Keywords: Conceptual Modeling
Astract: Fundamental decisions are made in the early phases of software development. The typical outcomes of these phases are models of different kinds, such as architectural models, data models, and process models. Automation support is required to efficiently and effectively handle large models and conduct continuous quality improvement processes. Thus, several approaches have been proposed that integrate modeling with Artificial Intelligence (AI) methods such as Genetic Algorithms (GAs), among others. These approaches, e.g., transform models to improve their quality by searching for good solutions within the potential solution space. In this chapter, we first review existing applications of AI methods to model-based software engineering problems. Subsequently, we show a representative use case of how a model-based software analysis and design problem can be solved using GAs. In particular, we focus on the well-known and challenging modularization problem: splitting an overarching, monolithic model into smaller modules. We present two encodings, the model-based and the transformation-based encoding, which are both applied for the modularization of Entity-Relationship (ER) diagrams. We further discuss how these encodings may be adapted to other structural models and conclude with an outlook on future research lines related to software modeling intelligence.

Keywords: Inclusive conceptual modeling, Inclusion, Diversity, Equity, Involvement, Belonging

Keywords: Enterprise architecture, Historical analysis, Knowledge graph
Astract: Enterprise Architecture (EA) is the discipline that aims to provide a holistic view of the enterprise by explicating business and IT alignment from the perspectives of high-level corporate strategy down to daily operations and network infrastructures. EAs are consequently complex as they compose and integrate many aspects on different architecture layers. A recent proposal to cope with this complexity and to make EAs amenable to automated and intuitive visual analysis is the transformation of EA models into EA Knowledge Graphs. A remaining limitation of these approaches is that they perceive the EA to be static, i.e., they represent and analyze EAs at a single point in time. In the paper at hand, we introduce a historization concept, a prototypical implementation, and a performance analysis for how EAs can be represented and processed to enable the analysis of their evolution.

Keywords: DSL, EMF, Interoperability, Metamodeling, MSDKVS, Sirius
Astract: Many powerful metamodeling platforms exist, each with strengths, weaknesses, functionalities, programming language(s), and developer community. To exploit the mutual benefits of these platforms, it would be ideal to establish interoperability amongst them and the exchange of metamodels and models. This would enable language engineers to choose the metamodeling platform freely without risking a lock-in effect. Two well-documented and freely available metamodeling platforms are the Eclipse Modeling Framework (EMF) and Microsoft’s Modeling SDK for Visual Studio (MSDKVS). This paper proposes the first achievements toward establishing interoperability between EMF and MSDKVS on an abstract syntax level and a graphical concrete syntax level. To develop such interoperability, we i) comprehensively analyze the two platforms, ii) present a conceptual mapping between them, and iii) eventually implement a bidirectional transformation bridge. The transformed results’ validity, executability, and expressiveness are then quantitatively and qualitatively assessed by transforming a collection of publicly available metamodels.

Keywords: cyber-physical systems, digital twin, model-driven engineering, multi-paradigm modeling
Astract: Cyber-physical systems (CPS) are finding increasing use, whether in factories, autonomous vehicles, or smart buildings. Monitoring the execution of CPSs is crucial since CPSs directly influence their physical environment. Like the actual system, the monitoring application must be designed, developed, and tested. Mobile CPSs, in contrast to stationary CPSs, bring the additional requirement that instances can dynamically join, leave, or fail during execution time. This dynamic behavior must also be considered in the monitoring application. This paper presents CPSAML, a language and code generation framework for the model-driven development of mobile CPS systems, including a cockpit application for monitoring and interacting with such a system. The pipeline starts with the formulation of the system and the CPSs it contains at an abstract level by the system architect using a domain-specific modeling language. Next, this model is transformed into SysML 2 for further extension and richer specificity by system engineers on a more technical level. In the last step of the pipeline, the SysML 2 model is used to generate code for the CPS devices, a system-wide digital twin, and the cockpit application mentioned above. This cockpit enables the operator to configure and apply the monitoring and interaction with the system during runtime. We evaluate our CPSAML language and code generation framework on an Indoor Transport System case study with Roomba vacuum cleaner robots.