(Requirement) evolution requirements for adaptive systems

Souza, Teófilo Miguel de; Lapouchnian,; Mylopoulos,

doi:10.1109/seams.2012.6224402

Cited by 23 publications

(28 citation statements)

References 20 publications

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“…Souza et al introduce evolution requirements for the purposes of requirements evolution (cf. first row in Table 1 for the characteristics of this concept) [12]. Evolution requirements define how requirements can change at runtime and under which conditions.…”

Section: Background and Related Workmentioning

confidence: 99%

“…ACon uses a MAPE-K feedback loop [28] to trigger adaptation of context operationalization concerning contextual requirements to deal with runtime uncertainty. From the related approaches feedback loops are used by Souza et al to trigger the execution of evolution requirements [12]. In their work evolution requirements are predefined and therefore they do not deal with requirements uncertainty at runtime.…”

Section: Autonomic Managermentioning

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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Section: Background and Related Workmentioning

confidence: 99%

Section: Autonomic Managermentioning

confidence: 99%

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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“…Using a similar approach, we have recently published a proposal to complement AwReqs with adaptation strategies that provide compensation for failures [33]. Discussion in section 3 illustrates how AwReqs and meta-AwReqs could be used to enact the required compensation behavior, including relaxation of desired success rates.…”

Section: Related Workmentioning

confidence: 99%

“…Unlike reconfigurations discussed above, evolution requirements may change the space of alternatives available for the system. In our recent work [33], we have identified a number of adaptation strategies, including abort, retry, delegate to an external agent, relax/strengthen the requirement, etc., constructed from the basic requirements evolution operations such as initiate (a requirement instance), rollback (changes due to an attempt to achieve a requirement), etc. These adaptation strategies can be applied at the requirements instance level (thus, fixing/improving a particular system instance) and/or type level (thereby changing the behavior of all subsequent system instances).…”

Section: Adaptation Strategiesmentioning

confidence: 99%

Awareness Requirements

Souza

Lapouchnian

Robinson

et al. 2013

Software Engineering for Self-Adaptive Systems II

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Abstract. The functional specification of any software system operationalizes stakeholder requirements. In this paper we focus on a class of requirements that lead to feedback loop operationalizations. These Awareness Requirements talk about the runtime success/failure of other requirements and domain assumptions. Our proposal includes a language for expressing awareness requirements, as well as techniques for elicitation and implementation based on the EEAT requirements monitoring framework.

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“…Requirements have an important role in these adaptive systems [13] [14]. These works address adaptation that occurs largely within software systems.…”

Section: Related Workmentioning

confidence: 99%

Adapting to uncertain and evolving enterprise requirements: The case of business-driven business intelligence

Lapouchnian

Deng

2013

IEEE 7th International Conference on Research Challenges in Information Science (RCIS)

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Abstract-Information systems today are expected to function in an increasingly dynamic world with many uncertainties. System development is seldom a linear progression from welldefined, fully-specified requirements to finished products that fully meet the initial requirements. More likely, there are ongoing cycles of exploration, design and implementation, taking into account evolving needs and capabilities, as well as lessons from earlier cycles. Existing requirements modeling and analysis techniques largely presume application settings that are stable and predictable. Can these techniques be used to support analysis in the new dynamic environment? Scenarios from the recent surge in demand for business intelligence capabilities in enterprises provide an interesting setting for examining organizational and IT responses to the challenges of high uncertainty and rapid change. In this paper, we apply existing requirements modeling techniques to these scenarios in order to uncover their inadequacies, and to identify research challenges.

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(Requirement) evolution requirements for adaptive systems

Cited by 23 publications

References 20 publications

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

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

Awareness Requirements

Adapting to uncertain and evolving enterprise requirements: The case of business-driven business intelligence

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