@Article{cmes.2021.015673,
AUTHOR = {Bao Rong Chang, Hsiu-Fen Tsai, Po-Wen Su},
TITLE = {Code Transform Model Producing High-Performance Program},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {129},
YEAR = {2021},
NUMBER = {1},
PAGES = {253--277},
URL = {http://www.techscience.com/CMES/v129n1/44207},
ISSN = {1526-1506},
ABSTRACT = {This paper introduces a novel transform method to produce the newly generated programs through code transform
model called the second generation of Generative Pre-trained Transformer (GPT-2) reasonably, improving the
program execution performance significantly. Besides, a theoretical estimation in statistics has given the minimum
number of generated programs as required, which guarantees to find the best one within them. The proposed
approach can help the voice assistant machine resolve the problem of inefficient execution of application code. In
addition to GPT-2, this study develops the variational Simhash algorithm to check the code similarity between
sample program and newly generated program, and conceives the piecewise longest common subsequence algorithm to examine the execution’s conformity from the two programs mentioned above. The code similarity check
deducts the redundant generated programs, and the output conformity check finds the best-performing generative
program. In addition to texts, the proposed approach can also prove the other media, including images, sounds,
and movies. As a result, the newly generated program outperforms the sample program significantly because the
number of code lines reduces 27.21%, and the program execution time shortens 24.62%.},
DOI = {10.32604/cmes.2021.015673}
}