A Methodology for Retrofitting Generative Aspects in Existing Applications.

Drave, Imke; Gerasimov, Arkadii; Michael, Judith; Netz, Lukas; Rumpe, Bernhard; Varga, Simon

doi:10.5381/jot.2021.20.2.a7

Cited by 7 publications

(5 citation statements)

References 79 publications

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“…By nature, these systems are highly complex and evolve continuously making them a predestined application domain for MDSE. Part of the complexity of engineering such systems arises from the fact that there are multiple and heterogeneous aspects [17] when it comes to engineering EISs. Examples for such aspects are the graphical user interface (GUI) and the data structures.…”

Section: Motivation and Relevancementioning

confidence: 99%

“…However, through the demand for new or adapted functionalities, not only the system evolves continuously but also the set of modeling languages and code generators employed in the development process [17]. Technology stacks and development processes therefore need to be amenable for change.…”

Section: Motivation and Relevancementioning

confidence: 99%

“…For modeling the input and outputs of tasks, process models need to reference the data classes modeled in a data model of the system and the generation process must assure that the type of a task's input is given by a corresponding class generated from the data models. These are two respective aspects in the sense of [17] the system and both may be relevant for other aspects. To keep models readable and to maintain their purpose, these aspects need to be modeled using appropriate languages.…”

Section: Requirements For Process Modelingmentioning

confidence: 99%

“…Changes in business processes require the new generation and deployment of the system which makes our approach less flexible than systems that only interpret BPMN models at run-time. The data structure and GUI models resulting from the generation using the BPMN models could be written and generated separately, and used by the process engine during run-time [17]. This would avoid a need for regeneration, as the necessary environment would be already generated.…”

Section: Generative Approach Vs Interpretationmentioning

confidence: 99%

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Model-Driven Engineering of Process-Aware Information Systems

Drave

Michael²,

Müller³

et al. 2022

SN COMPUT. SCI.

Self Cite

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Enterprise information systems created with model-driven software engineering methods need to handle not only data but also business processes in an automated way. This paper shows how to engineer process-aware information systems following the model-driven and generative software engineering paradigms. Existing approaches realize either the generation of automated or manual activities but do not employ model-driven engineering of all system aspects through systematic language composition. A generative approach that additionally uses process modeling languages allows developers to evolve generated data-centric information systems into process-aware information systems. To be usable within our generation process, we have developed a textual BPMN version and a corresponding language tooling to check the soundness of the models. We have included these process models into the generation process of an information system together with other domain-specific modeling languages, e.g., for data structures, and generate an extendable, process-aware information system that is open for continuous regeneration and hand-written additions. This approach allows us to lift a generated data-centric information system to a process-aware information system. Agile development enabled through the opportunity to validate assumptions automatically and adapt changes efficiently, enhances the engineering process as well as the generated systems themselves.

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Section: Motivation and Relevancementioning

confidence: 99%

Section: Motivation and Relevancementioning

confidence: 99%

Section: Requirements For Process Modelingmentioning

confidence: 99%

Section: Generative Approach Vs Interpretationmentioning

confidence: 99%

See 2 more Smart Citations

Model-Driven Engineering of Process-Aware Information Systems

Drave

Michael²,

Müller³

et al. 2022

SN COMPUT. SCI.

Self Cite

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“…An approach to achieve this is Model-Driven Engineering (MDE) [22], which utilizes models as the primary development artifacts. That is, these models serve as documentation and communication basis for engineers, but also as input for analyses and syntheses, such as verification [34], test case [17], or code generation [16]. For instance, MDE can be applied to facilitate the design of Artificial Intelligence (AI)-based systems [2,26,37,38].…”

Section: Introductionmentioning

confidence: 99%

Technical Report on Neural Language Models and Few-Shot Learning for Systematic Requirements Processing in MDSE

Bertram¹,

Boß²,

Kusmenko³

et al. 2022

Preprint

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Systems engineering, in particular in the automotive domain, needs to cope with the massively increasing numbers of requirements that arise during the development process. To guarantee a high product quality and make sure that functional safety standards such as ISO26262 are fulfilled, the exploitation of potentials of model-driven systems engineering in the form of automatic analyses, consistency checks, and tracing mechanisms is indispensable. However, the language in which requirements are written, and the tools needed to operate on them, are highly individual and require domainspecific tailoring. This hinders automated processing of requirements as well as the linking of requirements to models. Introducing formal requirement notations in existing projects leads to the challenge of translating masses of requirements and process changes on the one hand and to the necessity of the corresponding training for the requirements engineers.In this paper, based on the analysis of an open-source set of automotive requirements, we derive domain-specific language constructs helping us to avoid ambiguities in requirements and increase the level of formality. The main contribution is the adoption and evaluation of few-shot learning with large pretrained language models for the automated translation of informal requirements to structured languages such as a requirement DSL. We show that support sets of less than ten translation examples can suffice to few-shot train a language model to incorporate keywords and implement syntactic rules into informal natural language requirements.

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