Green Mining is a field of MSR that studies software energy consumption and relies on software performance data. Unfortunately there is a severe lack of publicly available software power use performance data. This means that green mining researchers must generate this data themselves by writing tests, building multiple revisions of a product, and then running these tests multiple times (10+) for each software revision while measuring power use. Then, they must aggregate these measurements to estimate the energy consumed by the tests for each software revision. This is time consuming and is made more difficult by the constraints of mobile devices and their OSes. In this paper we propose, implement, and demonstrate Green Miner: the first dedicated hardware mining software repositories testbed. The Green Miner physically measures the energy consumption of mobile devices (Android phones) and automates the testing of applications, and the reporting of measurements back to developers and researchers. The Green Miner has already produced valuable results for commercial Android application developers, and has been shown to replicate other power studies' results.

Extending battery life on mobile devices has become an important topic recently due to the increasing frequency of smartphone adoption. A primary component of smart phone energy consumption is the apps that run on these devices. Many apps have embedded advertising and web browser apps will show ads that are embedded on webpages. Other researchers have found that advertising libraries and advertisements tend to increase power usage. But is the converse true? If we use advertisement blocking software will we consume less energy, or will the overhead of ad-blocking consume more energy?This study seeks to determine the effects of advertisements on energy consumption, and the effects of attempts to block the advertisements. We compared different methods of blocking advertisements on an Android mobile phone platform and compared the power efficiency of these methods. We found many cases where ad-blocking software or methods resulted in increased power use.

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We present a new embedded instrument, with discussion on the challenges of developping embedded instruments, and the practice and theory of NIME evaluation and design. The Shake Stick is a Raspberry Pi-based embedded instrument using SuperCollider for granular synthesis. In our analysis and design, we explore the MINUET design framework, dimension space analysis for inter-instrument comparison, and learning curves. Furthermore, we discuss lessons learned from using the instrument in group improvisation, as well as challenges and prospects for the creation of sound palettes used in the granular synthesis.