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14 Papers

Early Fault-Detection in the Development of Exceedingly Complex Reactive Systems

Topics: General-Purpose Modeling Languages and Standards; Reasoning about Models; Artificial Intelligence (AI) for Modeling Support; Frameworks for Model-Based Development ; Software and Systems Engineering

Authors: Assaf Marron and David Harel

Affiliation: Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, 76100, Israel

Digital Object Identifier: 10.5220/0013369200003896

Proceedings of MODELSWARD 2025, ISBN: 978-989-758-729-0

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Finding hidden faults in reactive systems early in planning and development is critical for human safety, the environment, society and the economy. However, the ever growing complexity of reactive systems and their interactions, combined with the absence of adequate technical details in early development stages, pose a great obstacle. The problem is exacerbated by the constant evolution of systems, and by their extensive and growing interwoven-ness with other systems and the physical world. Appropriately, such systems may be termed super-reactive. We propose an architecture for models and tools that help overcome such barriers and enable simulation, systematic analysis, and fault detection and handling, early in the development of super-reactive systems. The main innovations are: (i) the allowing of natural language (NL) specifications in elements of otherwise standard models and specification formalisms, while deferring the interpretation of such NL elements to simulation and validation time; and (ii) a focus on early formalization of tacit interdependencies among seemingly orthogonal requirements. The approach is facilitated by combining newly specialized tools with standard development and verification facilities, and with the inference and abstraction capabilities of large language models (LLMs) and associated AI techniques. An important ingredient in the approach is the domain knowledge embedded in LLMs. Special methodological measures are proposed to mitigate well known limitations of LLMs.

On Augmenting Scenario-Based Modeling with Generative AI

Topics: General-Purpose Modeling Languages and Standards; Reasoning about Models; Artificial Intelligence (AI) for Modeling Support

Authors: David Harel 1 , Guy Katz 2 , Assaf Marron 1 and Smadar Szekely 1

Affiliations: 1 Weizmann Institute of Science, Rehovot, Israel , 2 The Hebrew University of Jerusalem, Jerusalem, Israel

Digital Object Identifier: 10.5220/0012427100003645

Proceedings of MODELSWARD 2024, ISBN: 978-989-758-682-8

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The manual modeling of complex systems is a daunting task; and although a plethora of methods exist that mitigate this issue, the problem remains very difficult. Recent advances in generative AI have allowed the creation of general-purpose chatbots, capable of assisting software engineers in various modeling tasks. However, these chatbots are often inaccurate, and an unstructured use thereof could result in erroneous system models. In this paper, we outline a method for the safer and more structured use of chatbots as part of the modeling process. To streamline this integration, we propose leveraging scenario-based modeling techniques, which are known to facilitate the automated analysis of models. We argue that through iterative invocations of the chatbot and the manual and automatic inspection of the resulting models, a more accurate system model can eventually be obtained. We describe favorable preliminary results, which highlight the potential of this approach.

Challenges in Modeling and Unmodeling Emergence, Rule Composition, and Networked Interactions in Complex Reactive Systems

Topics: Model Execution and Simulation; General-Purpose Modeling Languages and Standards; Agile Model-Based Development ; Software and Systems Engineering ; Modeling Environments; Model-Based Engineering of Digital Twins

Authors: Assaf Marron 1 , Irun Cohen 2 , Guy Frankel 1 , David Harel 1 and Smadar Szekely 1

Affiliations: 1 Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, 76100, Israel , 2 Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, 76100, Israel

Digital Object Identifier: 10.5220/0011728900003402

Proceedings of MODELSWARD 2023, ISBN: 978-989-758-633-0

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Models of complex systems, both human-made and natural, assist in making critical decisions with regard to function, safety, economy, the environment and more. In this position paper, we explore the difficulty in modeling several innate properties of such systems, including: (i) the frequent emergence of new entities (like group effects and temporal patterns) and the system’s reaction to such emergence; (ii) the essence of the system’s reactive behavior as a rich composition of stand-alone rules; and (iii) the vast number of internal and external interactions that the system engages in. For each of these challenges we propose some implications to modeling—methodological approaches that can help address it and potential support in modeling languages and tools. We introduce the concept of unmodeling—formally defining model entities and behaviors that are excluded from model execution—and discuss how unmodeling enhances model quality, supports incremental enhancement, and facilitates evaluation. This report emanates from our research and development in modeling languages and methods and our present research in biological evolution. We believe that analysis and implementation of these principles have general applicability in model development and assessment.

