Self-adaptive software systems are capable of adjusting their behavior at runtime to achieve certain functional or quality-of-service goals. Often a representation that reflects the internal structure of the managed system is used to reason about its characteristics and make the appropriate adaptation decisions. However, runtime conditions can radically change the internal structure in ways that were not accounted for during their design. As a result, unanticipated changes at runtime that violate the assumptions made about the internal structure of the system could degrade the accuracy of the adaptation decisions. We present an approach for engineering self-adaptive software systems that brings about two innovations: 1) a feature-oriented approach for representing engineers' knowledge of adaptation choices that are deemed practical, and 2) an online learning-based approach for assessing and reasoning about adaptation decisions that does not require an explicit representation of the internal structure of the managed software system. Engineers' knowledge, represented in feature-models, adds structure to learning, which in turn makes online learning feasible. We present an empirical evaluation of the framework using a real-world self-adaptive software system. Results demonstrate the framework's ability to accurately learn the changing dynamics of the system while achieving efficient analysis and adaptation.

extended-abstract Share on A learning-based approach for engineering feature-oriented self-adaptive software systems Author: Ahmed Elkhodary George Mason University, Fairfax, VA, USA George Mason University, Fairfax, VA, USAView Profile Authors Info & Claims FSE '10: Proceedings of the eighteenth ACM SIGSOFT international symposium on Foundations of software engineeringNovember 2010Pages 345–348https://doi.org/10.1145/1882291.1882343Published:07 November 2010Publication History 2citation276DownloadsMetricsTotal Citations2Total Downloads276Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Self-adaptive software systems are capable of adjusting their behavior at run-time to achieve certain objectives. Such systems typically employ analytical models specified at design-time to assess their characteristics at run-time and make the appropriate adaptation decisions. However, prior to system's deployment, engineers often cannot foresee the changes in the environment, requirements, and system's operational profile. Therefore, any analytical model used in this setting relies on underlying assumptions that if not held at run-time make the analysis and hence the adaptation decisions inaccurate. We present and evaluate FeatUre-oriented Self-adaptatION (FUSION) framework, which aims to solve this problem by learning the impact of adaptation decisions on the system's goals. The framework (1) allows for automatic online fine-tuning of the adaptation logic to unanticipated conditions, (2) reduces the upfront effort required for building such systems, and (3) makes the run-time analysis of such systems very efficient.