Guaranteeing Robustness in a Mobile Learning Application using Formally Verified MAPE Loops
2013 (English)In: Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, New York: IEEE Press, 2013, , 10 p.83-92 p.Conference paper (Refereed)
Mobile learning applications support traditional indoor lectures with outdoor activities using mobile devices. An example scenario is a team of students that use triangulation techniques to learn properties of geometrical figures. In previous work, we developed an agent-based mobile learning application in which students use GPS-enabled phones to calculate distances between them. From practical experience, we learned that the required level of GPS accuracy is not always guaranteed, which undermines the use of the application. In this paper, we explain how we have extended the existing application with a selfadaptation layer, making the system robust to degrading GPS accuracy. The self-adaptive layer is conceived as a set of interacting MAPE loops (Monitor-Analysis-Plan-Execute), distributed over the phones. To guarantee the robustness requirements, we formally specify the self-adaptive behaviors using timed automata, and the required properties using timed computation tree logic. We use the Uppaal tool to model the self-adaptive system and verify the robustness requirements. Finally, we discuss how the formal design supported the implementation of the selfadaptive layer on top of the existing application.
Place, publisher, year, edition, pages
New York: IEEE Press, 2013. , 10 p.83-92 p.
Self-adaptation MAPE Robustness M-Learning
IdentifiersURN: urn:nbn:se:lnu:diva-25908DOI: 10.1109/SEAMS.2013.6595495ISI: 000327972300009ISBN: 978-1-4673-4401-2OAI: oai:DiVA.org:lnu-25908DiVA: diva2:623707
8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), 20-21 May, 2013, San Francisco