# A Survey on Machine Learning Techniques for Source Code Analysis

arXiv: https://arxiv.org/abs/2110.09610 · HTML: https://mlsanity.com/paper/2110.09610/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 106 of its 517 references that are in the map.
- First posted on arXiv 18 Oct 2021.

## Builds on (most cited first, 30 of 106)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [IntelliCode Compose: Code Generation Using Transformer](https://mlsanity.com/paper/2005.08025/) (2020)
- [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)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [Learning to Mine Aligned Code and Natural Language Pairs from Stack Overflow](https://mlsanity.com/paper/1805.08949/) (2018)
- [SequenceR: Sequence-to-Sequence Learning for End-to-End Program Repair](https://mlsanity.com/paper/1901.01808/) (2018)
- [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020)
- [You Autocomplete Me: Poisoning Vulnerabilities in Neural Code Completion](https://mlsanity.com/paper/2007.02220/) (2020)
- [Automated Vulnerability Detection in Source Code Using Deep Representation Learning](https://mlsanity.com/paper/1807.04320/) (2018)
- [An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation](https://mlsanity.com/paper/1812.08693/) (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)
- [CURE: Code-Aware Neural Machine Translation for Automatic Program Repair](https://mlsanity.com/paper/2103.00073/) (2021)
- [Improved Code Summarization via a Graph Neural Network](https://mlsanity.com/paper/2004.02843/) (2020)
- [A Neural Model for Generating Natural Language Summaries of Program Subroutines](https://mlsanity.com/paper/1902.01954/) (2019)
- [DeepBugs: A Learning Approach to Name-based Bug Detection](https://mlsanity.com/paper/1805.11683/) (2018)
- [Generative Code Modeling with Graphs](https://mlsanity.com/paper/1805.08490/) (2018)
- [Neural Code Comprehension: A Learnable Representation of Code Semantics](https://mlsanity.com/paper/1806.07336/) (2018)
- [Multi-task Learning based Pre-trained Language Model for Code Completion](https://mlsanity.com/paper/2012.14631/) (2020)
- [A parallel corpus of Python functions and documentation strings for automated code documentation and code generation](https://mlsanity.com/paper/1707.02275/) (2017)
- [A Syntax-Guided Edit Decoder for Neural Program Repair](https://mlsanity.com/paper/2106.08253/) (2021)
- [Pythia: AI-assisted Code Completion System](https://mlsanity.com/paper/1912.00742/) (2019)
- [Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges](https://mlsanity.com/paper/2002.05442/) (2020)
- [Big Code != Big Vocabulary: Open-Vocabulary Models for Source Code](https://mlsanity.com/paper/2003.07914/) (2020)

## Built on by (most cited first, 24 of 24)

- [Large Language Models Meet NL2Code: A Survey](https://mlsanity.com/paper/2212.09420/) (2022)
- [Natural Language Generation and Understanding of Big Code for AI-Assisted Programming: A Review](https://mlsanity.com/paper/2307.02503/) (2023)
- [Learning to Parallelize in a Shared-Memory Environment with Transformers](https://mlsanity.com/paper/2204.12835/) (2022)
- [Review of Generative AI Methods in Cybersecurity](https://mlsanity.com/paper/2403.08701/) (2024)
- [Syntax-Guided Program Reduction for Understanding Neural Code Intelligence Models](https://mlsanity.com/paper/2205.14374/) (2022)
- [CodeS: Towards Code Model Generalization Under Distribution Shift](https://mlsanity.com/paper/2206.05480/) (2022)
- [The EarlyBIRD Catches the Bug: On Exploiting Early Layers of Encoder Models for More Efficient Code Classification](https://mlsanity.com/paper/2305.04940/) (2023)
- [MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers](https://mlsanity.com/paper/2305.09438/) (2023)
- [Code2Snapshot: Using Code Snapshots for Learning Representations of Source Code](https://mlsanity.com/paper/2111.01097/) (2021)
- [NeuDep: Neural Binary Memory Dependence Analysis](https://mlsanity.com/paper/2210.02853/) (2022)
- [End to End Software Engineering Research](https://mlsanity.com/paper/2112.11858/) (2021)
- [DACOS-A Manually Annotated Dataset of Code Smells](https://mlsanity.com/paper/2303.08729/) (2023)
- [Usenix'23 Extended Version: Smart Learning to Find Dumb Contracts](https://mlsanity.com/paper/2304.10726/) (2023)
- [Enabling Automatic Repair of Source Code Vulnerabilities Using Data-Driven Methods](https://mlsanity.com/paper/2202.03055/) (2022)
- [Evaluating the Impact of Source Code Parsers on ML4SE Models](https://mlsanity.com/paper/2206.08713/) (2022)
- [An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code](https://mlsanity.com/paper/2307.02443/) (2023)
- [Can You Improve My Code? Optimizing Programs with Local Search](https://mlsanity.com/paper/2307.05603/) (2023)
- [Enhancing Source Code Classification Effectiveness via Prompt Learning Incorporating Knowledge Features](https://mlsanity.com/paper/2401.05544/) (2024)
- [On the Effectiveness of Machine Learning-based Call Graph Pruning: An Empirical Study](https://mlsanity.com/paper/2402.07294/) (2024)
- [An Empirical Study of the Imbalance Issue in Software Vulnerability Detection](https://mlsanity.com/paper/2602.12038/) (2026)
- [Extracting Label-specific Key Input Features for Neural Code Intelligence Models](https://mlsanity.com/paper/2202.06474/) (2022)
- [CV4Code: Sourcecode Understanding via Visual Code Representations](https://mlsanity.com/paper/2205.08585/) (2022)
- [CONCORD: Towards a DSL for Configurable Graph Code Representation](https://mlsanity.com/paper/2401.17967/) (2024)
- [Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks](https://mlsanity.com/paper/2604.15390/) (2026)

## Abstract (arXiv)

The advancements in machine learning techniques have encouraged researchers to apply these techniques to a myriad of software engineering tasks that use source code analysis, such as testing and vulnerability detection. Such a large number of studies hinders the community from understanding the current research landscape. This paper aims to summarize the current knowledge in applied machine learning for source code analysis. We review studies belonging to twelve categories of software engineering tasks and corresponding machine learning techniques, tools, and datasets that have been applied to solve them. To do so, we conducted an extensive literature search and identified 479 primary studies published between 2011 and 2021. We summarize our observations and findings with the help of the identified studies. Our findings suggest that the use of machine learning techniques for source code analysis tasks is consistently increasing. We synthesize commonly used steps and the overall workflow for each task and summarize machine learning techniques employed. We identify a comprehensive list of available datasets and tools useable in this context. Finally, the paper discusses perceived challenges in this area, including the availability of standard datasets, reproducibility and replicability, and hardware resources.