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Identifying implicitly declared self-tuning behavior through dynamic analysis

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2 Author(s)
Ghanbari, H. ; Dept. of Comput. Sci., York Univ., North York, ON ; Litoiu, M.

Autonomic computing programming models explicitly address self management properties by introducing the notion of ldquoAutonomic Element. However, most of currently developed systems do not employ autonomic self-managing programming paradigms. Thus, a current challenge is to find mechanisms to identify the self-tuning behavior and self-tuning parameters which have implicitly been declared using non-autonomic elements, and to expose them for monitoring or to an analysis framework. Static analysis, although it shows a good potential, it results in many false positives. In this paper, we provide a mechanism to identify the tuning parameters more accurately through dynamic analysis.

Published in:

Software Engineering for Adaptive and Self-Managing Systems, 2009. SEAMS '09. ICSE Workshop on

Date of Conference:

18-19 May 2009

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