Automatically RELAXing a Goal Model to Cope with Uncertainty

Ramirez, Andres J.; Fredericks, Erik M.; Jensen, Adam C.; Cheng, Betty H. C.

doi:10.1007/978-3-642-33119-0_15

Cited by 19 publications

(8 citation statements)

References 22 publications

(42 reference statements)

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“…Such systems must be able to adapt their behavior in face of a series of possible unexpected situations. The paper by Ramirez et al (2012) proposes a search based approach to automatically A c c e p t e d M a n u s c r i p t define the RELAXation of the adaptation rules of a DAS. Adaptation rules RELAXing consists in defining fuzzy rules that specify the extent to which a goal can become temporarily unsatisfied and yet deliver acceptable behavior.…”

Section: Related Workmentioning

confidence: 99%

A robust optimization approach to the next release problem in the presence of uncertainties

Paixao

Souza

2015

Journal of Systems and Software

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Section: Related Workmentioning

confidence: 99%

A robust optimization approach to the next release problem in the presence of uncertainties

Paixao

Souza

2015

Journal of Systems and Software

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“…Several methods have been proposed to exploit the capabilities of RELAX. We mention AutoRELAX [13], an automated approach that relaxes goal models that contain RELAX expressions with the objective of satisfying their functional requirements while reducing the number of necessary adaptations.…”

Section: Background and Related Workmentioning

confidence: 99%

“…They do not deal with requirements uncertainty at runtime. Related work on uncertainty proposes to use goal relaxation in the sense of uncertainty-aware requirements [13] as well as adaptive requirements that capture points of adaptation [15]. ACon uses a MAPE-K feedback loop [28] to trigger adaptation of context operationalization concerning contextual requirements to deal with runtime uncertainty.…”

Section: Autonomic Managermentioning

confidence: 99%

“…Endusers can actively influence the adaptation to satisfy their needs better [41,42,43]. Last, integration of ACon with RELAX-based approaches to uncertainty mitigation is worth exploring [13]. In particular, we could consider applying the principle of relaxing goals for those situation where the data mining techniques proposed in ACon are not able to discover a clear correlation between context conditions and contextual requirement satisfaction.…”

Section: Connection Of Acon To Other Research Approachesmentioning

confidence: 99%

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

Knauss

Damian

Franch

et al. 2016

Information and Software Technology

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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 a↵ecting 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 a↵ected by runtime uncertainty, ACon analyzes 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 analyzed the sensor data collected onboard from 46 sensors and 90,748 measurements per sensor. Conclusion: ACon is an important step in dealing with uncertainty a↵ecting contextual requirements at runtime while considering end-user interaction. ACon supports systems in analyzing 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.

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“…AutoRELAX [37], is an approach that generates Relax-ed goal models that address environmental uncertainty by identifying which goals to Relax, which Relax operators to apply, and the shape of the fuzzy logic function that defines the goal satisfaction criteria.…”

Section: Requirements Engineering For Self-adaptive Systemsmentioning

confidence: 99%

Modeling and verification of Functional and Non-Functional Requirements of ambient Self-Adaptive Systems

Ahmad

Belloir

Bruel

2015

Journal of Systems and Software

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Self-Adaptive Systems modify their behavior at run-time in response to changing environmental conditions. For these systems, Non-Functional Requirements play an important role, and one has to identify as early as possible the requirements that are adaptable. We propose an integrated approach for modeling and verifying the requirements of Self-Adaptive Systems using Model Driven Engineering techniques. For this, we use Relax, which is a Requirements Engineering language which introduces flexibility in Non-Functional Requirements. We then use the concepts of Goal-Oriented Requirements Engineering for eliciting and modeling the requirements of Self-Adaptive Systems. For properties verification, we use OMEGA2/IFx profile and toolset. We illustrate our proposed approach by applying it on an academic case study.

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Automatically RELAXing a Goal Model to Cope with Uncertainty

Cited by 19 publications

References 22 publications

A robust optimization approach to the next release problem in the presence of uncertainties

A robust optimization approach to the next release problem in the presence of uncertainties

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

Modeling and verification of Functional and Non-Functional Requirements of ambient Self-Adaptive Systems

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