基于深度学习的网络电视自动化测试算法优化研究
Research on Optimization of Network TV Automation Testing Algorithm Based on Deep Learning
张惠琦1
作者信息
- 1. 迪斯泰克(北京)科技有限公司,北京 100000
- 折叠
摘要
该文提出了一种基于深度学习的新型网络电视自动化测试算法优化方案,方案采用卷积神经网络和强化学习等深度学习技术,实现了对UI控件的准确识别和动态测试用例生成,有效解决了系统复杂度高、界面无障碍支持度低等问题.文中介绍了算法的具体实现细节和优化策略,并通过大量实验论证了该算法的有效性和优越性.
Abstract
The article proposes a new network television automation testing algorithm optimization scheme based on deep learning.This scheme adopts deep learning techniques such as convolutional neural networks and reinforcement learning to achieve accurate recognition of UI controls and dynamic test case generation,effectively solving problems such as high system complexity and low interface accessibility support.The article introduces the specific implementation details and optimization strategies of the algorithm,and demonstrates its effectiveness and superiority through a large number of experiments.
关键词
网络电视/自动化测试/深度学习/卷积神经网络/强化学习Key words
network TV/automated testing/deep learning/convolutional neural networks/reinforcement learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据