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- W4313559382 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." @default.
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- W4313559382 date "2023-01-01" @default.
- W4313559382 modified "2023-09-26" @default.
- W4313559382 title "Implementation of Rapid Code Transformation Process Using Deep Learning Approaches" @default.
- W4313559382 cites W3196661197 @default.
- W4313559382 doi "https://doi.org/10.32604/cmes.2023.024018" @default.
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