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![Fig. 2 Semantic transparency spectrum (cf., [8, p. 115], [44, p. 765])](publication/352411961/figure/fig1/AS:1035011402768386@1623777356615/Semantic-transparency-spectrum-cf-8-p-115-44-p-765_Q320.jpg)
![Fig. 3 Revised notation for four BCM concepts cycles [60, p. 41]....](publication/352411961/figure/fig2/AS:1035011402780673@1623777356700/Revised-notation-for-four-BCM-concepts-cycles-60-p-41-Considering-the-existing-body_Q320.jpg)
The notation of a modeling language is of paramount importance for its efficient use and the correct comprehension of created models. A graphical notation, especially for domain-specific modeling languages, should therefore be aligned to the knowledge, beliefs, and expectations of the targeted model users. One quality attributed to notations is the...
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... extent to which a visual notation exploits semantic transparency can be interpreted as a spectrum (see Fig. 2). In the worst case, a notation whose appearance suggests an incorrect meaning is considered semantically perverse (false mnemonic). The neutral case refers to notations having an arbitrary relationship between their appearance and meaning (i.e., semantically opaque (conventional)). In the best case, a notation's appearance suggests ...
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... However, development of empirical (e.g., experimental) constructs and procedures, such as the ones we attempt in this paper, is important both for systematically and reliably measuring such quality aspects and for attaining a shared understanding of what such quality dimensions really mean. Work with a similar emphasis on the measurement aspect, but with a stronger focus on the organization of the overall empirical procedure has been reported by Bork et al. [37,38] In the area of empirical conceptual modeling, substantial effort has been dedicated towards assessing model understandability of various notations. An extensive survey of relevant studies as a means to an introduction to the problematic of measuring understandability is offered by Houy et al. [39]. ...
Conceptual modeling plays a central role in planning, designing, developing and maintaining software-intensive systems. One of the goals of conceptual modeling is to enable clear communication among stakeholders involved in said activities. To achieve effective communication, conceptual models must be understood by different people in the same way. To support such shared understanding, conceptual modeling languages are defined, which introduce rules and constraints on how individual models can be built and how they are to be understood. A key component of a modeling language is an ontology, i.e., a set of concepts that modelers must use to describe world phenomena. Once the concepts are chosen, a visual and/or textual vocabulary is adopted for representing the concepts. However, the choices both of the concepts and of the vocabulary used to represent them may affect the quality of the language under consideration: some choices may promote shared understanding better than other choices. To allow evaluation and comparison of alternative choices, we present Peira, a framework for empirically measuring the domain and comprehensibility appropriateness of conceptual modeling language ontologies. Given a language ontology to be evaluated, the framework is based on observing how prospective language users classify domain content under the concepts put forth by said ontology. A set of metrics is then used to analyze the observations and identify and characterize possible issues that the choice of concepts or the way they are represented may have. The metrics are abstract in that they can be operationalized into concrete implementations tailored to specific data collection instruments or study objectives. We evaluate the framework by applying it to compare an existing language against an artificial one that is manufactured to exhibit specific issues. We then test if the metrics indeed detect these issues. We find that the framework does offer the expected indications, but that it also requires good understanding of the metrics prior to committing to interpretations of the observations.
... A DSML is composed of (i) an abstract syntax, which is defined using a meta-model that describes the fundamental concepts to create correct models, (ii) a concrete syntax, which is a notation that describes the graphical or textual representations of the elements in the meta-model, and (iii) semantics, which specifies the meaning associated with the elements in the abstract syntax [3]. An intuitive DSML notation is important for an efficient use and comprehension of the created models by users [1]. In this respect, intuitiveness refers to semantic transparency, which is a design principle that can be used to design visual notations optimized for communication and problem solving [11]. ...
... Previous research proposed an iterative technique for evaluating and improving the semantic transparency of existing DSML notations [1]. The first phase evaluates the existing notation and the second phase aims at improving it. ...
... The design of PGA notation version 1.0 [13] (see left column in Table 2) was implemented by visual icons to adhere to the Semantic Transparency principle [11]. In follow-up research [1], PGA notation version 1.0 was evaluated by applying a technique that includes iterative evaluation and improvement tasks to steer the DSML notation toward semantic transparency. The notation revision had resulted in the redesign of four PGA entities (see Competence, Value Proposition, Internal Goal, and Customer Goal of version 2.0 in Table 2). ...
