A Learning Framework for Intelligent Selection of Software Verification Algorithms

Weipeng Cao, Zhongwu Xie, Xiaofei Zhou, Zhiwu Xu, Cong Zhou, Georgios Theodoropoulos, Qiang Wang
2020 Journal on Artificial Intelligence  
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
more » ... g 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 fatcat:lgmijs5rcvdwplxakeij4v33ae