Mon 5 Nov 2018 10:30 - 10:48 at Sandy Lake - A-TEST II

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.

Mon 5 Nov

10:30 - 12:00: A-TEST - A-TEST II at Sandy Lake
fse-2018-A-TEST10:30 - 10:48
David AdamoUltimate Software, USA, Md Khorrom KhanUniversity of North Texas, USA, Sreedevi KoppulaUniversity of North Texas, USA, Renée BryceUniversity of North Texas, USA
fse-2018-A-TEST10:48 - 11:06
Shogo TakakuraKwansei Gakuin University, Japan, Mitsuyoshi IwatsujiKwansei Gakuin University, Japan, Nagisa IshiuraKwansei Gakuin University
fse-2018-A-TEST11:06 - 11:24
Ákos KissUniversity of Szeged, Hungary, Renáta HodovánUniversity of Szeged, Hungary, Tibor GyimóthyUniversity of Szeged, Hungary
fse-2018-A-TEST11:24 - 11:42
Sten VercammenUniversity of Antwerp, Belgium, Mohammad GhafariUniversity of Bern, Serge DemeyerUniversity of Antwerp, Belgium, Markus BorgRISE Research Institutes of Sweden AB
fse-2018-A-TEST11:42 - 12:00
Thi Anh Tuyet VuongKeio University, Japan, Shingo TakadaKeio University, Japan