Domain-Specific Modeling Environment for Developing Domain Specific Modeling Languages as Lightweight General Purpose Modeling Language Extensions

This chapter introduces the concepts and techniques required for developing graphical, domain-specific modeling and program synthesis environments. It argues that a fully functional modeling environment can be quickly developed for a wide variety of engineering domains using a configurable and extensible toolset with a limited set of generic concepts. The configuration is accomplished through metamodels specifying the modeling language and methodology containing all syntactic, semantic and presentation information of the domain. The authors applied this approach to several real-world systems.

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Quantitative Productivity Analysis of a Domain-Specific Modeling Language

Handbook of Research on Innovations in Systems and Software Engineering - Advances in Systems Analysis, Software Engineering, and High Performance Computing ◽

10.4018/978-1-4666-6359-6.ch013 ◽

2015 ◽

pp. 313-344

Author(s):

Joe Hoffert ◽

Douglas C. Schmidt ◽

Aniruddha Gokhale

Keyword(s):

Ease Of Use ◽

Alternative Methods ◽

Modeling Language ◽

Modeling Languages ◽

Productivity Analysis ◽

Model Driven ◽

Domain Specific ◽

Productivity Gain ◽

Domain Specific Modeling ◽

Specific Modeling

Model-Driven Engineering (MDE), in general, and Domain-Specific Modeling Languages (DSMLs), in particular, are increasingly used to manage the complexity of developing applications in various domains. Although many DSML benefits are qualitative (e.g., ease of use, familiarity of domain concepts), there is a need to quantitatively demonstrate the benefits of DSMLs (e.g., quantify when DSMLs provide savings in development time) to simplify comparison and evaluation. This chapter describes how the authors conducted quantitative productivity analysis for a DSML (i.e., the Distributed Quality-of-Service [QoS] Modeling Language [DQML]). The analysis shows (1) the significant quantitative productivity gain achieved when using a DSML to develop configuration models compared with not using a DSML, (2) the significant quantitative productivity gain achieved when using a DSML interpreter to automatically generate implementation artifacts as compared to alternative methods when configuring application entities, and (3) the viability of quantitative productivity metrics for DSMLs.

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Methodology to develop domain specific modeling languages

Proceedings of the 17th ACM SIGPLAN International Workshop on Domain-Specific Modeling - DSM 2019 ◽

10.1145/3358501.3361235 ◽

2019 ◽

Author(s):

Subhrojyoti Roy Chaudhuri ◽

Swaminathan Natarajan ◽

Amar Banerjee ◽

Venkatesh Choppella

Keyword(s):

Modeling Languages ◽

Domain Specific ◽

Domain Specific Modeling ◽

Specific Modeling

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Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems

Applied Sciences ◽

10.3390/app11125476 ◽

2021 ◽

Vol 11 (12) ◽

pp. 5476

Author(s):

Ana Pajić Simović ◽

Slađan Babarogić ◽

Ognjen Pantelić ◽

Stefan Krstović

Keyword(s):

Process Mining ◽

Modeling Language ◽

Sufficient Information ◽

Event Data ◽

Erp Systems ◽

Domain Experts ◽

Domain Specific ◽

Event Logs ◽

Domain Specific Modeling ◽

Specific Modeling

Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.

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