2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)最新文献

Model-Based Development of Engine Control Systems: Experiences and Lessons Learnt 基于模型的发动机控制系统开发:经验和教训

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/MODELS50736.2021.00038

Justin Cooper, A. D. L. Vega, R. Paige, D. Kolovos, M. Bennett, Caroline Brown, Beatriz Sanchez Piña, Horacio Hoyos

引用次数: 6

Exploring Architectural Design Decisions in Industry 4.0: A Literature Review and Taxonomy 探索工业4.0中的建筑设计决策:文献综述和分类

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/MODELS50736.2021.00026

Tarik Terzimehic, K. Dorofeev, S. Voss

{"title":"Exploring Architectural Design Decisions in Industry 4.0: A Literature Review and Taxonomy","authors":"Tarik Terzimehic, K. Dorofeev, S. Voss","doi":"10.1109/MODELS50736.2021.00026","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00026","url":null,"abstract":"Architectural design decisions, such as service deployment and composition, plant layout synthesis, or production planning, are an indispensable and overarching part of an industrial manufacturing system design. In the fourth industrial revolution (Industry 4.0), frequent production changes trigger their synthesis, and preferably optimization. Yet, knowledge on architecture synthesis and optimization has been scattered around other domains, such as generic software engineering. We take a step towards synthesizing current knowledge on architectural design decisions in Industry 4.0. We developed a taxonomy describing architectural models, design decisions, and optimization possibilities. The developed taxonomy serves as a guideline for comparing different possibilities (e.g., application of different optimization algorithms) and selecting appropriate ones for a given context. Furthermore, we reviewed and mapped 30 relevant research works to the taxonomy, identifying research trends and gaps. We discuss interesting, and yet uncovered topics that emerged from our review.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121518858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Towards Reinforcement Learning for In-Place Model Transformations 面向就地模型转换的强化学习

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/MODELS50736.2021.00017

M. Eisenberg, Hans-Peter Pichler, Antonio Garmendía, M. Wimmer

{"title":"Towards Reinforcement Learning for In-Place Model Transformations","authors":"M. Eisenberg, Hans-Peter Pichler, Antonio Garmendía, M. Wimmer","doi":"10.1109/MODELS50736.2021.00017","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00017","url":null,"abstract":"Model-driven optimization has gained much interest in the last years which resulted in several dedicated extensions for in-place model transformation engines. The main idea is to exploit domain-specific languages to define models which are optimized by applying a set of model transformation rules. Objectives are guiding the optimization processes which are currently mostly realized by meta-heuristic searchers such as different kinds of Genetic Algorithms. However, meta-heuristic search approaches are currently challenged by reinforcement learning approaches for solving optimization problems. In this new ideas paper, we apply for the first time reinforcement learning for in-place model transformations. In particular, we extend an existing model-driven optimization approach with reinforcement learning techniques. We experiment with value-based and policy-based techniques. We investigate several case studies for validating the feasibility of using reinforcement learning for model-driven optimization and compare the performance against existing approaches. The initial evaluation shows promising results but also helped in identifying future research lines for the whole model transformation community.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122876730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Towards the Characterization of Realistic Model Generators using Graph Neural Networks 用图神经网络表征现实模型生成器

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/MODELS50736.2021.00015

José Antonio Hernández López, J. Cuadrado

{"title":"Towards the Characterization of Realistic Model Generators using Graph Neural Networks","authors":"José Antonio Hernández López, J. Cuadrado","doi":"10.1109/MODELS50736.2021.00015","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00015","url":null,"abstract":"The automatic generation of software models is an important element in many software and systems engineering scenarios such as software tool certification, validation of cyber-physical systems, or benchmarking graph databases. Several model generators are nowadays available, but the topic of whether they generate realistic models has been little studied. The state-of-the-art approach to check the realistic property in software models is to rely on simple comparisons using graph metrics and statistics. This generates a bottleneck due to the compression of all the information contained in the model into a small set of metrics. Furthermore, there is a lack of interpretation in these approaches since there are no hints of why the generated models are not realistic. Therefore, in this paper, we tackle the problem of assessing how realistic a generator is by mapping it to a classification problem in which a Graph Neural Network (GnN) will be trained to distinguish between the two sets of models (real and synthetic ones). Then, to assess how realistic a generator is we perform the Classifier Two-Sample Test (C2ST). Our approach allows for interpretation of the results by inspecting the attention layer of the GNN. We use our approach to assess four state-of-the-art model generators applied to three different domains. The results show that none of the generators can be considered realistic.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132733651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Leveraging Model-Driven Technologies for JSON Artefacts: The Shipyard Case Study 利用JSON工件的模型驱动技术:船厂案例研究

