Abstract

Simulation of testing web applications using fuzzing and dynamic Bayesian networks is considered. The basic principles of optimizing the structure of dynamic Bayesian networks are formulated, and hybrid algorithms for learning and probabilistic inference using quasi-Newtonian algorithms and elements of the theory of sufficient statistics are proposed.

中文翻译：

---

动态贝叶斯网络作为模糊测试 Web 应用程序的测试工具

摘要

考虑使用模糊测试和动态贝叶斯网络模拟测试 Web 应用程序。制定了优化动态贝叶斯网络结构的基本原则，并提出了使用准牛顿算法和充分统计理论要素进行学习和概率推理的混合算法。