Toward Automated Modeling of Abstract Concepts and Natural Phenomena: Autoencoding Straight Lines

Topics: Modeling for AI Applications; Artificial Intelligence (AI) for Modeling Support; Agile Model-Based Development ; Frameworks for Model-Based Development ; Software and Systems Engineering ; Model-Based Software Development; Modeling Environments

Authors: Yuval Bayer , David Harel , Assaf Marron and Smadar Szekely

Affiliation: Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, 76100, Rehovot, Israel

Digital Object Identifier: 10.5220/0011886100003402

Proceedings of MODELSWARD 2023, ISBN: 978-989-758-633-0

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Modeling complex systems or natural phenomena requires special skills and extensive domain knowledge. This makes automating model development an intriguing challenge. One question is whether a model’s ontology—the essence of its entities—can be learned automatically from observation. We describe work in progress on automating the learning of a basic concept: an image of the straight line segment between two points in a two-dimensional plane. Humans readily encode such images using two endpoints, or a point, an angle, and a length. Furthermore, image recognition algorithms readily detect line segments in images. Here, we employ autoencoders. Autoencoders perform both feature extraction and reconstruction of inputs from their coded representation. It turns out that autoencoding line segments is not trivial. Our interim conclusions include: (1) Developing methods for comparing the performance of different autoencoders in a given task is an essential research challenge. (2) Development of autoencoders manifests supervision of this purportedly unsupervised process; one then asks what knowledge employed in such development can be obtained automatically. (3) Automatic modeling of properties of observed objects requires multiple representations and sensors. This work can eventually benefit broader issues in automated model development.

Scenario-assisted Deep Reinforcement Learning

Topics: Model Execution and Simulation; Domain-Specific Modeling; Systems Engineering; Modeling for AI Applications

Authors: Raz Yerushalmi 1 , Guy Amir 2 , Achiya Elyasaf 3 , David Harel 1 , Guy Katz 2 and Assaf Marron 1

Affiliations: 1 Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel , 2 School of Computer Science and Engineering, The Hebrew University of Jerusalem, Givat Ram, Jerusalem 91904, Israel , 3 Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 8410501, Israel

Digital Object Identifier: 10.5220/0010904700003119

Proceedings of MODELSWARD 2022, ISBN: 978-989-758-550-0

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Deep reinforcement learning has proven remarkably useful in training agents from unstructured data. However, the opacity of the produced agents makes it difficult to ensure that they adhere to various requirements posed by human engineers. In this work-in-progress report, we propose a technique for enhancing the reinforcement learning training process (specifically, its reward calculation), in a way that allows human engineers to directly contribute their expert knowledge, making the agent under training more likely to comply with various relevant constraints. Moreover, our proposed approach allows formulating these constraints using advanced model engineering techniques, such as scenario-based modeling. This mix of black-box learning-based tools with classical modeling approaches could produce systems that are effective and efficient, but are also more transparent and maintainable. We evaluated our technique using a case-study from the domain of internet congestion control, obtaining promising results.

On-the-Fly Construction of Composite Events in Scenario-Based Modeling using Constraint Solvers

Topics: Model Execution and Simulation; General-Purpose Modeling Languages and Standards; Constraint Modeling and Languages; Frameworks for Model-Driven Development

Authors: Guy Katz 1 , Assaf Marron 2 , Aviran Sadon 3 and Gera Weiss 3

Affiliations: 1 The Hebrew University of Jerusalem, Jerusalem and Israel , 2 Weizmann Institute of Science, Rehovot and Israel , 3 Ben-Gurion University of the Negev, Be’er Sheva and Israel

