Context variability modeling for runtime configuration of service-based dynamic software product lines

Murguzur, Aitor; Capilla, Rafael; Trujillo, Salvador; Ortiz, Óscar; Lopez-Herrejon, Roberto E.

doi:10.1145/2647908.2655957

Cited by 18 publications

(10 citation statements)

References 28 publications

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“…Figure 5 shows these dimensions and some of their possible values which will be reviewed below. [51,52] or what components are di®erent between the variants of the system.…”

Section: Planningmentioning

confidence: 99%

Dynamic Software Product Line Engineering: A Reference Framework

Bashari

Bagheri

2017

Int. J. Soft. Eng. Knowl. Eng.

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Runtime adaptive systems are able to dynamically transform their internal structure, and hence their behavior, in response to internal or external changes. Such transformations provide the basis for new functionalities or improvements of the non-functional properties that match operational requirements and standards. Software Product Line Engineering (SPLE) has introduced several models and mechanisms for variability modeling and management. Dynamic software product lines (DSPL) engineering exploits the knowledge acquired in SPLE to develop systems that can be context-aware, post-deployment recon¯gurable, or runtime adaptive. This paper focuses on DSPL engineering approaches for developing runtime adaptive systems and proposes a framework for classifying and comparing these approaches from two distinct perspectives: adaptation properties and adaptation realization. These two perspectives are linked together by a series of guidelines that help to select a suitable adaptation realization approach based on desired adaptation types.

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“…Figure 5 shows these dimensions and some of their possible values which will be reviewed below. [51,52] or what components are di®erent between the variants of the system.…”

Section: Planningmentioning

confidence: 99%

Dynamic Software Product Line Engineering: A Reference Framework

Bashari

Bagheri

2017

Int. J. Soft. Eng. Knowl. Eng.

View full text Add to dashboard Cite

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“…However, they fall short in identifying and modeling the situations that the system may encounter during operation [67]. This results in maintaining an additional artifact as a context model (e.g., an ontology [59]) in parallel with the feature model at runtime, in order to bind the monitoring and planning phases of the MAPE-K loop [46]. IRM-SA provides an explicit support for capturing situations, pertinent to system operation and self-adaptation, via the assumption concept.…”

Section: Irm-sa Vs Dynamic Software Product Linesmentioning

confidence: 99%

Self-adaptation in software-intensive cyber–physical systems: From system goals to architecture configurations

Gerostathopoulos

Bureš

Hnětynka

et al. 2016

Journal of Systems and Software

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International audienceDesign of self-adaptive software-intensive Cyber-Physical Systems (siCPS) operating in dynamic environments is a significant challenge when a sufficient level of dependability is required. This stems partly from the fact that the concerns of self-adaptivity and dependability are to an extent contradictory. In this paper, we introduce IRM-SA (Invariant Refinement Method for Self-Adaptation) – a design method and associated formally grounded model targeting siCPS – that addresses self-adaptivity and supports dependability by providing traceability between system requirements, distinct situations in the environment, and predefined configurations of system architecture. Additionally, IRM-SA allows for architecture self-adaptation at runtime and integrates the mechanism of predictive monitoring that deals with operational uncertainty. As a proof of concept, it was implemented in DEECo, a component framework that is based on dynamic ensembles of components. Furthermore, its feasibility was evaluated in experimental settings assuming decentralized system operation

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“…It focuses on presenting tasks to the caseworker under certain circumstances. To foster run-time flexibility, Murguzur et al [27] presented a context variability modelling approach. They also presented fundamental building blocks of a framework for enabling context variability in service-based DSPLs.…”

Section: Related Workmentioning

confidence: 99%

Run-time planning of case-based business processes

Sprovieri

Díaz

Mazo

et al. 2016

2016 IEEE Tenth International Conference on Research Challenges in Information Science (RCIS)

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Abstract-Context: Organizations act in highly competitive markets, which forces them to be flexible. Constantly changing business requirements require flexible business processes. Case Management Model and Notation (CMMN) supports modeling run-time flexibility of partially structured business process models, but does not fully specify the control flow. Objective: The goal is to develop a planning algorithm that supports the case worker in planning case-based business processes at run-time. Method: We identify the requirements of run-time planning of partly structured processes by analyzing the admission process for the master degree at FHNW. To plan the process instance, we develop a planning algorithm. Our planning algorithm is evaluated using concrete cases provided by FHNW in order to demonstrate real application. Results: The planning algorithm reflects the requirements for serializing tasks at run-time. Conclusion: Our planning algorithm allows to automatically deriving context-specific execution plans for CMMN models at run-time.

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Context variability modeling for runtime configuration of service-based dynamic software product lines

Cited by 18 publications

References 28 publications

Dynamic Software Product Line Engineering: A Reference Framework

Dynamic Software Product Line Engineering: A Reference Framework

Self-adaptation in software-intensive cyber–physical systems: From system goals to architecture configurations

Run-time planning of case-based business processes

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