Better call the crowd: using crowdsourcing to shape the notation of domain-specific languages

Brambilla, Marco; Cabot, Jordi; Izquierdo, Javier Luis Cánovas; Mauri, Andrea

doi:10.1145/3136014.3136033

Cited by 10 publications

(9 citation statements)

References 11 publications

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“…Concerning the highest degree obtained, the highest median score (i.e., 80) belongs to the group with a Master's degree, while the lowest median score (i.e., 52.5) can be found in the category of people educated at the Bachelor and Doctoral level. If we analyze the medians across the categories of Models read, the group with a number of models between 41 and 50 have the highest score (i.e., median = 95), while the lowest scores origin in the ranges [1][2][3][4][5][6][7][8][9][10] and 50+ (i.e., median = 50). With respect to Requirements Engineering modeling, respondents without knowledge have a significantly higher median (i.e., 85) than the group with basic notions (i.e., median = 60).…”

Section: Evaluation Resultsmentioning

confidence: 99%

“…A conceptual modeling language is composed of three components [7]: an abstract syntax, commonly referred to and specified by means of a metamodel (i.e., the specification of the abstract concepts that can be used to create valid models), a concrete syntax, commonly referred to as a notation (i.e., the specification of the graphical and/or textual representations of the metamodel elements), and semantics (i.e., the specification of the meaning attached to the abstract syntax elements).…”

Section: Domain-specific Conceptual Modelingmentioning

confidence: 99%

“…[13] -Similar to [8,18], participants first draw samples which are then analyzed for identifying Stereotypes and Prototypes. Other participants then interpreted the prototypes [29] Existing notation Participants are shown sample models and asked to respond to comprehension questions [43] Existing notation Participants are shown sample models and asked to respond to comprehension questions [7] Existing notation…”

Section: Related Workmentioning

confidence: 99%

“…An interesting approach to improve the notation of DSMLs was proposed in [7]. The approach involves the crowd in the language design process.…”

Section: Related Workmentioning

confidence: 99%

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A technique for evaluating and improving the semantic transparency of modeling language notations

Bork

Roelens

2021

Softw Syst Model

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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 their semantic transparency, indicating the extent to which a notation intuitively suggests its meaning to untrained users. Method engineers should thus aim at semantic transparency for realizing intuitively understandable notations. However, notation design is often treated poorly—if at all—in method engineering methodologies. This paper proposes a technique that, based on iterative evaluation and improvement tasks, steers the notation toward semantic transparency. The approach can be efficiently applied to arbitrary modeling languages and allows easy integration into existing modeling language engineering methodologies. We show the feasibility of the technique by reporting on two cycles of Action Design Research including the evaluation and improvement of the semantic transparency of the Process-Goal Alignment modeling language notation. An empirical evaluation comparing the new notation against the initial one shows the effectiveness of the technique.

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Section: Evaluation Resultsmentioning

confidence: 99%

Section: Domain-specific Conceptual Modelingmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…An interesting approach to improve the notation of DSMLs was proposed in [7]. The approach involves the crowd in the language design process.…”

Section: Related Workmentioning

confidence: 99%

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A technique for evaluating and improving the semantic transparency of modeling language notations

Bork

Roelens

2021

Softw Syst Model

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“…Note that specification using chatbots differs from crowdsourcing the specification elements as in [19]. Presumably, while the chatbots are likely to be made available to a close-knit community of people already aware of the specification language, crowdsourcing, by definition, involves anyone willing to contribute.…”

Section: Collaborative Model Specification Using Chatbotsmentioning

confidence: 99%

AI-driven streamlined modeling: experiences and lessons learned from multiple domains

et al. 2022

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Model-driven technologies (MD*), considered beneficial through abstraction and automation, have not enjoyed widespread adoption in the industry. In keeping with the recent trends, using AI techniques might help the benefits of MD* outweigh their costs. Although the modeling community has started using AI techniques, it is, in our opinion, quite limited and requires a change in perspective. We provide such a perspective through five industrial case studies where we use AI techniques in different modeling activities. We discuss our experiences and lessons learned, in some cases evolving purely modeling solutions with AI techniques, and in others considering the AI aids from the beginning. We believe that these case studies can help the researchers and practitioners make sense of various artifacts and data available to them and use applicable AI techniques to enhance suitable modeling activities.

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Composing an Initial Domain-Specific Modeling Language Notation by Reusing Icons

Roelens,

Ebben,

de Wetering

2023

Lecture Notes in Business Information Processing

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Better call the crowd: using crowdsourcing to shape the notation of domain-specific languages

Cited by 10 publications

References 11 publications

A technique for evaluating and improving the semantic transparency of modeling language notations

A technique for evaluating and improving the semantic transparency of modeling language notations

AI-driven streamlined modeling: experiences and lessons learned from multiple domains

Composing an Initial Domain-Specific Modeling Language Notation by Reusing Icons

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