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.

通过从 arXiv 论文中提取近 320,000 个伪代码示例，我们创建了一个庞大的伪代码集合。我们的方法包括一个定制化的提取机制和一个基于随机抽样的验证机制，以优化覆盖范围并检查准确性和可靠性。同时，通过聚类和统计分析，我们提供了对常见伪代码结构的洞察，表明伪代码的使用呈现出指数型增长，凸显其不断增加的重要性。