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Retrieval-Based Transformer Pseudocode Generation

Author

Listed:
  • Anas Alokla

    (Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • Walaa Gad

    (Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • Waleed Nazih

    (College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia)

  • Mustafa Aref

    (Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

  • Abdel-Badeeh Salem

    (Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt)

Abstract

The comprehension of source code is very difficult, especially if the programmer is not familiar with the programming language. Pseudocode explains and describes code contents that are based on the semantic analysis and understanding of the source code. In this paper, a novel retrieval-based transformer pseudocode generation model is proposed. The proposed model adopts different retrieval similarity methods and neural machine translation to generate pseudocode. The proposed model handles words of low frequency and words that do not exist in the training dataset. It consists of three steps. First, we retrieve the sentences that are similar to the input sentence using different similarity methods. Second, pass the source code retrieved (input retrieved) to the deep learning model based on the transformer to generate the pseudocode retrieved. Third, the replacement process is performed to obtain the target pseudo code. The proposed model is evaluated using Django and SPoC datasets. The experiments show promising performance results compared to other language models of machine translation. It reaches 61.96 and 50.28 in terms of BLEU performance measures for Django and SPoC, respectively.

Suggested Citation

  • Anas Alokla & Walaa Gad & Waleed Nazih & Mustafa Aref & Abdel-Badeeh Salem, 2022. "Retrieval-Based Transformer Pseudocode Generation," Mathematics, MDPI, vol. 10(4), pages 1-16, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:4:p:604-:d:750560
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    Citations

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    Cited by:

    1. Jani Dugonik & Mirjam Sepesy Maučec & Domen Verber & Janez Brest, 2023. "Reduction of Neural Machine Translation Failures by Incorporating Statistical Machine Translation," Mathematics, MDPI, vol. 11(11), pages 1-22, May.
    2. Robert Waszkowski & Grzegorz Bocewicz, 2022. "Visibility Matrix: Efficient User Interface Modelling for Low-Code Development Platforms," Sustainability, MDPI, vol. 14(13), pages 1-24, July.
    3. Anas Alokla & Walaa Gad & Waleed Nazih & Mustafa Aref & Abdel-badeeh Salem, 2022. "Pseudocode Generation from Source Code Using the BART Model," Mathematics, MDPI, vol. 10(21), pages 1-14, October.

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