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

• URL: http://arxiv.org/abs/2303.09062v1
• Date: Thu, 16 Mar 2023 03:38:08 GMT
• Title: Knowledge Transfer for Pseudo-code Generation from Low Resource Programming Language
• Authors: Ankita Sontakke, Kanika Kalra, Manasi Patwardhan, Lovekesh Vig, Raveendra Kumar Medicherla, Ravindra Naik, Shrishti Pradhan
• Abstract summary: 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 observe an improvement of 23.27% in the success rate of the generated C codes through back translation.
• Score: 13.716669765394293
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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