# Memorization and Generalization in Neural Code Intelligence Models

arXiv: https://arxiv.org/abs/2106.08704 · HTML: https://mlsanity.com/paper/2106.08704/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 50 of its 94 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 30 of 50)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks](https://mlsanity.com/paper/1802.08232/) (2018)
- [Generalization through Memorization: Nearest Neighbor Language Models](https://mlsanity.com/paper/1911.00172/) (2019)
- [Gated Graph Sequence Neural Networks](https://mlsanity.com/paper/1511.05493/) (2015)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks](https://mlsanity.com/paper/2103.14749/) (2021)
- [Insights on representational similarity in neural networks with canonical correlation](https://mlsanity.com/paper/1806.05759/) (2018)
- [Adversarially Robust Generalization Requires More Data](https://mlsanity.com/paper/1804.11285/) (2018)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [A Survey of Machine Learning for Big Code and Naturalness](https://mlsanity.com/paper/1709.06182/) (2017)
- [Learning to Represent Programs with Graphs](https://mlsanity.com/paper/1711.00740/) (2017)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels](https://mlsanity.com/paper/1905.05040/) (2019)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [What do Neural Machine Translation Models Learn about Morphology?](https://mlsanity.com/paper/1704.03471/) (2017)
- [Learning to Learn from Noisy Labeled Data](https://mlsanity.com/paper/1812.05214/) (2018)
- [The Adverse Effects of Code Duplication in Machine Learning Models of Code](https://mlsanity.com/paper/1812.06469/) (2018)
- [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018)
- [A Convolutional Attention Network for Extreme Summarization of Source Code](https://mlsanity.com/paper/1602.03001/) (2016)
- [Improved Code Summarization via a Graph Neural Network](https://mlsanity.com/paper/2004.02843/) (2020)

## Built on by (most cited first, 19 of 19)

- [Vulnerability Detection with Code Language Models: How Far Are We?](https://mlsanity.com/paper/2403.18624/) (2024)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [Studying LLM Performance on Closed- and Open-source Data](https://mlsanity.com/paper/2402.15100/) (2024)
- [Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models](https://mlsanity.com/paper/2205.14374/) (2022)
- [Enhancing Generalization in Chain of Thought Reasoning for Smaller Models](https://mlsanity.com/paper/2501.09804/) (2025)
- [Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code](https://mlsanity.com/paper/2111.01097/) (2021)
- [A Critical Study of What Code-LLMs (Do Not) Learn](https://mlsanity.com/paper/2406.11930/) (2024)
- [Promise and Peril of Collaborative Code Generation Models: Balancing Effectiveness and Memorization](https://mlsanity.com/paper/2409.12020/) (2024)
- [ObscuraCoder: Powering Efficient Code LM Pre-Training Via Obfuscation Grounding](https://mlsanity.com/paper/2504.00019/) (2025)
- [Study of Distractors in Neural Models of Code](https://mlsanity.com/paper/2303.01739/) (2023)
- [Trojans in Large Language Models of Code: A Critical Review through a Trigger-Based Taxonomy](https://mlsanity.com/paper/2405.02828/) (2024)
- [Redundancy and Concept Analysis for Code-trained Language Models](https://mlsanity.com/paper/2305.00875/) (2023)
- [Measuring Impacts of Poisoning on Model Parameters and Neuron Activations: A Case Study of Poisoning CodeBERT](https://mlsanity.com/paper/2402.12936/) (2024)
- [Backdoors in Code Summarizers: How Bad Is It?](https://mlsanity.com/paper/2506.01825/) (2025)
- [Effective Code Membership Inference for Code Completion Models via Adversarial Prompts](https://mlsanity.com/paper/2511.15107/) (2025)
- [Testing the Robustness of a BiLSTM-based Structural Story Classifier](https://mlsanity.com/paper/2201.02733/) (2022)
- [Extracting Label-specific Key Input Features for Neural Code Intelligence Models](https://mlsanity.com/paper/2202.06474/) (2022)
- [Artificial Neuropsychology: Are Large Language Models Developing Executive Functions?](https://mlsanity.com/paper/2305.04134/) (2023)
- [Malicious and Unintentional Disclosure Risks in Large Language Models for Code Generation](https://mlsanity.com/paper/2503.22760/) (2025)

## Abstract (arXiv)

Deep Neural Networks (DNNs) are increasingly being used in software engineering and code intelligence tasks. These are powerful tools that are capable of learning highly generalizable patterns from large datasets through millions of parameters. At the same time, their large capacity can render them prone to memorizing data points. Recent work suggests that the memorization risk manifests especially strongly when the training dataset is noisy, involving many ambiguous or questionable samples, and memorization is the only recourse. The goal of this paper is to evaluate and compare the extent of memorization and generalization in neural code intelligence models. It aims to provide insights on how memorization may impact the learning behavior of neural models in code intelligence systems. To observe the extent of memorization in models, we add random noise to the original training dataset and use various metrics to quantify the impact of noise on various aspects of training and testing. We evaluate several state-of-the-art neural code intelligence models and benchmarks based on Java, Python, and Ruby codebases. Our results highlight important risks: millions of trainable parameters allow the neural networks to memorize anything, including noisy data, and provide a false sense of generalization. We observed all models manifest some forms of memorization. This can be potentially troublesome in most code intelligence tasks where they rely on rather noise-prone and repetitive data sources, such as code from GitHub. To the best of our knowledge, we provide the first study to quantify memorization effects in the domain of software engineering and code intelligence systems. This work raises awareness and provides new insights into important issues of training neural models in code intelligence systems that are usually overlooked by software engineering researchers.