mlsanityData to 7 Oct 2026

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

arXiv 2303.09062 ↗First posted 16 Mar 2023cs.SE cs.AI cs.LGcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 29 of its 44 references that are in the map. First posted on arXiv 16 Mar 2023.

Builds on

29 of 44 references.

References · 29

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17 more
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Citing papers per year: 2024: 1.

1 paper.

  1. 2024Scaling Automatic Extraction of Pseudocodecited by 0