Raquel Araújo de Oliveira, Mario Cortes-Cornax, Agnès Front
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Supporting method engineering with a low-code approach: the LOMET tool
Method engineering emerged in the 1990s as a discipline aiming to design, construct, and adapt methods, techniques, and tools for the development of information systems. By executing a method step by step, users can follow a well-defined process to achieve the intended results for which the method was created. To create methods in a more guided and systematic manner, a framework of methods can serve as a template. This allows individuals to leverage the expertise of method engineers who have consolidated their best practices within these frameworks. However, the creation and adoption of a method can be challenging in the absence of tools to support these activities. Additionally, method engineers may lack the programming skills required to implement such tools. In this context, we extend an approach inspired by the low-code paradigm for method engineering. By integrating construction rules for guidance (called here protocols), the goal of this approach is to assist method engineers in creating new methods or adapting existing frameworks. It automatically provides tool support, enabling method experts to effectively execute the method. This paper builds upon previous work and presents the approach through a proof-of-concept implementation, LOMET. We present a second version of LOMET, which has been refined based on feedback received during an empirical evaluation conducted through semi-structured interviews.
期刊介绍:
We invite authors to submit papers that discuss and analyze research challenges and experiences pertaining to software and system modeling languages, techniques, tools, practices and other facets. The following are some of the topic areas that are of special interest, but the journal publishes on a wide range of software and systems modeling concerns:
Domain-specific models and modeling standards;
Model-based testing techniques;
Model-based simulation techniques;
Formal syntax and semantics of modeling languages such as the UML;
Rigorous model-based analysis;
Model composition, refinement and transformation;
Software Language Engineering;
Modeling Languages in Science and Engineering;
Language Adaptation and Composition;
Metamodeling techniques;
Measuring quality of models and languages;
Ontological approaches to model engineering;
Generating test and code artifacts from models;
Model synthesis;
Methodology;
Model development tool environments;
Modeling Cyberphysical Systems;
Data intensive modeling;
Derivation of explicit models from data;
Case studies and experience reports with significant modeling lessons learned;
Comparative analyses of modeling languages and techniques;
Scientific assessment of modeling practices