@Article{jai.2020.014829,
AUTHOR = {Weipeng Cao, Zhongwu Xie, Xiaofei Zhou, Zhiwu Xu, Cong Zhou, Georgios Theodoropoulos, Qiang Wang},
TITLE = {A Learning Framework for Intelligent Selection of Software Verification  Algorithms},
JOURNAL = {Journal on Artificial Intelligence},
VOLUME = {2},
YEAR = {2020},
NUMBER = {4},
PAGES = {177--187},
URL = {http://www.techscience.com/jai/v2n4/41107},
ISSN = {2579-003X},
ABSTRACT = {Software verification is a key technique to ensure the correctness of 
software. Although numerous verification algorithms and tools have been 
developed in the past decades, it is still a great challenge for engineers to 
accurately and quickly choose the appropriate verification techniques for the 
software at hand. In this work, we propose a general learning framework for the 
intelligent selection of software verification algorithms, and instantiate the 
framework with two state-of-the-art learning algorithms: Broad learning (BL) and 
deep learning (DL). The experimental evaluation shows that the training efficiency 
of the BL-based model is much higher than the DL-based models and the support 
vector machine (SVM)-based models, while the prediction accuracy of the DLbased model is much higher than other models.},
DOI = {10.32604/jai.2020.014829}
}