SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime. (15th May 2018)

• Record Type: Journal Article
• Authors: Zavala, Edith; Franch, Xavier; Marco, Jordi; Knauss, Alessia; Damian, Daniela
• Is Part Of: Expert systems with applications. Volume 98(2018)
• Page Start: 166
• Page End: 188
• Publication Date: 2018-05-15
• Subjects: Self-adaptive systems -- Decentralized control loops -- Machine learning -- Requirements engineering -- Contextual requirements -- Requirements adaptation; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2018.01.009
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Abstract: Runtime uncertainty such as unpredictable resource unavailability, changing environmental conditions and user needs, as well as system intrusions or faults represents one of the main current challenges of self-adaptive systems. Moreover, today's systems are increasingly more complex, distributed, decentralized, etc. and therefore have to reason about and cope with more and more unpredictable events. Approaches to deal with such changing requirements in complex today's systems are still missing. This work presents SACRE (S martA daptation throughC ontextualRE quirements), our approach leveraging an adaptation feedback loop to detect self-adaptive systems' contextual requirements affected by uncertainty and to integrate machine learning techniques to determine the best operationalization of context based on sensed data at runtime. SACRE is a step forward of our former approach ACon which focus had been on adapting the context in contextual requirements, as well as their basic implementation. SACRE primarily focuses on architectural decisions, addressing self-adaptive systems' engineering challenges. Furthering the work on ACon, in this paper, we perform an evaluation of the entire approach in different uncertainty scenarios in real-time in the extremely demanding domain of smart vehicles. The real-time evaluation is conducted in a simulated environment in which the smart vehicle is implemented through software components. The evaluation results provide empirical evidence about the applicability of SACRE in real and complex software system domains.