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

Yuki Moriguchi, Shingo Takada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - SEKE 2021
Subtitle of host publication33rd International Conference on Software Engineering and Knowledge Engineering
PublisherKnowledge Systems Institute Graduate School
Pages522-527
Number of pages6
ISBN (Electronic)1891706527
DOIs
Publication statusPublished - 2021
Event33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021 - Pittsburgh, United States
Duration: 2021 Jul 12021 Jul 10

Publication series

NameProceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE
Volume2021-July
ISSN (Print)2325-9000
ISSN (Electronic)2325-9086

Conference

Conference33rd International Conference on Software Engineering and Knowledge Engineering, SEKE 2021
Country/TerritoryUnited States
CityPittsburgh
Period21/7/121/7/10

Keywords

  • Android application
  • Q-learning
  • Software testing

ASJC Scopus subject areas

  • Software

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