Visible to the public Biblio

Filters: Author is Zhu, Zhengzhou  [Clear All Filters]
Ren, Yidan, Zhu, Zhengzhou, Chen, Xiangzhou, Ding, Huixia, Zhang, Geng.  2018.  Research on Defect Detection Technology of Trusted Behavior Decision Tree Based on Intelligent Data Semantic Analysis of Massive Data. Proceedings of the 10th International Conference on Computer Modeling and Simulation. :168–175.

With the rapid development of information technology, software systems' scales and complexity are showing a trend of expansion. The users' needs for the software security, software security reliability and software stability are growing increasingly. At present, the industry has applied machine learning methods to the fields of defect detection to repair and improve software defects through the massive data intelligent semantic analysis or code scanning. The model in machine learning is faced with big difficulty of model building, understanding, and the poor visualization in the field of traditional software defect detection. In view of the above problems, we present a point of view that intelligent semantic analysis technology based on massive data, and using the trusted behavior decision tree model to analyze the soft behavior by layered detection technology. At the same time, it is equipped related test environment to compare the tested software. The result shows that the defect detection technology based on intelligent semantic analysis of massive data is superior to other techniques at the cost of building time and error reported ratio.