Which factors affect q-learning-based automated android testing? - A study focusing on algorithm, learning target, and reward function

Yuki Moriguchi, Shingo Takada

研究成果: Conference contribution

抄録

With the spread of smartphones, the importance of automated testing of mobile applications has increased. However, many current approaches are inadequate, as they are not able to test functions that are available only on hard-to-reach GUI, which is a screen that can be reached only through a specific sequence of input events. To solve this problem, there has been an increase in testing research based on reinforcement learning, specifically Q-learning. Each research uses different learning targets and reward function. Testing research has also been done using Deep Q-Network, which extends reinforcement learning in a “deep” way. Although each work has conducted their own evaluation, it is not clear how the combination of learning algorithm, learning target, and reward function affects the result. To bridge this gap, we have conducted an empirical study comparing eight possible combinations. Our study found that the combination of Deep Q-Network as the learning algorithm, component as the learning target, and GUI change ratio as the reward function had the highest test quality in terms of code coverage.

本文言語English
ホスト出版物のタイトルProceedings - SEKE 2021
ホスト出版物のサブタイトル33rd International Conference on Software Engineering and Knowledge Engineering
出版社Knowledge Systems Institute Graduate School
ページ522-527
ページ数6
ISBN(電子版)1891706527
DOI
出版ステータスPublished - 2021
イベント33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021 - Pittsburgh, United States
継続期間: 2021 7 12021 7 10

出版物シリーズ

名前Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
2021-July
ISSN(印刷版)2325-9000
ISSN(電子版)2325-9086

Conference

Conference33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021
国/地域United States
CityPittsburgh
Period21/7/121/7/10

ASJC Scopus subject areas

  • ソフトウェア

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