@Article{cmc.2020.010420,
AUTHOR = {Wenbin Bi, Fang Yu, Ning Cao, Wei Huo, Guangsheng Cao, Xiuli Han,
Lili Sun, Russell Higgs},
TITLE = {Research on Data Extraction and Analysis of Software Defect in  IoT Communication Software},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1837--1854},
URL = {http://www.techscience.com/cmc/v65n2/39910},
ISSN = {1546-2226},
ABSTRACT = {Software defect feature selection has problems of feature space dimensionality 
reduction and large search space. This research proposes a defect prediction feature 
selection framework based on improved shuffled frog leaping algorithm (ISFLA).Using 
the two-level structure of the framework and the improved hybrid leapfrog algorithm's 
own advantages, the feature values are sorted, and some features with high correlation are 
selected to avoid other heuristic algorithms in the defect prediction that are easy to 
produce local The case where the convergence rate of the optimal or parameter 
optimization process is relatively slow. The framework improves generalization of 
predictions of unknown data samples and enhances the ability to search for features 
related to learning tasks. At the same time, this framework further reduces the dimension 
of the feature space. After the contrast simulation experiment with other common defect 
prediction methods, we used the actual test data set to verify the framework for multiple 
iterations on Internet of Things (IoT) system platform. The experimental results show 
that the software defect prediction feature selection framework based on ISFLA is very 
effective in defect prediction of IoT communication software. This framework can save 
the testing time of IoT communication software, effectively improve the performance of 
software defect prediction, and ensure the software quality.},
DOI = {10.32604/cmc.2020.010420}
}