Abstract
A self-adaptive system (SAS) can reconfigure at run time in response to adverse combinations of system and environmental conditions in order to continuously satisfy its requirements. Moreover, SASs are subject to cross-cutting non-functional requirements (NFRs), such as performance, security, and usability, that collectively characterize how functional requirements (FRs) are to be satisfied. In many cases, the trigger for adapting an SAS may be due to a violation of one or more NFRs. For a given NFR, different combinations of hierarchically-organized FRs may yield varying degrees of satisfaction (i.e., satisficement). This paper presents Providentia, a search-based technique to optimize NFR satisficement when subjected to various sources of uncertainty (e.g., environment, interactions between system elements, etc.). Providentia searches for optimal combinations of FRs that, when considered with different subgoal decompositions and/or differential weights, provide optimal satisficement of NFR objectives. Experimental results suggest that using an SAS goal model enhanced with search-based optimization significantly improves system performance when compared with manually- and randomly-generated weights and subgoals.
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Notes
- 1.
This work does not use the KAOS formal refinement infrastructure.
- 2.
For this paper, we select a threshold of 0.4 to signify requirement non-satisfaction based on empirical evidence.
- 3.
The Wilcoxon-Mann-Whitney U-test was performed for all presented statistics.
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Acknowledgements
This work has been supported in part by grants from the NSF (CNS-1657061, CNS-1305358, and DBI-0939454), the Michigan Space Grant Consortium, the Comcast Innovation Fund, Oakland University, Ford Motor Company, General Motors Research, the Air Force Research Laboratory (AFRL) under agreement number FA8750-16-2-0284, and Michigan State University through the Institute for Cyber-Enabled Research. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and do not necessarily represent the opinions of the sponsors.
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Bowers, K.M., Fredericks, E.M., Cheng, B.H.C. (2018). Automated Optimization of Weighted Non-functional Objectives in Self-adaptive Systems. In: Colanzi, T., McMinn, P. (eds) Search-Based Software Engineering. SSBSE 2018. Lecture Notes in Computer Science(), vol 11036. Springer, Cham. https://doi.org/10.1007/978-3-319-99241-9_9
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