ACon: A learning-based approach to deal with uncertainty in contextual requirements at runtime

Context: Runtime uncertainty such as unpredictable operational environment and failure of sensors that gather environmental data is a well-known challenge for adaptive systems. Objective: To execute requirements that depend on context correctly, the system needs up-to-date knowledge about the context relevant to such requirements. Techniques to cope with uncertainty in contextual requirements are currently underrepresented. In this paper we present ACon (Adaptation of Contextual requirements), a data-mining approach to deal with runtime uncertainty affecting contextual requirements. Method: ACon uses feedback loops to maintain up-to-date knowledge about contextual requirements based on current context information in which contextual requirements are valid at runtime. Upon detecting that contextual requirements are affected by runtime uncertainty, ACon analyses and mines contextual data, to (re-)operationalize context and therefore update the information about contextual requirements. Results: We evaluate ACon in an empirical study of an activity scheduling system used by a crew of 4 rowers in a wild and unpredictable environment using a complex monitoring infrastructure. Our study focused on evaluating the data mining part of ACon and analysed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty affecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analysing the environment to adapt contextual requirements and complements existing requirements monitoring approaches by keeping the requirements monitoring specification up-to-date. Consequently, it avoids manual analysis that is usually costly in today’s complex system environments.Peer ReviewedPostprint (author's final draft

A survey on engineering approaches for self-adaptive systems (extended version)

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation

Modeling and Selection of Software Service Variants

Providers and consumers have to deal with variants of software services, which are alternative instances of a services design, implementation, deployment, or operation. This work develops the service feature modeling language to represent software service variants and a suite of methods to select variants for development or delivery. An evaluation describes the systems implemented to make use of service feature modeling and its application to two real-world use cases

Requirements Engineering for Adaptive Service Based Applications

none2Service-Based Applications (SBA) are inherently open and distributed, as they rely on third-party services that are available over the Internet, and have to cope with the dynamism of such operating environment. This motivates the need for SBA to be self-adaptive to accommodate changes in service availability and performance, in consumers’ needs and preferences, and more generally in the operational environment, which may occur at run-time. Engineering such applications significantly challenges the role of requirements engineering (RE). Usually, RE is carried out at the outset of the whole development process, but in the context of SBA, RE activities are also needed at run-time thus enabling a seamless SBA evolution. In this paper, we investigate RE for SBA at run-time proposing a method that supports the continuous refinement of requirements artifacts at run-time, which involves consumers and the SBA itself as primary stakeholders.Nauman A. Qureshi; Anna PeriniQureshi, Nauman Ahmed; Perini, Ann