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A Learning Framework for Intelligent Selection of Software Verification Algorithms

Weipeng Cao1, Zhongwu Xie1, Xiaofei Zhou2, Zhiwu Xu1, Cong Zhou1, Georgios Theodoropoulos3, Qiang Wang3,*

1 Shenzhen University, Shenzhen, China
2 Hangzhou Dianzi University, Hangzhou, China
3 Southern University of Science and Technology, Shenzhen, China

* Corresponding Author: Qiang Wang. Email: email

Journal on Artificial Intelligence 2020, 2(4), 177-187. https://doi.org/10.32604/jai.2020.014829

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.

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APA Style
Cao, W., Xie, Z., Zhou, X., Xu, Z., Zhou, C. et al. (2020). A learning framework for intelligent selection of software verification algorithms. Journal on Artificial Intelligence, 2(4), 177-187. https://doi.org/10.32604/jai.2020.014829
Vancouver Style
Cao W, Xie Z, Zhou X, Xu Z, Zhou C, Theodoropoulos G, et al. A learning framework for intelligent selection of software verification algorithms. J Artif Intell . 2020;2(4):177-187 https://doi.org/10.32604/jai.2020.014829
IEEE Style
W. Cao et al., "A Learning Framework for Intelligent Selection of Software Verification Algorithms," J. Artif. Intell. , vol. 2, no. 4, pp. 177-187. 2020. https://doi.org/10.32604/jai.2020.014829



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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