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/MODELS50736.2021.00033

A. Colantoni, Antonio Garmendía, L. Berardinelli, M. Wimmer, Johannes Bräuer

{"title":"Leveraging Model-Driven Technologies for JSON Artefacts: The Shipyard Case Study","authors":"A. Colantoni, Antonio Garmendía, L. Berardinelli, M. Wimmer, Johannes Bräuer","doi":"10.1109/MODELS50736.2021.00033","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00033","url":null,"abstract":"With JSON's increasing adoption, the need for structural constraints and validation capabilities led to JSON Schema, a dedicated meta-language to specify languages which are in turn used to validate JSON documents. Currently, the standardisation process of JSON Schema and the implementation of adequate tool support (e.g., validators and editors) are work in progress. However, the periodic issuing of newer JSON Schema drafts makes tool development challenging. Nevertheless, many JSON Schemas as language definitions exist, but JSON documents are still mostly edited in basic text-based editors. To tackle this challenge, we investigate in this paper how Model-Driven Engineering (MDE) methods for language engineering can help in this area. Instead of re-inventing the wheel of building up particular technologies directly for JSON, we study how the existing MDE infrastructures may be utilized for JSON. In particular, we present a bridge between the JSONware and Modelware technical spaces to exchange languages and documents. Based on this bridge, our approach supports language engineers, domain experts, and tool providers in editing, validating, and generating tool support with enhanced capabilities for JSON schemas and their documents. We evaluate our approach with Shipyard, a JSON Schema-based language for the workflow specification for Keptn, an open-source tool for DevOps automation of cloud-native applications. The results of the case study show that proper editors and language evolution support from MDE can be reused and, at the same time, the surface syntax of JSON is maintained.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134561726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Scalable N-Way Model Matching Using Multi-Dimensional Search Trees 使用多维搜索树的可扩展N-Way模型匹配

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/MODELS50736.2021.00010

Alexander Schultheiss, P. M. Bittner, Lars Grunske, Thomas Thüm, Timo Kehrer

{"title":"Scalable N-Way Model Matching Using Multi-Dimensional Search Trees","authors":"Alexander Schultheiss, P. M. Bittner, Lars Grunske, Thomas Thüm, Timo Kehrer","doi":"10.1109/MODELS50736.2021.00010","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00010","url":null,"abstract":"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.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133785437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

[Title page] (标题页)

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/models50736.2021.00002

引用次数: 0

MoDALAS: Model-Driven Assurance for Learning-Enabled Autonomous Systems MoDALAS:支持学习的自治系统的模型驱动保证

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/MODELS50736.2021.00027

Michael Austin Langford, Kenneth H. Chan, Jonathon Emil Fleck, P. McKinley, Betty H. C. Cheng

引用次数: 6

Monte Carlo Tree Search and GR(1) Synthesis for Robot Tasks Planning in Automotive Production Lines 汽车生产线机器人任务规划的蒙特卡洛树搜索与GR(1)综合

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/MODELS50736.2021.00039

Eric Wete, Joel Greenyer, A. Wortmann, Oliver Flegel, Martin Klein

{"title":"Monte Carlo Tree Search and GR(1) Synthesis for Robot Tasks Planning in Automotive Production Lines","authors":"Eric Wete, Joel Greenyer, A. Wortmann, Oliver Flegel, Martin Klein","doi":"10.1109/MODELS50736.2021.00039","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00039","url":null,"abstract":"In automotive production cells, complex processes involving multiple robots must be optimized for cycle time. We investigated using symbolic GR(1) controller synthesis for automating multi-robot task planning. Given a specification of the order of tasks and states to avoid, often multiple valid strategies can be computed; in many states there are multiple choices to satisfy the specification, such as choosing different robots to perform a certain task. To determine the best choices under the consideration of movement times and probabilities that robots may be interrupted for repairs or corrections, we combine the execution of the synthesized controller with Monte Carlo Tree Search (MCTS), a heuristic AI-planning technique. The result is a model-at-run-time approach that we present by the example of a multi-robot spot welding cell. We report on experiments showing that the approach (1) can reduce cycle times by choosing time-efficient movement sequences and (2) can choose executions that react efficiently to interruptions by choosing to delay tasks that, if an interruption of one robot should occur later, can be reallocated to another robot. Most interestingly, we found, however, that (3) in some cases there is a conflict between time-efficient movement sequences and ones that may react efficiently to probable future interruptions—and when interruption probabilities are low, increasing the time allocated for MCTS, i.e., increasing the number of sample simulations made by MCTS, does not improve cycle time.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117079391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

OSTRICH - A Type-Safe Template Language for Low-Code Development 用于低代码开发的类型安全模板语言

2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS) Pub Date : 2021-10-01 DOI: 10.1109/MODELS50736.2021.00030

Hugo Lourenço, Carla Ferreira, João Costa Seco

{"title":"OSTRICH - A Type-Safe Template Language for Low-Code Development","authors":"Hugo Lourenço, Carla Ferreira, João Costa Seco","doi":"10.1109/MODELS50736.2021.00030","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00030","url":null,"abstract":"Low-code platforms aim at allowing non-experts to develop complex systems and knowledgeable developers to improve their productivity in orders of magnitude. The greater gain comes from (re)using components developed by experts capturing common patterns across all layers of the application, from the user interface to the data layer and integration with external systems. Often, cloning sample code fragments is the only alternative in such scenarios, requiring extensive adaptation to reach the intended use. Such customization activities require deep knowledge outside of the comfort zone of low-code. To effectively speed up the reuse, composition, and adaptation of pre-defined components, low-code platforms need to provide safe and easy-to-use language mechanisms. This paper introduces OSTRICH, a strongly-typed rich templating language for a low-code platform (OutSystems) that builds on metamodel annotations and allows the correct instantiation of templates. We conservatively extend the existing metamodel and ensure that the resulting code is always well-formed. The results we present include a novel type safety verification of template definitions, and template arguments, providing model consistency across application layers. We implemented this template language in a prototype of the OutSystems platform and ported nine of the top ten most used sample code fragments, thus improving the reuse of professionally designed components.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123850438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9