Domain-Specific Modeling Languages (DSMLs) increasingly gain attention in the field of Conceptual Modeling as they target a particular problem in a delineated application domain by using concepts that are tailored to end-users. As such, DSMLs provide a flexible approach that can be used in different application domains. To realize their potential, the graphical notation of a DSML must be aligned with the users’ knowledge and perceptions. In particular, an intuitively understandable DSML notation is important to facilitate communication with the end-users. Therefore, we propose a technique for composing an initial DSML notation by reusing icons. This technique is applied in the context of the Process-Goal Alignment (PGA) business modeling technique. Furthermore, we empirically evaluate the proposed technique by an experiment with 85 business users, in which we compare the intuitiveness of the newly developed and existing PGA notations. The results show that the semantic transparency of the newly developed notation is significantly higher than the existing PGA notations. More specifically, the composition technique based on the reuse of icons enables to derive the meaning of DSML concepts correctly.KeywordsDomain-Specific Modeling Languagenotationiconexperimental evaluation
... However, they found only a few advises for testing (Ratiu et al., 2018), and reported ones were more focused on testing the language implementation and associated tooling (editors, code generators, etc.). Bork and Roelens (2021) discuss the definition of visual modelling language notations. They recommend a technique to evaluate and improve the semantic transparency of a notation. ...
Modelling languages play a central role in developing complex, critical systems. A precise, comprehensible, and high-quality modelling language specification is essential to all stakeholders using, implementing, or extending the language. Many good practices can be found that improve the understandability or consistency of the languages’ semantics. However, designing a modelling language intended for a large audience is still challenging. In this paper, we investigate the challenges and typical issues with assessing the specifications of behavioural modelling language semantics. Our key insight is that the various stakeholder’s understandings of the language’s semantics are often misaligned, and the semantics defined in various artefacts (simulators, test suites) are inconsistent. Therefore assessment of semantics should focus on identifying and resolving these inconsistencies. To illustrate these challenges and techniques, we assessed parts of a state-of-the-art specification for a general-purpose modelling language, the Precise Semantics of UML State Machines (PSSM). We reviewed the text of the specification, analysed and executed PSSM’s conformance test suite, and categorised our experiences according to questions generally relevant to modelling languages. Finally, we made recommendations for improving the development of future modelling languages by representing the semantic domain and traces more explicitly, applying diverse test design techniques to obtain conformance test suites, and using various tools to support early-phase language design.
... Table 1. Please note that the approaches we have identified overlap mostly with the ones recently identified in [17]. While ana-lyzing identified approaches we focused on answering the following questions: (1) What are the main aims (scopes) as well as the applicability (e.g., domain-specificity) of the proposed method? ...
... Concerning assessment of the (draft) visual notation, a well-known and often used, cf. e.g., [127], means for validating a visual notation are the guidelines proposed in the Physics of Notation (PoN) [98], and its various adaptations [17,21,116]. For example, both [17,21] propose an approach which actively involves (prospective) model users in assessing and improving PoN's semantic transparency, which as a theoretic principle assesses the extent to which the visualization of a concept suggests its meaning. ...
... e.g., [127], means for validating a visual notation are the guidelines proposed in the Physics of Notation (PoN) [98], and its various adaptations [17,21,116]. For example, both [17,21] propose an approach which actively involves (prospective) model users in assessing and improving PoN's semantic transparency, which as a theoretic principle assesses the extent to which the visualization of a concept suggests its meaning. ...
Enterprise models have the potential to constitute a valuable asset for organizations, e.g., in terms of enabling a variety of analyses or by fostering cross-organizational communication. Therefore, while designing an enterprise modeling method one needs to ensure that created enterprise models are of good quality in terms of: (1) syntactic validity, which entails that a model adheres to syntactic rules encoded in the underlying modeling language, (2) semantic validity, i.e., that the model should make sense in its context of use, and (3) pragmatic validity, i.e., that the model should effectively and efficiently serve the intended purpose. To ensure these three validity types, verification and validation (V &V) techniques need to be exploited while designing the enterprise modeling method, e.g., to check created enterprise models against syntactic rules, or to ensure intra- and inter-model consistency. This paper targets the systematic embedding of V &V techniques into the engineering of (enterprise) domain-specific modeling methods (DSMMs). Specifically, after identifying and analyzing existing DSMM engineering approaches, we synthesize their elements (such as typical phases and steps) and enrich them with V &V techniques. This paper is an extension of our previous work and additionally contributes (1) a systematic analysis of a wider set of existing approaches to DSMM engineering, (2) an extended background that covers information on models, modeling languages and modeling methods, (3) additional details regarding selected validation and verification techniques for each phase, and finally (4) a road-map encompassing desiderata for further advances in V &V in domain-specific modeling method engineering, from the perspectives of practice, research and education.
... However, the "generality" evaluation criteria and the entire dimension of "evolution" are not covered at all. Next, Bork and Roelens [22] introduced a technique for evaluating and improving the semantic transparency of modelling notations. They suggest to split this evaluation process into two phases (evaluating the initial notation and revision) including gathering participant's feedback on the intuitiveness of the notation. ...
