Automatic Collaborative Testing of Applications Integrating Text Features and Priority Experience Replay

Lizhi Cai, Jin Wang, Mingang Chen, Jilong Wang
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Abstract

With the popularity of deep reinforcement learning(DRL), people have great interest in using deep reinforcement learning for application automated testing. However, most automated testing methods based on reinforcement learning ignore text information, use random sampling in experience replay and ignore the characteristics of Android automated testing. To solve above problem, this paper proposes ITPRTesting(Integrated Text feature information and Priority experience in Testing). It extracts the text information in the interface and uses the BERT algorithm to generate sentence vectors. It fuses the interactive control feature diagram(ICFD), which is mentioned in the previous work, and text information as the state required by reinforcement learning. And in reinforcement learning, the priority experience replay is combined, also the traditional priority experience replay is improved. This paper has carried out experiments on 10 open source applications. The experimental results show that ITPRTesting is superior to other methods in statement coverage and branch coverage.
集成文本功能和优先级体验回放的应用程序的自动协作测试
随着深度强化学习(DRL)的普及,人们对使用深度强化学习进行应用程序自动化测试产生了浓厚的兴趣。然而,大多数基于强化学习的自动化测试方法忽略了文本信息,在体验回放中使用随机抽样,忽略了Android自动化测试的特点。为了解决上述问题,本文提出了ITPRTesting(Integrated Text feature information and Priority experience in Testing)。它提取界面中的文本信息,并使用BERT算法生成句子向量。它融合了之前工作中提到的交互式控制特征图(ICFD)和文本信息作为强化学习所需的状态。在强化学习中,结合了优先级经验重播,对传统的优先级经验重播进行了改进。本文在10个开源应用程序上进行了实验。实验结果表明,ITPRTesting在语句覆盖率和分支覆盖率方面都优于其他方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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