A self-adaptive system (SAS) can reconfigure at run time in response to uncertainty and/or adversity to continually deliver an acceptable level of service. An SAS can experience uncertainty during execution in terms of environmental conditions for which it was not explicitly designed as well as unanticipated combinations of system parameters that result from a self-reconfiguration or misunderstood requirements. Run-time testing provides assurance that an SAS continually behaves as it was designed even as the system reconfigures and the environment changes. Moreover, introducing adaptive capabilities via lightweight evolutionary algorithms into a run-time testing framework can enable an SAS to effectively update its test cases in response to uncertainty alongside the SAS's adaptation engine while still maintaining assurance that requirements are being satisfied. However, the impact of the evolutionary parameters that configure the search process for run-time testing may have a significant impact on test results. Therefore, this paper provides an empirical study that focuses on the mutation parameter that guides online evolution as applied to a run-time testing framework, in the context of an SAS.
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Automated Optimization of Weighted Non-functional Objectives in Self-adaptive Systems
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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- Award ID(s):
- 1657061
- NSF-PAR ID:
- 10088802
- Date Published:
- Journal Name:
- Proceedings of the 10th Symposium on Search-Based Software Engineering (SSBSE)
- Page Range / eLocation ID:
- 182--197
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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