Model-based fault detection in context-aware adaptive applications

  • Authors:

  • Michele Sama;David S. Rosenblum;Zhimin Wang;Sebastian Elbaum

  • Affiliations:

  • University College London, London, UK;University College London, London, UK;University of Nebraska - Lincoln, Lincoln, NE;University of Nebraska - Lincoln, Lincoln, NE

  • Venue:
  • Proceedings of the 16th ACM SIGSOFT International Symposium on Foundations of software engineering
  • Year:
  • 2008

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Abstract

Applications running on mobile devices are heavily context-aware and adaptive, leading to new analysis and testing challenges as streams of context values drive these applications to undesired configurations that are not easily exposed by existing validation techniques. We address this challenge by employing a finite-state model of adaptive behavior to enable the detection of faults caused by (1) erroneous adaptation logic, and (2) asynchronous updating of context information, which leads to inconsistencies between the external physical context and its internal representation within an application. We identify a number of adaptation fault patterns, each describing a class of faulty behaviors that we detect automatically by analyzing the system's adaptation model. We illustrate our approach on a simple but realistic application in which a cellphone's configuration profile is changed automatically based on the user's location, speed and surrounding environment.