@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} }