Digital Object Identifier: 10.5220/0007573801430156

Proceedings of MODELSWARD 2019, ISBN: 978-989-758-358-2

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Scenario-Based Programming is a methodology for modeling and constructing complex reactive systems from simple, stand-alone building blocks, called scenarios. These scenarios are designed to model different traits of the system, and can be interwoven together and executed to produce cohesive system behavior. Existing execution frameworks for scenario-based programs allow scenarios to specify their view of what the system must, may, or must not do only through very strict interfaces. This limits the methodology’s expressive power and often prevents users from modeling certain complex requirements. Here, we propose to extend Scenario-Based Programming’s execution mechanism to allow scenarios to specify how the system should behave using rich logical constraints. We then leverage modern constraint solvers (such as SAT or SMT solvers) to resolve these constraints at every step of running the system, towards yielding the desired overall system behavior. We provide an implementation of our approach and demonstrate its applicability to various systems that could not be easily modeled in an executable manner by existing Scenario-Based approaches.
Collapse Button Related Ontology Subjects/Areas/Topics (11)
Applications and Software Development

Languages, Tools and Architectures

Models

Constraint Modeling and Languages

Methodologies, Processes and Platforms

Paradigm Trends

Frameworks for Model-Driven Development

Model Execution and Simulation

Software Engineering
General-Purpose Modeling Languages and Standards

Model-Driven Software Development

Using Reactive-System Modeling Techniques to Create Executable Models of Biochemical Pathways

Topics: Domain-Specific Modeling; General-Purpose Modeling Languages and Standards

Authors: Hadas Lapid , Assaf Marron , Smadar Szekely and David Harel

Affiliation: Weizmann Institute of Science, Rehovot and Israel

Digital Object Identifier: 10.5220/0007572504560466

Proceedings of MODELSWARD 2019, ISBN: 978-989-758-358-2

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Scenario-based modeling (SBM) is an emerging approach for creating executable models of complex reactive systems. In addition to its use in software and system development, SBM has been shown to serve well in modeling biological processes. In this position paper, we show that SBM can be used effectively in modeling biochemical pathways at the molecular level, complementing existing biochemical modeling techniques. One of the key benefits of these SBM models is in helping professionals and students better conceptualize and understand such complex processes.
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Domain-Specific Modeling and Domain-Specific Languages

Languages, Tools and Architectures

Software Engineering
General-Purpose Modeling Languages and Standards

Model-Driven Software Development

Towards Systematic and Automatic Handling of Execution Traces Associated with Scenario-based Models

Topics: Model Execution and Simulation; General-Purpose Modeling Languages and Standards; Reasoning about Models; Systems Engineering; Frameworks for Model-Driven Development; Model-Based Testing and Validation

Authors: Joel Greenyer 1 , Daniel Gritzner 1 , David Harel 2 and Assaf Marron 2

Affiliations: 1 Leibniz Universität Hannover, Germany , 2 Weizmann Institute of Science, Israel

Digital Object Identifier: 10.5220/0006671105600566

Proceedings of MODELSWARD 2018, ISBN: 978-989-758-283-7

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Scenario-based specification approaches offer system engineering advantages with their intuitiveness, executability, and amenability to formal verification and synthesis. However, many engineering tasks such as debugging or maintenance are still far from trivial even when using such specifications. Specifically, it is hard to find out why a complex system behaves as it does, or how it would behave under certain conditions. Here, we present work in progress towards the (semi-)automatic analysis of event traces emanating from simulation runs and actual executions. These traces may be large, yet developers are often interested only in specific properties thereof, like is any specification property violated? are particular properties demonstrated? is there a smaller sub-sequence of events that violates or demonstrates the same properties? which trace properties are common to multiple traces and which are unique? etc. Our approach includes automatic techniques for discovering and distilling relevant properties of traces, analyzing properties of sets of traces, using (sets of) execution traces for understanding specified and actual system behavior and problems therein, planning system enhancement and repair, and more. Our work leverages and extends existing work on trace summarization, formal methods for model analysis, specification mining from execution traces, and others, in the context of scenario-based specifications. A key guiding perspective for this research is that interesting properties of a trace often can be associated with one or very few concise scenarios, depicting desired or forbidden behavior, which are already in the specification, or should be added to it.
Collapse Button Related Ontology Subjects/Areas/Topics (13)
Applications and Software Development

