SIF: a selective instrumentation framework for mobile applications

  • Authors:

  • Shuai Hao;Ding Li;William G.J. Halfond;Ramesh Govindan

  • Affiliations:

  • University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA

  • Venue:
  • Proceeding of the 11th annual international conference on Mobile systems, applications, and services
  • Year:
  • 2013

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Abstract

Mobile app ecosystems have experienced tremendous growth in the last five years. As researchers and developers turn their attention to understanding the ecosystem and its different apps, instrumentation of mobile apps is a much needed emerging capability. In this paper, we explore a selective instrumentation capability that allows users to express instrumentation specifications at a high level of abstraction; these specifications are then used to automatically insert instrumentation into binaries. The challenge in our work is to develop expressive abstractions for instrumentation that can also be implemented efficiently. Designed using requirements derived from recent research that has used instrumented apps, our selective instrumentation framework, SIF, contains abstractions that allow users to compactly express precisely which parts of the app need to be instrumented. It also contains a novel path inspection capability, and provides users feedback on the approximate overhead of the instrumentation specification. Using experiments on our SIF implementation for Android, we show that SIF can be used to compactly (in 20-30 lines of code in most cases) specify instrumentation tasks previously reported in the literature. SIF's overhead is under 2% in most cases, and its instrumentation overhead feedback is within 15% in many cases. As such, we expect that SIF can accelerate studies of the mobile app ecosystem.