Pub Date : 2021-10-01DOI: 10.1109/MODELS50736.2021.00038
Justin Cooper, A. D. L. Vega, R. Paige, D. Kolovos, M. Bennett, Caroline Brown, Beatriz Sanchez Piña, Horacio Hoyos
Rolls-Royce Control Systems supplies engine control and monitoring systems for aviation applications, and is required to design, certify, and deliver these to the highest level of safety assurance. To allow Rolls-Royce to develop safe and robust systems, which continue to increase in complexity, model-based techniques are now a critical part of the software development process. In this paper, we discuss the experiences, challenges and lessons learnt when developing a bespoke domain-specific modelling workbench based on open-source modelling technologies including the Eclipse Modelling Framework (EMF), Xtext, Sirius and Epsilon. This modelling workbench will be used to architect and integrate the software for all future Rolls-Royce engine control and monitoring systems.
{"title":"Model-Based Development of Engine Control Systems: Experiences and Lessons Learnt","authors":"Justin Cooper, A. D. L. Vega, R. Paige, D. Kolovos, M. Bennett, Caroline Brown, Beatriz Sanchez Piña, Horacio Hoyos","doi":"10.1109/MODELS50736.2021.00038","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00038","url":null,"abstract":"Rolls-Royce Control Systems supplies engine control and monitoring systems for aviation applications, and is required to design, certify, and deliver these to the highest level of safety assurance. To allow Rolls-Royce to develop safe and robust systems, which continue to increase in complexity, model-based techniques are now a critical part of the software development process. In this paper, we discuss the experiences, challenges and lessons learnt when developing a bespoke domain-specific modelling workbench based on open-source modelling technologies including the Eclipse Modelling Framework (EMF), Xtext, Sirius and Epsilon. This modelling workbench will be used to architect and integrate the software for all future Rolls-Royce engine control and monitoring systems.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"7 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":"121249057","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}
Pub Date : 2021-10-01DOI: 10.1109/MODELS50736.2021.00026
Tarik Terzimehic, K. Dorofeev, S. Voss
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.
{"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}
Pub Date : 2021-10-01DOI: 10.1109/MODELS50736.2021.00017
M. Eisenberg, Hans-Peter Pichler, Antonio Garmendía, M. Wimmer
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.
{"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}
Pub Date : 2021-10-01DOI: 10.1109/MODELS50736.2021.00015
José Antonio Hernández López, J. Cuadrado
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.
{"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}
Pub Date : 2021-10-01DOI: 10.1109/models50736.2021.00002
{"title":"[Title page]","authors":"","doi":"10.1109/models50736.2021.00002","DOIUrl":"https://doi.org/10.1109/models50736.2021.00002","url":null,"abstract":"","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"55 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":"116668816","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}
Pub Date : 2021-10-01DOI: 10.1109/MODELS50736.2021.00039
Eric Wete, Joel Greenyer, A. Wortmann, Oliver Flegel, Martin Klein
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.
{"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}
Pub Date : 2021-10-01DOI: 10.1109/MODELS50736.2021.00027
Michael Austin Langford, Kenneth H. Chan, Jonathon Emil Fleck, P. McKinley, Betty H. C. Cheng
Increasingly, safety-critical systems include artificial intelligence and machine learning components (i.e., Learning-Enabled Components (LECs)). However, when behavior is learned in a training environment that fails to fully capture real-world phenomena, the response of an LEC to untrained phenomena is uncertain, and therefore cannot be assured as safe. Automated methods are needed for self-assessment and adaptation to decide when learned behavior can be trusted. This work introduces a model-driven approach to manage self-adaptation of a Learning-Enabled System (LES) to account for run-time contexts for which the learned behavior of LECs cannot be trusted. The resulting framework enables an LES to monitor and evaluate goal models at run time to determine whether or not LECs can be expected to meet functional objectives. Using this framework enables stakeholders to have more confidence that LECs are used only in contexts comparable to those validated at design time.
