SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime
作者：
Highlights：
• Support of contextual requirements’ adaptation in modern SASs is provided.
• A feedback loop is leveraged to detect requirements affected by runtime uncertainty.
• Machine learning is used to determine the best runtime operationalization of context.
• Validation in the domain of smart vehicles for supporting drowsy drivers is provided.
• Empirical evidence demonstrates the approach applicability in real software domains.
摘要
•Support of contextual requirements’ adaptation in modern SASs is provided.•A feedback loop is leveraged to detect requirements affected by runtime uncertainty.•Machine learning is used to determine the best runtime operationalization of context.•Validation in the domain of smart vehicles for supporting drowsy drivers is provided.•Empirical evidence demonstrates the approach applicability in real software domains.
论文关键词：Self-adaptive systems,Decentralized control loops,Machine learning,Requirements engineering,Contextual requirements,Requirements adaptation
论文评审过程：Received 24 June 2017, Revised 5 December 2017, Accepted 7 January 2018, Available online 12 January 2018, Version of Record 6 February 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.01.009
