# Scaling Automatic Extraction of Pseudocode

arXiv: https://arxiv.org/abs/2406.04635 · HTML: https://mlsanity.com/paper/2406.04635/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 7 Jun 2024.

## Builds on (most cited first, 21 of 21)

- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [InCoder: A Generative Model for Code Infilling and Synthesis](https://mlsanity.com/paper/2204.05999/) (2022)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [Unsupervised Translation of Programming Languages](https://mlsanity.com/paper/2006.03511/) (2020)
- [Nougat: Neural Optical Understanding for Academic Documents](https://mlsanity.com/paper/2308.13418/) (2023)
- [SPoC: Search-based Pseudocode to Code](https://mlsanity.com/paper/1906.04908/) (2019)
- [Graph-based, Self-Supervised Program Repair from Diagnostic Feedback](https://mlsanity.com/paper/2005.10636/) (2020)
- [TableFormer: Table Structure Understanding with Transformers](https://mlsanity.com/paper/2203.01017/) (2022)
- [Leveraging Automated Unit Tests for Unsupervised Code Translation](https://mlsanity.com/paper/2110.06773/) (2021)
- [DOBF: A Deobfuscation Pre-Training Objective for Programming Languages](https://mlsanity.com/paper/2102.07492/) (2021)
- [Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction](https://mlsanity.com/paper/1906.09317/) (2019)
- [AxCell: Automatic Extraction of Results from Machine Learning Papers](https://mlsanity.com/paper/2004.14356/) (2020)
- [TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics](https://mlsanity.com/paper/2101.10273/) (2021)
- [Incremental Sampling Without Replacement for Sequence Models](https://mlsanity.com/paper/2002.09067/) (2020)
- [Prompting with Pseudo-Code Instructions](https://mlsanity.com/paper/2305.11790/) (2023)
- [Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization](https://mlsanity.com/paper/2006.16205/) (2020)
- [Fine-grained Pseudo-code Generation Method via Code Feature Extraction and Transformer](https://mlsanity.com/paper/2102.06360/) (2021)
- [Semantic Scaffolds for Pseudocode-to-Code Generation](https://mlsanity.com/paper/2005.05927/) (2020)
- [ScanSSD: Scanning Single Shot Detector for Mathematical Formulas in PDF Document Images](https://mlsanity.com/paper/2003.08005/) (2020)
- [Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language](https://mlsanity.com/paper/2303.09062/) (2023)

## Abstract (arXiv)

Pseudocode in a scholarly paper provides a concise way to express the algorithms implemented therein. Pseudocode can also be thought of as an intermediary representation that helps bridge the gap between programming languages and natural languages. Having access to a large collection of pseudocode can provide various benefits ranging from enhancing algorithmic understanding, facilitating further algorithmic design, to empowering NLP or computer vision based models for tasks such as automated code generation and optical character recognition (OCR). We have created a large pseudocode collection by extracting nearly 320,000 pseudocode examples from arXiv papers. This process involved scanning over 2.2 million scholarly papers, with 1,000 of them being manually inspected and labeled. Our approach encompasses an extraction mechanism tailored to optimize the coverage and a validation mechanism based on random sampling to check its accuracy and reliability, given the inherent heterogeneity of the collection. In addition, we offer insights into common pseudocode structures, supported by clustering and statistical analyses. Notably, these analyses indicate an exponential-like growth in the usage of pseudocodes, highlighting their increasing significance.