Reinforcement Learning for Android GUI Testing

被引：59

作者：

Adamo, David ^{[1
]}

Khan, Md Khorrom ^{[2
]}

Koppula, Sreedevi ^{[2
]}

Bryce, Renee ^{[2
]}

机构：

[1] Ultimate Software Grp Inc, Weston, FL 33326 USA

[2] Univ North Texas, Denton, TX 76207 USA

来源：

PROCEEDINGS OF THE 9TH ACM SIGSOFT INTERNATIONAL WORKSHOP ON AUTOMATING TEST CASE DESIGN, SELECTION, AND EVALUATION (A-TEST '18) | 2018年

关键词：

GUI Testing; Mobile application testing; Android; Q-learning;

D O I：

10.1145/3278186.3278187

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper presents a reinforcement learning approach to automated GUI testing of Android apps. We use a test generation algorithm based on Q-learning to systematically select events and explore the GUI of an application under test without requiring a preexisting abstract model. We empirically evaluate the algorithm on eight Android applications and find that the proposed approach generates test suites that achieve between 3.31% to 18.83% better block-level code coverage than random test generation.

引用

页码：2 / 8

页数：7

共 28 条

[1] Combinatorial-based event sequence testing of Android applications [J].