Auflistung nach Schlagwort "Model-driven engineering"
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- ZeitschriftenartikelAI-Enhanced Hybrid Decision Management(Business & Information Systems Engineering: Vol. 65, No. 2, 2023) Bork, Dominik; Ali, Syed Juned; Dinev, Georgi MilenovThe Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach.
- KonferenzbeitrageMoflon: A Tool for Tools and Transformations(Modellierung 2018, 2018) Fritsche, Lars; Kulcsár, GézaeMoflon is a model-based meta-CASE framework, which allows users to build their own solutions for modern MDE scenarios. Particularly, eMoflon supports meta-modeling and unidirectional as well as bidirectional model transformation. In this tutorial, those major functionalities of eMoflon are presented using a case study of object-oriented refactorings.
- KonferenzbeitragA framework for capturing, statistically modeling and analyzing the evolution of software models(Software Engineering und Software Management 2018, 2018) Shariat Yazdi, Hamed; Angelis, Lefteris; Kehrer, Timo; Kelter, UdoIn this work, we report about a recently developed framework for capturing, statistically modeling and analyzing the evolution of software models, published in the Journal of Systems and Software, Vol-118, Aug-2016. State-of-the-art approaches to understand the evolution of models of software systems are based on software metrics and similar static properties; the extent of the changes between revisions of a software system is expressed as differences of metrics values, and statistical analyses are based on these differences. Unfortunately, such approaches do not properly reflect the dynamic nature of changes. In contrast to this, our framework captures the changes between revisions of models in terms of both low-level (internal) and high-level (developer-visible) edit operations applied between revisions. Evolution is modeled statistically by using ARMA, GARCH and mixed ARMA-GARCH time series models. Forecasting and simulation aspects of these time series models are thoroughly assessed, and the suitability of the framework is shown by applying it to a large set of design models of real Java systems. A main motivation for, and application of, the resulting statistical models is to control the generation of realistic model histories which are intended to be used for testing model versioning tools. Further usages of the statistical models include various forecasting and simulation tasks.
- KonferenzbeitragScalable N-Way Model Matching Using Multi-Dimensional Search Trees - Summary(Software Engineering 2022, 2022) Schultheiß, Alexander; Bittner, Paul Maximilian; Thüm, Thomas; Kehrer, TimoIn this work, we report about recent research on n-way model matching, originally published at the International Conference on Model Driven Engineering Languages and Systems (MODELS) 2021. Model matching algorithms are used to identify common elements in input models, which is a fundamental precondition for many software engineering tasks, such as merging software variants or views. If there are multiple input models, an n-way matching algorithm that simultaneously processes all models typically produces better results than the sequential application of two-way matching algorithms. However, existing algorithms for n-way matching do not scale well, as the computational effort grows fast in the number of models and their size. We propose a scalable n-way model matching algorithm, which uses multi-dimensional search trees for efficiently finding suitable match candidates through range queries. We implemented our generic algorithm named RaQuN (Range Queries on N input models) in Java, and empirically evaluate the matching quality and runtime performance on several datasets of different origin and model type. Compared to the state-of-the-art, our experimental results show a performance improvement by an order of magnitude, while delivering matching results of better quality.
- ZeitschriftenartikelSWEL: A Domain-Specific Language for Modeling Data-Intensive Workflows(Business & Information Systems Engineering: Vol. 66, No. 2, 2024) Salado-Cid, Rubén; Vallecillo, Antonio; Munir, Kamram; Romero, José RaúlData-intensive applications aim at discovering valuable knowledge from large amounts of data coming from real-world sources. Typically, workflow languages are used to specify these applications, and their associated engines enable the execution of the specifications. However, as these applications become commonplace, new challenges arise. Existing workflow languages are normally platform-specific, which severely hinders their interoperability with other languages and execution engines. This also limits their reusability outside the platforms for which they were originally defined. Following the Design Science Research methodology, the paper presents SWEL (Scientific Workflow Execution Language). SWEL is a domain-specific modeling language for the specification of data-intensive workflows that follow the model-driven engineering principles, covering the high-level definition of tasks, information sources, platform requirements, and mappings to the target technologies. SWEL is platform-independent, enables collaboration among data scientists across multiple domains and facilitates interoperability. The evaluation results show that SWEL is suitable enough to represent the concepts and mechanisms of commonly used data-intensive workflows. Moreover, SWEL facilitates the development of related technologies such as editors, tools for exchanging knowledge assets between workflow management systems, and tools for collaborative workflow development.
- KonferenzbeitragA systematic approach to constructing families of incremental topology control algorithms using graph transformation(Software Engineering und Software Management 2018, 2018) Kluge, Roland; Stein, Michael; Varró, Gergely; Schürr, Andy; Hollick, Matthias; Mühlhäuser, MaxIn this talk, we present results on integrating support for variability modeling into a correct-by-construction development methodology for topology control algorithms, as appeared online in the Software & Systems Modeling journal in 2017. A topology control algorithm reduces the size of the visible neighborhood of a node in a wireless communication network. At the same time, it must fulfill important consistency properties to ensure a high quality of service. In previous work, we proposed a constructive, model-driven methodology for designing individual topology control algorithms based on declarative graph constraints and graph transformation rules; the resulting algorithms are guaranteed to preserve the specified properties. Even though many topology control algorithms share substantial (structural) parts, few works leverage these commonalities at design time. In this work, we generalize our proposed construction methodology by modeling variability points to support the construction of families of algorithms. We show the applicability of our approach by reengineering six existing topology control algorithms and developing e-kTC, a novel energy-efficient variant of the topology control algorithm kTC. Finally, we evaluate a subset of the algorithms using a novel integration of a wireless network simulator and a graph transformation tool.