# Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language

arXiv: https://arxiv.org/abs/2303.09062 · HTML: https://mlsanity.com/paper/2303.09062/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 44 references that are in the map.
- First posted on arXiv 16 Mar 2023.

## Builds on (most cited first, 29 of 29)

- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [CodeXGLUE: A Machine Learning Benchmark Dataset for Code Understanding and Generation](https://mlsanity.com/paper/2102.04664/) (2021)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [Unified Pre-training for Program Understanding and Generation](https://mlsanity.com/paper/2103.06333/) (2021)
- [UniXcoder: Unified Cross-Modal Pre-training for Code Representation](https://mlsanity.com/paper/2203.03850/) (2022)
- [code2seq: Generating Sequences from Structured Representations of Code](https://mlsanity.com/paper/1808.01400/) (2018)
- [SPoC: Search-based Pseudocode to Code](https://mlsanity.com/paper/1906.04908/) (2019)
- [A Transformer-based Approach for Source Code Summarization](https://mlsanity.com/paper/2005.00653/) (2020)
- [Coarse-to-Fine Decoding for Neural Semantic Parsing](https://mlsanity.com/paper/1805.04793/) (2018)
- [Improving Automatic Source Code Summarization via Deep Reinforcement Learning](https://mlsanity.com/paper/1811.07234/) (2018)
- [Graph-based, Self-Supervised Program Repair from Diagnostic Feedback](https://mlsanity.com/paper/2005.10636/) (2020)
- [Abstract Syntax Networks for Code Generation and Semantic Parsing](https://mlsanity.com/paper/1704.07535/) (2017)
- [Retrieval-Augmented Generation for Code Summarization via Hybrid GNN](https://mlsanity.com/paper/2006.05405/) (2020)
- [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)
- [Language-Agnostic Representation Learning of Source Code from Structure and Context](https://mlsanity.com/paper/2103.11318/) (2021)
- [CoTexT: Multi-task Learning with Code-Text Transformer](https://mlsanity.com/paper/2105.08645/) (2021)
- [CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees](https://mlsanity.com/paper/2108.12987/) (2021)
- [ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation](https://mlsanity.com/paper/2104.08006/) (2021)
- [Code Summarization with Structure-induced Transformer](https://mlsanity.com/paper/2012.14710/) (2020)
- [CodeTrans: Towards Cracking the Language of Silicon's Code Through Self-Supervised Deep Learning and High Performance Computing](https://mlsanity.com/paper/2104.02443/) (2021)
- [Incremental Sampling Without Replacement for Sequence Models](https://mlsanity.com/paper/2002.09067/) (2020)
- [Learning to Represent Programs with Heterogeneous Graphs](https://mlsanity.com/paper/2012.04188/) (2020)
- [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)
- [Generation of Pseudo Code from the Python Source Code using Rule-Based Machine Translation](https://mlsanity.com/paper/1906.06117/) (2019)

## Built on by (most cited first, 1 of 1)

- [Scaling Automatic Extraction of Pseudocode](https://mlsanity.com/paper/2406.04635/) (2024)

## Abstract (arXiv)

Generation of pseudo-code descriptions of legacy source code for software maintenance is a manually intensive task. Recent encoder-decoder language models have shown promise for automating pseudo-code generation for high resource programming languages such as C++, but are heavily reliant on the availability of a large code-pseudocode corpus. Soliciting such pseudocode annotations for codes written in legacy programming languages (PL) is a time consuming and costly affair requiring a thorough understanding of the source PL. In this paper, we focus on transferring the knowledge acquired by the code-to-pseudocode neural model trained on a high resource PL (C++) using parallel code-pseudocode data. We aim to transfer this knowledge to a legacy PL (C) with no PL-pseudocode parallel data for training. To achieve this, we utilize an Iterative Back Translation (IBT) approach with a novel test-cases based filtration strategy, to adapt the trained C++-to-pseudocode model to C-to-pseudocode model. We observe an improvement of 23.27% in the success rate of the generated C codes through back translation, over the successive IBT iteration, illustrating the efficacy of our approach.