Weipeng Cao1, Zhongwu Xie1, Xiaofei Zhou2, Zhiwu Xu1, Cong Zhou1, Georgios Theodoropoulos3, Qiang Wang3,*
Journal on Artificial Intelligence, Vol.2, No.4, pp. 177-187, 2020, DOI:10.32604/jai.2020.014829
- 31 December 2020
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 More >
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