SACRE: Supporting contextual requirements’ adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

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 (Smart Adaptation through Contextual REquirements), 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 selfadaptive 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.Peer ReviewedPostprint (author's final draft

SACRE: Supporting contextual requirements' adaptation in modern self-adaptive systems in the presence of uncertainty at runtime

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 (Smart Adaptation through Contextual REquirements), 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.Comment: 45 pages, journal article, 14 figures, 9 tables, CC-BY-NC-ND 4.0 licens

Fusione Termonucleare Controllata - Il punto sulla ricerca

Adapting user interfaces to different contexts of use is essential to enhance usability. Adaptation enhances user satisfaction by meeting changing context of use requirements. However, given the variety of contexts of use, and the significant amount of involved information and contextual treatments, transformations of user interface models that consider adaptation become complex. This complexity becomes a challenge when trying to add new adaptation rules or modify the transformation. In this paper, we present “Adapt-first”, an adaptation approach intended to simplify adaptation within model based user interfaces. It capitalizes on differentiating adaptations and concretization via two transformation techniques: Concretization and translation. First-Adapt approach aims at reducing complexity and maintenance efforts of transformations from a model to another

Adapt-First: a MDE Transformation Approach for Supporting User Interface Adaptation

Adapting user interfaces to different contexts of use is essential to enhance usability. Adaptation enhances user satisfaction by meeting changing context of use requirements. However, given the variety of contexts of use, and the significant amount of involved information and contextual treatments, transformations of user interface models that consider adaptation become complex. This complexity becomes a challenge when trying to add new adaptation rules or modify the transformation. In this paper, we present “Adapt-first”, an adaptation approach intended to simplify adaptation within model based user interfaces. It capitalizes on differentiating adaptations and concretization via two transformation techniques: concretization and translation. First-Adapt approach aims at reducing complexity and maintenance efforts of transformations from a model to another

Adaptive development and maintenance of user-centric software systems

A software system cannot be developed without considering the various facets of its environment. Stakeholders – including the users that play a central role – have their needs, expectations, and perceptions of a system. Organisational and technical aspects of the environment are constantly changing. The ability to adapt a software system and its requirements to its environment throughout its full lifecycle is of paramount importance in a constantly changing environment. The continuous involvement of users is as important as the constant evaluation of the system and the observation of evolving environments. We present a methodology for adaptive software systems development and maintenance. We draw upon a diverse range of accepted methods including participatory design, software architecture, and evolutionary design. Our focus is on user-centred software systems

VOLARE: Adaptive Web Service Discovery Middleware for Mobile Systems

With the recent advent and widespread use of smart mobile devices, the flexibility and versatility offered by Service Oriented Architecture's (SOA) makes it an ideal approach to use in the rapidly changing mobile environment. However, the mobile setting presents a set of new challenges that service discovery methods developed for nonmobile environments cannot address. The requirements a mobile client device will have from a Web service may change due to changes in the context or the resources of the client device. In a similar manner, a mobile device that acts as a Web service provider will have different capabilities depending on its status, which may also change dramatically during runtime. This paper introduces VOLARE, a middleware-based solution that will monitor the resources and context of the device, and adapt service requests accordingly. The same method will be used to adapt the Quality of Service (QoS) levels advertised by service providers, to realistically reflect each provider's capabilities at any given moment. This approach will allow for more resource-efficient and accurate service discovery in mobile systems and will enable more reliable provider functionality in mobile devices