The Invariant Refinement Method

Bureš, Tomáš; Gerostathopoulos, Ilias; Hnětynka, Petr; Keznikl, Jaroslav; Kit, Michał; Plášil, František

doi:10.1007/978-3-319-16310-9_12

Cited by 11 publications

(8 citation statements)

References 19 publications

(23 reference statements)

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“…The ASCENS Project approaches the requirements problem from a few angles. In addition to ARE, ASCENS also considers a design method called Invariant Re-finement Method (IRM) (see Chapter III.4 [3]) and a generic approach called SOTA (States Of The Affairs) [1] (see Chapter III.1 [9]). IRM relies on goal-based requirements and refines system goals down to the responsibilities of individual components, component processes, and ensembles.…”

Section: Related Workmentioning

confidence: 99%

Engineering Requirements for Autonomy Features

Vassev

Hinchey

2015

Software Engineering for Collective Autonomic Systems

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Abstract. This chapter outlines an approach to Autonomy Requirements Engineering (ARE). ARE targets the integration and promotion of autonomy in software-intensive systems by providing a mechanism and methodology for elicitation and expression of autonomy requirements. ARE relies on goal-oriented requirements engineering to elicit and define system goals, and uses the generic autonomy requirements model to derive and define assistive and, eventually, alternative objectives. The system may pursue these "self-* objectives" in the presence of factors threatening the achievement of the initial system goals. Once identified, the autonomy requirements are specified with the KnowLang language. To demonstrate the ARE's ability to handle autonomy requirements for autonomic ensembles, the ARE's application to the ASCENS Science Clouds case study is presented and discussed in detail.

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Section: Related Workmentioning

confidence: 99%

Engineering Requirements for Autonomy Features

Vassev

Hinchey

2015

Software Engineering for Collective Autonomic Systems

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“…Architecture design departs from a conceptual- ization of the EVTP. It then uses the state-of-the-affairs (SOTA) approach, as discussed in Chapter III.1 [14], and the invariant refinement method (IRM), as discussed in Chapter III.4 [6], to infer both functional and non-functional system requirements. Components and ensembles are derived and a set of feedback loops connecting them.…”

Section: Software Development Life Cyclementioning

confidence: 99%

The E-mobility Case Study

Hoch

Bensler

Abeywickrama

et al. 2015

Software Engineering for Collective Autonomic Systems

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Abstract. Electro-mobility (e-mobility) is one of the promising technologies being considered by automotive OEMs as an alternative to internal combustion engines as a means of propulsion. The e-mobility case study provides a novel example of a relevant industry application within the ASCENS framework. An overview of the system design is given which describes how e-mobility is conceptualized and then transformed using the ensemble development life cycle (EDLC) approach into a distributed autonomic (i.e self-aware, self-adaptive) component-based software system. The system requirements engineering is based on the state-of-theaffairs (SOTA) approach and the invariant refinement method (IRM) which are both revisited and applied. Regarding the implementation and deployment of the system, a dependable emergent ensembles of components (DEECo) approach is utilized. The DEECo components and ensembles are coded and deployed using the Java-based jDEECo runtime environment. The runtime environment integrates the multi-agent transport simulation tool (MATSim), which is used to predict the effects of the physical interactions of users, vehicles and infrastructure resources. jDEECo handles multiple MATSim instances to allow for different belief states between components and ensembles.

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“…Every e-car has to arrive to its place of interest (POI) and park within a radius of 100 meters. [2] In order to do that, every car needs to :…”

Section: Requirementsmentioning

confidence: 99%

“…One of the key IRM-SA ideas is that each invariant (with the exception of assumption ones) represents a computation that produces output knowledge given a particular input knowledge such that the invariant is satisfied (as stated in [2]).…”

Section: Knowledge Flow Recognitionmentioning

confidence: 99%

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Towards an Automated Requirements-driven Development of Smart Cyber-Physical Systems

Vinárek

Hnětynka

2016

Electron. Proc. Theor. Comput. Sci.

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The Invariant Refinement Method for Self Adaptation (IRM-SA) is a design method targeting development of smart Cyber-Physical Systems (sCPS). It allows for a systematic translation of the system requirements into the system architecture expressed as an ensemble-based component system (EBCS). However, since the requirements are captured using natural language, there exists the danger of their misinterpretation due to natural language requirements' ambiguity, which could eventually lead to design errors. Thus, automation and validation of the design process is desirable. In this paper, we (i) analyze the translation process of natural language requirements into the IRM-SA model, (ii) identify individual steps that can be automated and/or validated using natural language processing techniques, and (iii) propose suitable methods.

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The Invariant Refinement Method

Cited by 11 publications

References 19 publications

Engineering Requirements for Autonomy Features

Engineering Requirements for Autonomy Features

The E-mobility Case Study

Towards an Automated Requirements-driven Development of Smart Cyber-Physical Systems

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