{"title":"MoDALAS: Model-Driven Assurance for Learning-Enabled Autonomous Systems","authors":"Michael Austin Langford, Kenneth H. Chan, Jonathon Emil Fleck, P. McKinley, Betty H. C. Cheng","doi":"10.1109/MODELS50736.2021.00027","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00027","url":null,"abstract":"Increasingly, safety-critical systems include artificial intelligence and machine learning components (i.e., Learning-Enabled Components (LECs)). However, when behavior is learned in a training environment that fails to fully capture real-world phenomena, the response of an LEC to untrained phenomena is uncertain, and therefore cannot be assured as safe. Automated methods are needed for self-assessment and adaptation to decide when learned behavior can be trusted. This work introduces a model-driven approach to manage self-adaptation of a Learning-Enabled System (LES) to account for run-time contexts for which the learned behavior of LECs cannot be trusted. The resulting framework enables an LES to monitor and evaluate goal models at run time to determine whether or not LECs can be expected to meet functional objectives. Using this framework enables stakeholders to have more confidence that LECs are used only in contexts comparable to those validated at design time.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"93 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":"116263965","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}
Pub Date : 2021-10-01DOI: 10.1109/MODELS50736.2021.00030
Hugo Lourenço, Carla Ferreira, João Costa Seco
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.
{"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}
Pub Date : 2021-10-01DOI: 10.1109/models50736.2021.00008
{"title":"Program Board","authors":"","doi":"10.1109/models50736.2021.00008","DOIUrl":"https://doi.org/10.1109/models50736.2021.00008","url":null,"abstract":"","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"120 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":"128064834","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}
Pub Date : 2021-10-01DOI: 10.1109/MODELS50736.2021.00024
Florian Ege, Matthias Tichy
The declarative graph transformation language Henshin transforms instance models represented as graphs by applying a series of basic steps that match and replace structural patterns on parts of models. These simple transformation rules are then combined into control flow constructs similar to those of imperative programming languages to create more complex transformations. However, defects in the structure of control flow or in transformation rules might misschedule the application of operations, resulting in basic steps to be inapplicable or produce incorrect output. Understanding and fixing these bugs is complicated by the fact that pattern matching in rules is non-deterministic. Moreover, some control flow structures employ a nondeterministic choice of alternatives. This makes it challenging for developers to keep track of all the possible execution paths and interactions between them. For conventional programming languages, techniques have been developed to execute a program symbolically. By abstracting over the concrete values of variables in any actual run, generalized knowledge is gained about the possible behavior of the program. This can be useful in understanding problems and fixing bugs. In this paper, we present an approach to symbolically execute graph transformations for a subset of Henshin, using symbolic path constraints based on the cardinalities of graph pattern occurrences in the model.
{"title":"Towards Control Flow Analysis of Declarative Graph Transformations with Symbolic Execution","authors":"Florian Ege, Matthias Tichy","doi":"10.1109/MODELS50736.2021.00024","DOIUrl":"https://doi.org/10.1109/MODELS50736.2021.00024","url":null,"abstract":"The declarative graph transformation language Henshin transforms instance models represented as graphs by applying a series of basic steps that match and replace structural patterns on parts of models. These simple transformation rules are then combined into control flow constructs similar to those of imperative programming languages to create more complex transformations. However, defects in the structure of control flow or in transformation rules might misschedule the application of operations, resulting in basic steps to be inapplicable or produce incorrect output. Understanding and fixing these bugs is complicated by the fact that pattern matching in rules is non-deterministic. Moreover, some control flow structures employ a nondeterministic choice of alternatives. This makes it challenging for developers to keep track of all the possible execution paths and interactions between them. For conventional programming languages, techniques have been developed to execute a program symbolically. By abstracting over the concrete values of variables in any actual run, generalized knowledge is gained about the possible behavior of the program. This can be useful in understanding problems and fixing bugs. In this paper, we present an approach to symbolically execute graph transformations for a subset of Henshin, using symbolic path constraints based on the cardinalities of graph pattern occurrences in the model.","PeriodicalId":375828,"journal":{"name":"2021 ACM/IEEE 24th International Conference on Model Driven Engineering Languages and Systems (MODELS)","volume":"86 6 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":"126094875","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}
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