Semantic Evolution Analysis of Feature Models

Drave, Imke; Kautz, Oliver; Michael, Judith; Rumpe, Bernhard

doi:10.1145/3336294.3336300

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…R3. The developed DSL follows an open-world assumption [18,19], i.e., whatever is not restricted by the model is allowed. Currently, the DSL focuses on isolated requirements written in one sentence.…”

Section: Example Dsl For Structured Requirementsmentioning

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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Section: Example Dsl For Structured Requirementsmentioning

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

View full text Add to dashboard Cite

show abstract

“…During this activity, model evolution management including syntactic (Alanen & Porres 2003;Kehrer et al 2011) and semantic model differencing (Maoz et al 2010;Kautz & Rumpe 2018;Acher et al 2012;Langer et al 2014;Maoz & Ringert 2016) enables monitoring changes to the represented system. Semantic differencing, in particular, enables one to decide, whether the modeled system has been changed to include undesired properties, e.g., (Drave, Kautz, et al 2019). It might be possible to reuse existing models without changes for another problem aspect, e.g., a class diagram for the generation of the data structure and the communication infrastructure.…”

Section: Communicationmentioning

confidence: 99%

“…Our methodology aims to enable systematic retrofitting of generated aspects into already running systems, which may be a part of a system family developed using (Czarnecki 2005). An approach to interpret feature models according to a development phase has been proposed in the context of semantic differencing in (Drave, Kautz, et al 2019), which addresses the fact that changes conducted at early development stages have a different intent than at later development stages.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Methodology for Retrofitting Generative Aspects in Existing Applications.

Drave¹,

Gerasimov²,

Michael³

et al. 2021

JOT

Self Cite

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“…These differences can be combined with composition and decomposition operators. Open-world semantics for feature models, together with semantic diffs based on those semantics, are introduced in [DKMR19]. Such semantics includes all configurations-even containing features not belonging to the original feature model-which do not contradict the feature model formula.…”

Section: Software Product Linesmentioning

confidence: 99%

Language Family Engineering with Product Lines of Multi-level Models

Lara

Guerra

2021

Form. Asp. Comput.

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Modelling is an essential activity in software engineering. It typically involves two meta-levels: one includes meta-models that describe modelling languages, and the other contains models built by instantiating those meta-models. Multi-level modelling generalizes this approach by allowing models to span an arbitrary number of meta-levels. A scenario that profits from multi-level modelling is the definition of language families that can be specialized (e.g., for different domains) by successive refinements at subsequent meta-levels, hence promoting language reuse. This enables an open set of variability options given by all possible specializations of the language family. However, multi-level modelling lacks the ability to express closed variability regarding the availability of language primitives or the possibility to opt between alternative primitive realizations. This limits the reuse opportunities of a language family. To improve this situation, we propose a novel combination of product lines with multi-level modelling to cover both open and closed variability. Our proposal is backed by a formal theory that guarantees correctness, enables top-down and bottom-up language variability design, and is implemented atop the MetaDepth multi-level modelling tool.

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Semantic Evolution Analysis of Feature Models

Cited by 9 publications

References 35 publications

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

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

A Methodology for Retrofitting Generative Aspects in Existing Applications.

Language Family Engineering with Product Lines of Multi-level Models

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