantic-based Neuron-level Language Model Repair for Code Generationcited by 4
2023Neural Machine Translation for Code Generationcited by 3
2024SuperCoder2.0: Technical Report on Exploring the feasibility of LLMs as Autonomous Programmercited by 3
2024Contextualized Data-Wrangling Code Generation in Computational Notebookscited by 3
2024CoT-TL: Low-Resource Temporal Knowledge Representation of Planning Instructions Using Chain-of-Thought Reasoningcited by 3
2025MalCodeAI: Autonomous Vulnerability Detection and Remediation via Language Agnostic Code Reasoningcited by 3
2022Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executablescited by 2
2022LAMNER: Code Comment Generation Using Character Language Model and Named Entity Recognitioncited by 1
2026Bridging Generation and Training: A Systematic Review of Quality Issues in LLMs for Codecited by 1
2021Advances in Electron Microscopy with Deep Learningcited by 0
2022MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domaincited by 0
2022Learning to generate Reliable Broadcast Algorithmscited by 0
2022Topical: Learning Repository Embeddings from Source Code using Attentioncited by 0
2022Code Compliance Assessment as a Learning Problemcited by 0
2023ML-driven Hardware Cost Model for MLIRcited by 0
2024A Systematic Literature Review on the Use of Machine Learning in Software Engineeringcited by 0

50 most cited of 55 All 55 (JSON)