STRL:基于强化学习的测试算法OACSTPCD
STRL:Testing Algorithm Based on Reinforcement Learning
强化学习因为不需要大量样本进行训练而采用与环境交互的方式产生动态数据的特点,使得强化学习成为近些年机器学习领域的研究热点.本文提出一种新的基于强化学习的软件测试框架STRL,能有效解决回归测试耗时时间久,状态覆盖率低的问题.STRL利用强化学习算法PPO实现高效的自适应探索.实验结果表明,STRL算法在状态覆盖率和测试时间方面都优于人工测试和自动化脚本测试.
Reinforcement learning has become research focus in the field of machine learning in recent years due to its character-istic of generating dynamic data through interaction with the environment without requiring a large number of samples for training.This paper proposes a new software testing framework STRL based on reinforcement learning,which can effectively solve the problem of long time consuming and low state coverage of regression testing.STRL utilizes …查看全部>>
赵花蕊
河南省平台经济发展指导中心,河南 郑州 450008
计算机与自动化
人工智能软件智能化软件传统软件软件生命周期强化学习
artificial intelligence softwareintelligent softwaretraditional softwaresoftware lifecyclereinforcement learning
《计算机与现代化》 2024 (8)
5-10,6
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