26th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2018), November 4–9, 2018, Lake Buena Vista, FL, USA

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Research Papers
Detection of Energy Inefficiencies in Android Wear Watch Faces
Hailong Zhang, Haowei Wu, and Atanas Rountev
(Ohio State University, USA)
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Abstract: This work considers watch faces for Android Wear devices such as smartwatches. Watch faces are a popular category of apps that display current time and relevant contextual information. Our study of watch faces in an app market indicates that energy efficiency is a key concern for users and developers. The first contribution of this work is the definition of several energy-inefficiency patterns of watch face behavior, focusing on two energy-intensive resources: sensors and displays. Based on these patterns, we propose a control-flow model and static analysis algorithms to identify instances of these patterns. The algorithms use interprocedural control-flow analysis of callback methods and the invocation sequences of these methods. Potential energy inefficiencies are then used for automated test generation and execution, where the static analysis reports are validated via run-time execution. Our experimental results and case studies demonstrate that the analysis achieves high precision and low cost, and provide insights into potential pitfalls faced by developers of watch faces.


Time stamp: 2019-03-18T14:32:17+01:00