Evaluating a formal modelling language

Ruiz, Fidel; Harmelen, Frank van; Aben, Manfred; Plassche, J. van de

doi:10.1007/3-540-58487-0_2

Cited by 5 publications

(3 citation statements)

References 5 publications

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“…Hard theory also often uses formal language, which is explicit, precise and specific (mathematics or logic). According to Ruiz et al (1994), formal modelling languages reduce the vagueness and ambiguity of informal language descriptions. They also allow for validation of completeness and consistency through “proofs” and bridge the gap between the informal model and system design.…”

Section: Introductionmentioning

confidence: 99%

The complexity continuum, Part 1: hard and soft theories

Yolles

2019

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Purpose Complex systems adapt to survive, but little comparative literature exists on various approaches. Adaptive complex systems are generic, this referring to propositions concerning their bounded instability, adaptability and viability. Two classes of adaptive complex system theories exist: hard and soft. Hard complexity theories include Complex Adaptive Systems (CAS) and Viability Theory, and softer theories, which we refer to as Viable Systems Theories (VSTs), that include Management Cybernetics at one extreme and Humanism at the other. This paper has a dual purpose distributed across two parts. In Part 1, the purpose of this paper is to identify the conditions for the complementarity of the two classes of theory. In Part 2, the purpose is to explore (in part using Agency Theory) the two classes of theory and their proposed complexity continuum. Design/methodology/approach A detailed analysis of the literature permits a distinction between hard and softer approaches towards modelling complex social systems. Hard theories are human-incommensurable, while soft ones are human-commensurable, therefore more closely related to the human condition. The characteristics that differentiate between hard and soft approaches are identified. Findings Hard theories are more restrictive than the softer theories. The latter can embrace degrees of “softness” and it is explained how hard and soft approaches can be mixed, sometimes creating Harmony. Originality/value There are very few explorations of the relationship between hard and soft approaches to complexity theory, and even fewer that draw in the notion of harmony.

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Section: Introductionmentioning

confidence: 99%

The complexity continuum, Part 1: hard and soft theories

Yolles

2019

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“…After applying both modeling techniques to the case study, they have been compared at the light of a set of criteria. As can be seen in Table 1, the criteria are based on three intakes: generic modeling criteria based on existing literature [24,25]; blockchain-specific criteria defined in consultation with blockchain expert (I2), and other criteria based on findings from applying both modeling techniques. Comparing both techniques is useful to determine the pros and cons but also the complementarity of each technique.…”

Section: Research Paradigm Question and Methodologymentioning

confidence: 99%

Using i* and UML for Blockchain Oriented Software Engineering: Strengths, Weaknesses, Lacks and Complementarity

Vingerhoets

Heng

Wautelet

2021

J. Complex Syst. Inform. Model. Q.

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New blockchain-based projects do appear every day. The technology has indeed been popularized by cryptocurrencies but is now gaining interest in various domains and new types of applications are evaluated constantly. Understanding the impact of blockchain adoption on the organization and the internals of blockchain-related behavior nevertheless remains a challenge for managers but also for IT professionals. This article studies how two existing organizational and software modeling languages can be fit to document a blockchain development project in Supply Chain Management (SCM) at its earliest stages. These two frameworks are i* on the one side and the Unified Modeling Language (UML) use case and sequence diagrams on the other side. The real life project used as a case study in this application is 'Farm-to-Fork' where a blockchain solution for the Supply Chain (SC) of farm animals is developed. The application of the frameworks is intended to identify their strengths and weaknesses. An extension of i* is proposed to deal with blockchain privacy issues as well as laws and norms. We finally point to the complementarity of i* and UML use case and sequence diagrams in a Blockchain-Oriented Software Engineering (BOSE) context. The i* framework indeed supports early requirements to understand the impact of the project on stakeholders while UML use case and sequence diagrams support the late requirements and the design by depicting the use of blockchain and some of its behavioral mechanisms.

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“…The second set of questions in this part of the evaluation investigated six evaluation criteria. These six criteria are expressiveness, frequency of errors, redundancy, locality of change, reusability, and guidelines [49]. In a modelling language, expressiveness refers to both the possibility and the ease of expressing real-world concepts and to effectively conveying the meaning of that concept.…”

Section: General Evaluation Of Transplan Modelling Languagementioning

confidence: 99%

Engineering transparency requirements: A modelling and analysis framework

Hosseini

Shahri

Phalp

et al. 2018

Information Systems

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Transparency is a requirement that denotes the communication of information that should help audience to take informed decisions. The existing research on transparency in information systems usually focuses on the party who provides transparency and its interrelation with other requirements such as privacy, security and regulatory requirements. Engineering transparency, however, also requires the analysis of the information receivers' situation and their transparency requirements and the medium used to communicate and present the information. A holistic consideration of transparency will enhance its management and increase its usefulness. In this paper, we provide a novel engineering framework, consisting of a modelling language and nine analytical reasonings, which is meant to represent transparency requirements and detect a set of possible side-effects. Examples of such detections include detecting information overload, information starvation, and transparency leading to biased decisions. We then evaluate the modelling language through a case study and report the results.

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Evaluating a formal modelling language

Cited by 5 publications

References 5 publications

The complexity continuum, Part 1: hard and soft theories

The complexity continuum, Part 1: hard and soft theories

Using i* and UML for Blockchain Oriented Software Engineering: Strengths, Weaknesses, Lacks and Complementarity

Engineering transparency requirements: A modelling and analysis framework

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