Semantic analysis for deep Q-network in android GUI testing

Tuyet Vuong, Shingo Takada

研究成果: Conference contribution

6 被引用数 (Scopus)

抄録

Since the big boom of smartphone and consequently of mobile applications, developers nowadays have many tools to help them create applications easier and faster. However, efficient automated testing tools are still missing, especially for GUI testing. We propose an automated GUI testing tool for Android applications using Deep Q-Network and semantic analysis of the GUI. We identify the semantic meanings of GUI elements and use them as an input to a neural network, which through training, approximates the behavioral model of the application under test. The neural network is trained using the Q-Learning algorithm of Reinforcement Learning. It guides the testing tool to explore more often functionalities that can only be accessed through a specific sequence of actions. The tool does not require access to the source code of the application under test. It obtains higher code coverage and is better at fault detection in comparison to state-of-the-art testing tools.

本文言語English
ホスト出版物のタイトルProceedings - SEKE 2019
ホスト出版物のサブタイトル31st International Conference on Software Engineering and Knowledge Engineering
出版社Knowledge Systems Institute Graduate School
ページ123-128
ページ数6
ISBN(電子版)1891706489
DOI
出版ステータスPublished - 2019 1月 1
イベント31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019 - Lisbon, Portugal
継続期間: 2019 7月 102019 7月 12

出版物シリーズ

名前Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
2019-July
ISSN(印刷版)2325-9000
ISSN(電子版)2325-9086

Conference

Conference31st International Conference on Software Engineering and Knowledge Engineering, SEKE 2019
国/地域Portugal
CityLisbon
Period19/7/1019/7/12

ASJC Scopus subject areas

  • ソフトウェア

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