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This paper presents the foundations and the real use of a tool to automatically detect anomalies in Internet traffic produced by mobile applications. In particular, our MVE tool is focused on analyzing the impact that user interactions have on the traffic produced and received by the smartphones. To make the analysis exhaustive with regard to the potential user behaviors, we follow a model-based approach to automatically generate test cases to be executed on the smartphones. In addition, we make use of a specification language to define traffic patterns to be compared with the actual traffic in the device. MVE also includes monitoring and verification support to detect executions that do not fit the patterns. In these cases, the developer will obtain detailed information on the user actions that produce the anomaly in order to improve the application. To validate the approach, the paper presents an experimental study with the well-known Spotify app for Android, in which we detected some interesting behaviors. For instance, some HTTP connections do not end successfully due to timeout errors from the remote Spotify service.

Performance Analysis of Spotify® for Android with Model-Based Testing