Functional test generation from UI test scenarios using reinforcement learning for android applications. (5th October 2020)

• Record Type: Journal Article
• Authors: Koroglu, Yavuz; Sen, Alper
• Other Names: Alégroth Emil guestEditor.; Ardito Luca guestEditor.; Coppola Riccardo guestEditor.; Feldt Robert guestEditor.
• Is Part Of: Software testing, verification & reliability. Volume 31:Number 3(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-10-05
• Subjects: mobile applications -- reinforcement learning -- software testing -- temporal logic -- test oracles -- test scenarios; Computer software -- Testing -- Periodicals; Computer software -- Verification -- Periodicals; Computer software -- Reliability -- Periodicals; 005.14
• DOI: 10.1002/stvr.1752
• Languages: English
• ISSNs: 0960-0833

Abstract

Summary: With the ever‐growing Android graphical user interface (GUI) application market, there have been many studies on automated test generation for Android GUI applications. These studies successfully demonstrate how to detect fatal exceptions and achieve high coverage with fully automated test generation engines. However, it is unclear how many GUI functions these engines manage to test. The current best practice for the functional testing of Android GUI applications is to design user interface (UI) test scenarios with a non‐technical and human‐readable language such as Gherkin and implement Java/Kotlin methods for every statement of all the UI test scenarios. Writing tests for UI test scenarios is hard, especially when some scenario statements are high‐level and declarative, so it is not clear what actions should the generated test perform. We propose the Fully Automated Reinforcement LEArning‐Driven specification‐based test generator for Android (FARLEAD‐Android). FARLEAD‐Android first translates the UI test scenario to a GUI‐level formal specification as a linear‐time temporal logic (LTL) formula. The LTL formula guides the test generation and acts as a specified test oracle. By dynamically executing the application under test (AUT), and monitoring the LTL formula, FARLEAD‐Android learns how to produce a witness for the UI test scenario, using reinforcement learning (RL). Our evaluation shows that FARLEAD‐Android is more effective and achieves higher performance in generating tests for UI test scenarios than three known engines: 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. Abstract : ▪▪▪