Teaching Modelling Literacy: An Artificial Intelligence Approach

Saini, Rijul; Mussbacher, Gunter; Guo, Jin; Kienzle, Joerg

doi:10.1109/models-c.2019.00108

Cited by 14 publications

(8 citation statements)

References 7 publications

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“…MAGNET [16] guides users on the next tutorials to speed up the learning curve of a modelling tool. Finally, Mod-Bud [116] is an envisioned framework to build assistants that educate novice modellers on abstraction. Such assistants may provide recommendations on a constructed model by comparison with a prescriptive model devised by the assistant.…”

Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

“…Fewer approaches provide recommendations proactively without user intervention (12 out of 51, a 23.53%), typically by monitoring the user editing actions to update the recommendations in return. Only a few tools (3 of them, a 5.88%) can trigger the recommendations both on demand and proactively: the recommender of domain model elements DoMoRe [5,6], the envisioned modelling learning environment ModBud [116] and the generic RS framework Hermes [34][35][36]. Finally, Savary-Leblanc [121] does not give enough details on how to access the recommendations, so we mark it as unknown in the table.…”

Section: Maturitymentioning

confidence: 99%

“…In some cases, users correspond to developers for which personalised recommendations are generated, and in other cases, users (and items) are mapped to elements within the artefacts that are target of recommendations. MAGNET [16], PARMOREL [11,53] and ModBud [116] [56].…”

Section: Content-based These Approaches Use Different Contentmentioning

confidence: 99%

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Recommender systems in model-driven engineering

et al. 2021

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Recommender systems are information filtering systems used in many online applications like music and video broadcasting and e-commerce platforms. They are also increasingly being applied to facilitate software engineering activities. Following this trend, we are witnessing a growing research interest on recommendation approaches that assist with modelling tasks and model-based development processes. In this paper, we report on a systematic mapping review (based on the analysis of 66 papers) that classifies the existing research work on recommender systems for model-driven engineering (MDE). This study aims to serve as a guide for tool builders and researchers in understanding the MDE tasks that might be subject to recommendations, the applicable recommendation techniques and evaluation methods, and the open challenges and opportunities in this field of research.

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Section: Complete Most Approaches Whose Purpose Is Completingmentioning

confidence: 99%

Section: Maturitymentioning

confidence: 99%

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Recommender systems in model-driven engineering

et al. 2021

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“…Next, we consider each relationship in and check if it is present in the Descriptive Model Cluster and if the probability score of relationship is more than decision threshold for this relationship, i.e., and add it to (L. [11][12][13]. We do the same for the predictive model and add it to (L. [14][15][16]. The result is an cluster for the ℎ concept with relationships that have probability scores greater than their respective decision thresholds.…”

Section: Domobot Architecturementioning

confidence: 99%

“…Following our research goal, we introduce DoMoBOT (a domain modelling bot) in this paper. Its architecure builds upon the ModBud framework proposed in our previous work [16] and follows along our previous works [14,15] where we leverage the capabilities of Natural Language Processing (NLP) and Machine Learning (ML) techniques for extracting domain models with high accuracy. Furthermore, we use ML techniques [14] to learn the context information of domain concepts and predict their associated relationships or modelling patterns, e.g., association relationship and Player-Role pattern.…”

Section: Introductionmentioning

confidence: 99%

DoMoBOT

Saini

Mussbacher

Guo

et al. 2020

Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

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Domain modelling transforms domain problem descriptions written in natural language (NL) into analyzable and concise domain models (class diagrams) during requirements analysis or the early stages of design in software development. Since the practice of domain modelling requires time in addition to modelling skills and experience, several approaches have been proposed to automate or semi-automate the construction of domain models from problem descriptions expressed in NL. Despite the existing work on domain model extraction, some significant challenges remain unaddressed: (i) the extracted domain models are not accurate enough to be used directly or with minor modifications in software development, (ii) existing approaches do not facilitate the tracing of the rationale behind the modelling decisions taken by the model extractor, and (iii) existing approaches do not provide interactive interfaces to update the extracted domain models. Therefore, in this paper, we introduce a domain modelling bot called DoMoBOT, explain its architecture, and implement it in the form of a web-based prototype tool. The bot automatically extracts a domain model from a problem description written in NL with an accuracy higher than existing approaches. Furthermore, the bot enables modellers to update a part of the extracted domain model and in response the bot re-configures the other parts of the domain model pro-actively. To improve the accuracy of extracted domain models, we combine the techniques of Natural Language Processing and Machine Learning. Finally, we evaluate the accuracy of the extracted domain models. CCS CONCEPTS • Software and its engineering → Designing software; • Humancentered computing → Natural language interfaces.

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