Implementation of Rapid Code Transformation Process Using Deep Learning Approaches

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 pro...

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Vydáno v:Computer modeling in engineering & sciences Ročník 136; číslo 1; s. 107 - 134
Hlavní autoři: Rong Chang, Bao, Tsai, Hsiu-Fen, Chou, Han-Lin
Médium: Journal Article
Jazyk:angličtina
Vydáno: Henderson Tech Science Press 2023
Témata:
Algorithms
Deep learning
Explainable artificial intelligence
Machine learning
ISSN:1526-1506, 1526-1492, 1526-1506
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:1526-1506
1526-1492
1526-1506
DOI:10.32604/cmes.2023.024018