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Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

Bao Rong Chang1, Hsiu-Fen Tsai2,*, Han-Lin Chou1

1 Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, 811, Taiwan
2 Department of Fragrance and Cosmetic Science, Kaohsiung Medical University, Kaohsiung, 811, Taiwan

* Corresponding Author: Hsiu-Fen Tsai. Email: email

Computer Modeling in Engineering & Sciences 2023, 136(1), 107-134. https://doi.org/10.32604/cmes.2023.024018

Abstract

Our previous work has introduced the newly generated program using the code transformation model GPT-2, verifying the generated programming codes through simhash (SH) and longest common subsequence (LCS) algorithms. However, the entire code transformation process has encountered a time-consuming problem. Therefore, the objective of this study is to speed up the code transformation process significantly. This paper has proposed deep learning approaches for modifying SH using a variational simhash (VSH) algorithm and replacing LCS with a piecewise longest common subsequence (PLCS) algorithm to faster the verification process in the test phase. Besides the code transformation model GPT-2, this study has also introduced Microsoft MASS and Facebook BART for a comparative analysis of their performance. Meanwhile, the explainable AI technique using local interpretable model-agnostic explanations (LIME) can also interpret the decision-making of AI models. The experimental results show that VSH can reduce the number of qualified programs by 22.11%, and PLCS can reduce the execution time of selected pocket programs by 32.39%. As a result, the proposed approaches can significantly speed up the entire code transformation process by 1.38 times on average compared with our previous work.

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Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

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APA Style
Chang, B.R., Tsai, H., Chou, H. (2023). Implementation of rapid code transformation process using deep learning approaches. Computer Modeling in Engineering & Sciences, 136(1), 107-134. https://doi.org/10.32604/cmes.2023.024018
Vancouver Style
Chang BR, Tsai H, Chou H. Implementation of rapid code transformation process using deep learning approaches. Comput Model Eng Sci. 2023;136(1):107-134 https://doi.org/10.32604/cmes.2023.024018
IEEE Style
B.R. Chang, H. Tsai, and H. Chou "Implementation of Rapid Code Transformation Process Using Deep Learning Approaches," Comput. Model. Eng. Sci., vol. 136, no. 1, pp. 107-134. 2023. https://doi.org/10.32604/cmes.2023.024018



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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