The new ITIL 4 standard was introduced in 2020. Still, ITIL standardization highly influences how IT-Service Management (ITSM) is seen and performed in practice. Hence, this new standard may have a similarly high impact. The major focus on StakeholderValue (SV) is a key element of ITIL 4 for analysing IT-Services. Generally, stakeholder orientation is a current trend in business analysis. Therefore, our itsVALUE method and modeller provide means to model and analyze value delivery in IT-Services. In an ITIL 4 context, both can also be used for the design of services. itsVALUE combines “traditional” approaches to Value Stream (VS) analysis and service modelling. Further, it adds concepts and functionalities that meet the requirements of ITSM and ITIL 4. Still, itsVALUE must be checked in practice regarding its method, notation and ADOxx modeller. To investigate these issues, this work deeply describes and discusses a first case study we carried out regardingly. Overall, the participants involved perceived the itsVALUE method as suitable to support value-oriented ITSM. However, there were slight differences in their perception of the itsVALUE notation. Lastly, they perceived the itsVALUE ADOxx modeller as a powerful and supportive tool.KeywordsITIL 4Service modellingService valueValue stream modellingStakeholder valueService blueprintingIT-Service ManagementCase studyMethod evaluationNotation evaluationADOxx modellerPrototype evaluation
... They demonstrated how the framework can be used for evaluating a modeling language. Bork et al. [55] presented a technique that modeling language designers can use to evaluate and improve the semantic transparency of their language. They illustrated the technique by applying it to the process-goal alignment modeling language notation and analyzing the results. ...
The Object Management Group introduced the Model-Driven Architecture in 2001. Since then, the research community has embraced model-driven engineering (MDE), but to a lesser extent than practitioners had hoped. A good awareness of practitioners’ challenges, particularly with modeling, is required to ensure the relevance of a research agenda. Therefore, this study conducts a meta-review on the state of practice in using modeling languages for software engineering over the last five years using Kitchenham’s guidelines. This study serves as an orientation within the research field and a basis for further research. It contributes to the literature by focusing on publications discussing the practical use of modeling languages and the benefits and problems perceived by practitioners. The main finding of this review is that practitioners benefit from MDE in the following ways: it is beneficial for several stakeholders; it saves cost; it is easy to use; it improves productivity, quality, and understanding of the system; and it provides support for software development activities. However, practitioners continue to face several serious challenges. The most frequently reported issues are the missing tool functionalities. Many studies have found that adhering to the Physics of Notation principles would improve modeling languages. Other findings include that modeling is mostly used for documentation and requirements elicitation, and UML is the most often used.
... However, they found only a few advises for testing (Ratiu et al, 2018), and reported ones were more focused on testing the language implementation and associated tooling (editors, code generators, etc.). Bork and Roelens (2021) discuss the definition of visual modelling language notations. They recommend a technique to evaluate and improve the semantic transparency of a notation. ...
Modelling languages play a central role in developing complex, critical systems. Having a precise, comprehensible, and high-quality modelling language specification is essential to all the different stakeholders using, implementing, or extending the language. Many good practices can be found that improve the understandability or consistency of the languages' semantics. However, designing a modelling language intended for a large audience is still challenging. In this paper, we investigate how to assess the specification of modelling language semantics: (i) how it helps professionals to understand the language (ii) whether it contains errors, contradictions, ambiguities, and (iii) how suitable it is to assess the correctness of the related modelling tools. We identified the various stakeholders' viewpoints on the language and argued why understanding the semantics is crucial for all of them, although on different levels that can be supported by different artefacts. Next, we assessed a state-of-the-art specification for a general-purpose modelling language, the Precise Semantics of UML State Machines (PSSM). We reviewed the text of the specification, analysed and executed PSSM's conformance test suite, and categorized our experiences according to questions that are generally relevant for modelling languages. Finally, we made recommendations for improving the development of future modelling languages by representing the semantic domain and traces more explicitly, applying diverse test design techniques to obtain conformance test suites, and using various tools to support early-phase language design.
As stated in the previous chapter’s conclusion, two-dimensional (2D) metamodeling platforms such as ADOxx (see Sect. 5.2.2 ) are not suitable for modeling three-dimensional augmented reality (AR) applications, such as modeling with the newly introduced Augmented Reality Workflow Modeling Method (ARWFMM). However, not only for visual modeling of AR scenarios, such 2D metamodeling platforms come to their limits. Taking again into account the derived use cases for extended reality applications combined with conceptual modeling in Chap. 3 , it becomes clear that the traditional 2D approach is not sufficient here either.
This chapter evaluates three different aspects of the M2AR platform. First, it includes a comparative evaluation of the global generic- and specific requirements (see Chaps. 3 and 4 ), against the first implementation of the metamodeling platform M2AR (see Chaps. 6 and 7 ), and against the first implementations of the Augmented Reality Workflow Modeling Method (ARWFMM) language implemented on M2AR . This is followed by a demonstration of M2AR and its ARWFMM implementation, and third, an empirical evaluation of the comprehensibility of the ARWFMM and its language concepts.
