# Fine-grained Pseudo-code Generation Method via Code Feature Extraction and Transformer

arXiv: https://arxiv.org/abs/2102.06360 · HTML: https://mlsanity.com/paper/2102.06360/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2021.

## Built on by (most cited first, 6 of 6)

- [Towards an Understanding of Context Utilization in Code Intelligence](https://mlsanity.com/paper/2504.08734/) (2025)
- [BashExplainer: Retrieval-Augmented Bash Code Comment Generation based on Fine-tuned CodeBERT](https://mlsanity.com/paper/2206.13325/) (2022)
- [Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language](https://mlsanity.com/paper/2303.09062/) (2023)
- [Converting Epics/Stories into Pseudocode using Transformers](https://mlsanity.com/paper/2312.05047/) (2023)
- [Enhancing Source Code Classification Effectiveness via Prompt Learning Incorporating Knowledge Features](https://mlsanity.com/paper/2401.05544/) (2024)
- [Scaling Automatic Extraction of Pseudocode](https://mlsanity.com/paper/2406.04635/) (2024)

## Abstract (arXiv)

Pseudo-code written by natural language is helpful for novice developers' program comprehension. However, writing such pseudo-code is time-consuming and laborious. Motivated by the research advancements of sequence-to-sequence learning and code semantic learning, we propose a novel deep pseudo-code generation method DeepPseudo via code feature extraction and Transformer. In particular, DeepPseudo utilizes a Transformer encoder to perform encoding for source code and then use a code feature extractor to learn the knowledge of local features. Finally, it uses a pseudo-code generator to perform decoding, which can generate the corresponding pseudo-code. We choose two corpora (i.e., Django and SPoC) from real-world large-scale projects as our empirical subjects. We first compare DeepPseudo with seven state-of-the-art baselines from pseudo-code generation and neural machine translation domains in terms of four performance measures. Results show the competitiveness of DeepPseudo. Moreover, we also analyze the rationality of the component settings in DeepPseudo.