Mon 5 Nov 2018 11:42 - 12:00 at Sandy Lake - A-TEST II

In recent years, researchers have actively proposed tools to automate testing for Android applications. Their techniques, however, still encounter major difficulties. First is the difficulty of achieving high code coverage because applications usually have a large number of possible combinations of operations and transitions, which makes testing all possible scenarios time-consuming and ineffective for large systems. Second is the difficulty of achieving a wide range of application functionalities, because some functionalities can only be reached through a specific sequence of events. Therefore they are tested less often in random testing. Facing these problems, we apply a reinforcement learning algorithm called Q-learning to take advantage of both random and model-based testing. A Q-learning agent interacts with the Android application, builds a behavioral model gradually and generates test cases based on the model. The agent explores the application in an optimal way that reveals as much functionalities of the application as possible. The exploration using Q-learning improves code coverage in comparison to random and model-based testing and is able to detect faults in applications under test.

Mon 5 Nov
Times are displayed in time zone: Guadalajara, Mexico City, Monterrey change

10:30 - 12:00: A-TEST IIA-TEST at Sandy Lake
10:30 - 10:48
Talk
Reinforcement Learning for Android GUI Testing
A-TEST
David AdamoUltimate Software, USA, Md Khorrom KhanUniversity of North Texas, USA, Sreedevi KoppulaUniversity of North Texas, USA, Renée BryceUniversity of North Texas, USA
10:48 - 11:06
Talk
Extending Equivalence Transformation Based Program Generator for Random Testing of C Compilers
A-TEST
Shogo TakakuraKwansei Gakuin University, Japan, Mitsuyoshi IwatsujiKwansei Gakuin University, Japan, Nagisa IshiuraKwansei Gakuin University
11:06 - 11:24
Talk
HDDr: A Recursive Variant of the Hierarchical Delta Debugging Algorithm
A-TEST
Ákos KissUniversity of Szeged, Hungary, Renáta HodovánUniversity of Szeged, Hungary, Tibor GyimóthyUniversity of Szeged, Hungary
11:24 - 11:42
Talk
Goal-Oriented Mutation Testing with Focal Methods
A-TEST
Sten VercammenUniversity of Antwerp, Belgium, Mohammad GhafariUniversity of Bern, Serge DemeyerUniversity of Antwerp, Belgium, Markus BorgRISE Research Institutes of Sweden AB
Pre-print
11:42 - 12:00
Talk
A Reinforcement Learning Based Approach to Automated Testing of Android Applications
A-TEST
Thi Anh Tuyet VuongKeio University, Japan, Shingo TakadaKeio University, Japan