PADRAIG: Precise Android Automated Input Generation

Jordan Doyle, Thomas Laurent, Anthony Ventresque

Abstract: Android automated test input generation has been a highly researched topic for over a decade and has shown promising results with a variety of approaches. Random input generation is commonly used and the easiest to maintain, but ultimately inefficient. Systematic and search-based approaches produce effective tests but require a disproportionally large generation runtime. Model-based approaches have the additional overhead of modelling the application under test (AUT) but they result in a faster test generation. In this paper we present Precise AnDRoid Automated Input Generation (PADRAIG), a model-based test input generation framework that uses a detailed control flow model of the AUT to generate tests that can achieve higher line coverage, with a lower test generation runtime than the state of the art. We compare the line coverage achieved, and the generation runtime of PADRAIG against 3 state of the art tools, each of which uses a different test input generation technique. Our results, using 19 randomly selected Android apps from the F-Droid application store, show that PADRAIG achieves, on average, 16% more coverage of the AUT than the state of the art and it can generate tests with, on average, 84% less runtime.