Reinforcement Learning-Driven Test Generation for Android GUI Applications using Formal Specifications
This addresses the challenge of testing specific GUI functions in mobile applications with automated oracles, though it is incremental as it builds on existing test generation methods.
The paper tackles the problem of automated test generation for Android GUI applications by proposing FARLEAD-Android, which uses reinforcement learning guided by formal specifications (LTL formulas) to generate tests and act as test oracles, achieving higher effectiveness and performance compared to Random, Monkey, and QBEa approaches.
There have been many studies on automated test generation for mobile Graphical User Interface (GUI) applications. These studies successfully demonstrate how to detect fatal exceptions and achieve high code and activity coverage with fully automated test generation engines. However, it is unclear how many GUI functions these engines manage to test. Furthermore, these engines implement only implicit test oracles. We propose Fully Automated Reinforcement LEArning-Driven Specification-Based Test Generator for Android (FARLEAD-Android). FARLEAD-Android accepts a GUI-level formal specification as a Linear-time Temporal Logic (LTL) formula. By dynamically executing the Application Under Test (AUT), it learns how to generate a test that satisfies the LTL formula using Reinforcement Learning (RL). The LTL formula does not just guide the test generation but also acts as a specified test oracle, enabling the developer to define automated test oracles for a wide variety of GUI functions by changing the formula. Our evaluation shows that FARLEAD-Android is more effective and achieves higher performance in generating tests for specified GUI functions than three known approaches, Random, Monkey, and QBEa. To the best of our knowledge, FARLEAD-Android is the first fully automated mobile GUI testing engine that uses formal specifications.