Model Execution and Simulation

Paradigm Trends

Frameworks for Model-Driven Development

Model-Based Testing and Validation

Reasoning about Models

General-Purpose Modeling Languages and Standards

Model-Driven Software Development

Software Engineering

Languages, Tools and Architectures

Models

Systems Engineering
Methodologies, Processes and Platforms

Distributing Scenario-based Models: A Replicate-and-Project Approach

Topics: Model Transformation; Model Execution and Simulation; Syntax and Semantics of Modeling Languages; Reasoning about Models; Model Transformations and Generative Approaches

Authors: Shlomi Steinberg 1 , Joel Greenyer 2 , Daniel Gritzner 2 , David Harel 1 , Guy Katz 3 and Assaf Marron 1

Affiliations: 1 The Weizmann Institute of Science, Israel , 2 Leibniz Universität Hannover, Germany , 3 Stanford University, United States

Digital Object Identifier: 10.5220/0006271301820195

Proceedings of MODELSWARD 2017, ISBN: 978-989-758-210-3

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In recent years, scenario-based modeling has been proposed to help mitigate some of the underlying difficulties in modeling complex reactive systems, by allowing modelers to specify system behavior in a way that is intuitive and directly executable. This modeling approach simplifies the specification of systems that include events occurring in distinct system components. However, when these system components are physically distributed, executing the scenario-based model requires inter-component coordination that may negatively affect system performance or robustness. We describe a technique that aims to reduce the amount of joint event-selection decisions that require coordination and synchronization among distributed system components. The technique calls for replicating the entire scenario-based executable specification in each of the components, and then transforming it in a component-specific manner that induces the required differences in execution while reducing synchronization requirements. In addition to advantages in streamlining design and improving performance, our approach captures the fact that in certain ``smart' distributed systems it is often required that components know what rules govern the behavior of other components. Our evaluation of the technique shows promising results.
Collapse Button Related Ontology Subjects/Areas/Topics (12)
Applications and Software Development

Model Transformation

Paradigm Trends

Languages, Tools and Architectures

Model Transformations and Generative Approaches

Reasoning about Models

Methodologies, Processes and Platforms

Model-Driven Software Development

Software Engineering

Model Execution and Simulation

Models

Syntax and Semantics of Modeling Languages

An Initial Wise Development Environment for Behavioral Models

Topics: Component-Based Software Engineering; Model Execution and Simulation; Reasoning about Models; Frameworks for Model-Driven Development; Model-Based Testing and Validation

Authors: David Harel , Guy Katz , Rami Marelly and Assaf Marron

Affiliation: The Weizmann Institute of Science, Israel

Digital Object Identifier: 10.5220/0005811806000612

Proceedings of MODELSWARD 2016, ISBN: 978-989-758-168-7

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We present a development environment that proactively and interactively assists the software engineer in modeling complex reactive systems. Our framework repeatedly analyzes models of the system under development at various levels of abstraction, and then reasons about these models in order to detect possible errors and to derive emergent properties of interest. Upon request, the environment can then augment the system model in order to repair or avoid detected behavior that is undesired, or instrument it in order to monitor the execution for certain behaviors. Specialized automated and human-assisted techniques are incorporated to direct and prioritize the analysis and related tasks, based on the relevance of the observed properties and the expected impact of actions to be taken. Our development environment is an initial step in the direction of the very recent Wise Computing vision, which calls for turning the computer (namely, the development environment) into an equal member of the development team: knowledgeable, independent, concerned and proactively involved in the development process. Our tool is implemented within the context of behavioral programming (BP), a scenario-based modeling approach, where components are aligned with how humans often describe desired system behavior. Thus, our work further enhances the naturalness and incrementality of developing in BP.
Collapse Button Related Ontology Subjects/Areas/Topics (12)
Applications and Software Development

Methodologies, Processes and Platforms

Models

Component-Based Software Engineering

Model Execution and Simulation

Paradigm Trends

Frameworks for Model-Driven Development

Model-Based Testing and Validation

Reasoning about Models

Languages, Tools and Architectures

Model-Driven Software Development

Software Engineering

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