OAT : An Optimized Android Testing Framework Based on Reinforcement Learning

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

2 Scopus Citations
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Author(s)

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Detail(s)

Original languageEnglish
Title of host publicationTheoretical Aspects of Software Engineering - 17th International Symposium, TASE 2023, Proceedings
EditorsCristina David, Meng Sun
PublisherSpringer, Cham
Pages38-58
ISBN (electronic)9783031352577
ISBN (print)9783031352560
Publication statusPublished - 2023

Publication series

NameLecture Notes in Computer Science
Volume13931
ISSN (Print)0302-9743
ISSN (electronic)1611-3349

Conference

Title17th International Symposium on Theoretical Aspects of Software Engineering (TASE 2023)
PlaceUnited Kingdom
CityBristol
Period4 - 6 July 2023

Abstract

Automated testing of Android applications is always a challenging task. Deep reinforcement learning can continuously optimize the current exploration strategy through the interaction with the application under test and can explore application states that are difficult to reach in the testing process. However, existing state-of-the-art deep reinforcement learning techniques rely on coarse GUI state definitions, which make them hard to explore interesting application states even with the guidance of reward function. In this work, we propose OAT, an optimized automated testing tool for Android applications based on deep reinforcement learning. OAT is designed with a pair of fine-grained state representation and reward function to provide more effective reward incentives for reinforcement learning. OAT also adopts the Monte Carlo Tree Search (MCTS) strategy to more effectively explore promising GUI states. Our experimental evaluation shows that OAT is more effective than the state-of-the-art Android application testing techniques in terms of both code coverage and fault detection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Research Area(s)

  • Android Testing, Deep reinforcement learning, Reward function

Citation Format(s)

OAT: An Optimized Android Testing Framework Based on Reinforcement Learning. / Du, Mengjun; Li, Peiyang; Song, Lian et al.
Theoretical Aspects of Software Engineering - 17th International Symposium, TASE 2023, Proceedings. ed. / Cristina David; Meng Sun. Springer, Cham, 2023. p. 38-58 (Lecture Notes in Computer Science; Vol. 13931).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review