AIP: Scalable and Reproducible Execution Traces in Energy Studies on Mobile Devices

Energy consumption in mobile applications is a key area of software engineering studies, since any advance could affect billions of devices. Currently, several software-based energy calculation tools can provide close estimates of the energy consumed by mobile applications without relying on physical hardware, offering new opportunities to conduct large-scale energy studies in mobile devices. In these studies, one key step of data collection is generating events, since it allows exercising specific parts of the code and, as a consequence, assessing their energy consumption. Given the fact that manually generating events by interacting with applications is time-consuming and not scalable, large-scale studies often use software-based tools to automate event generation to profile devices. Existing tools rely on randomly generated events, which undermines the reproducibility and generalizability of such studies.We present AIP (Android Instrumentation Profiler), an alternative to existing software-based event generation tools such as Monkey. AIP uses instrumented tests as a source of event generation, which enables the targeting of complex use cases for energy consumption estimations, as well as the creation of fully reproducible events and execution traces, while maintaining the scaling abilities of other state-of-the-art tools. The tool and demo video can be found on https://github.com/ONourry/AndroidInstrumentationProfiler.