Automated, Cost-effective, and Update-driven App Testing

Apps’ pervasive role in our society led to the definition of test automation approaches ensure their dependability. However, state-of-the-art tend generate large numbers inputs and are unlikely achieve more than 50% method coverage. In this article, we propose a strategy significantly higher coverage code affected by updates with much smaller number inputs, thus alleviating oracle problem. More specifically, present ATUA, model-based approach that synthesizes App models static analysis, integrates dynamically refined state abstraction function combines complementary testing strategies, including (1) model structure, (2) code, (3) random exploration, (4) dependencies identified through information retrieval. Its enables ATUA small set exercise only updates. turn, makes common solutions cost-effective, as they involve human effort. A empirical evaluation, conducted 72 versions belonging nine popular Android Apps, has shown is effective less effort-intensive when