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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:

Journal on Artificial Intelligence 2020, 2(4), 177-187.

Received 20 October 2020; Accepted 29 November 2020; Issue published 31 December 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.


Software verification; algorithm selection; broad learning; deep learning

Cite This Article

W. Cao, Z. Xie, X. Zhou, Z. Xu, C. Zhou et al., "A learning framework for intelligent selection of software verification algorithms," Journal on Artificial Intelligence, vol. 2, no.4, pp. 177–187, 2020.

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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