A Learning Framework for Intelligent Selection of Software Verification Algorithms
Weipeng Cao, Zhongwu Xie, Xiaofei Zhou, Zhiwu Xu, Cong Zhou, Georgios Theodoropoulos, Qiang Wang · Journal on artificial intelligence · 2020
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.