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ACM Transactions on Autonomous and Adaptive Systems
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Inferring Software Component Interaction Dependencies for Adaptation Support NAEEM ESFAHANI, Google Inc. ERIC YUAN and KYLE R. CANAVERA, George Mason University SAM MALEK, University of California, Irvine A self-managing software system should be able to monitor and analyze its runtime behavior and make adaptation decisions accordingly to meet certain desirable objectives. Traditional software adaptation techniques and recent "models@runtime" approaches usually require an a priori model for a system's dynamic behavior. Oftentimes the model is difficult to define and labor-intensive to maintain, and tends to get out of date due to adaptation and architecture decay. We propose an alternative approach that does not require defining the system's behavior model beforehand, but instead involves mining software component interactions from system execution traces to build a probabilistic usage model, which is in turn used to analyze, plan, and execute adaptations. In this article, we demonstrate how such an approach can be realized and effectively used to address a variety of adaptation concerns. In particular, we describe the details of one application of this approach for safely applying dynamic changes to a running software system without creating inconsistencies. We also provide an overview of two other applications of the approach, identifying potentially
ACM Transactions on Autonomous and Adaptive Systems (TAAS) – Association for Computing Machinery
Published: Feb 3, 2016
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