Experience paper: Testing of mobile apps is time-consuming and requires a great deal of manual effort. For this reason, industry and academic researchers have proposed a number of test-input- generation techniques for automating app testing. Although useful, these techniques have weaknesses and limitations that often pre- vent them from achieving high coverage. We believe that one of the reasons for these limitations is that tool developers tend to focus mainly on improving the strategy the techniques employ to explore app behavior, whereas limited effort has been put into investigating other ways to improve the performance of these tech- niques. To address this problem, and get a better understanding of the limitations of input-generation techniques for mobile apps, we conducted an in-depth study of the limitations of Monkey–arguably the most widely used tool for automated testing of Android apps. Specifically, in our study, we manually analyzed Monkey’s perfor- mance on 68 benchmarks to identify the common limitations that prevent the tool from achieving better coverage results. We then assessed the coverage improvement that Monkey could achieve if these limitations were eliminated. In our analysis of the results, we also discuss whether other existing test-input-generation tools suffer from these common limitations and provide insights on how they could address them.