A framework based on learning techniques for decision-making in self-adaptive software
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Data
2015-01-01
Autores
Affonso, Frank José [UNESP]
Leite, Gustavo [UNESP]
Oliveira, Rafael A.P.
Nakagawa, Elisa Yumi
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Resumo
The development of Self-adaptive Software (SaS) presents specific innovative features compared to traditional ones since this type of software constantly deals with structural and/or behavioral changes at runtime. Capabilities of human administration are showing a decrease in relative effectiveness, since some tasks have been difficult to manage introducing potential problems, such as change management and simple human error. Self-healing systems, a system class of SaS, have emerged as a feasible solution in contrast to management complexity, since such system often combines machine learning techniques with control loops to reduce the number of situations requiring human intervention. This paper presents a framework based on learning techniques and the control loop (MAPE-K) to support the decision-making activity for SaS. In addition, it is noteworthy that this framework is part of a wider project developed by the authors of this paper in previous work (i.e., reference architecture for SaS [1]). Aiming to present the viability of our framework, we have conducted a case study using a flight plan module for Unmanned Aerial Vehicles. The results have shown an environment accuracy of about 80%, enabling us to project good perspectives of contribution to the SaS area and other domains of software systems, and enabling knowledge sharing and technology transfer from academia to industry.
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Decision-making, Framework, Learning Techniques, Reference Architecture, Self-adaptive software
Como citar
Proceedings of the International Conference on Software Engineering and Knowledge Engineering, SEKE, v. 2015-January, p. 24-29.