Workshop program

Keynote

Authors: Steven Kelly

Presenter: Steven Kelly

9:30-10:30

The oldest continuously used and evolved domain-specific modelling language among MetaEdit+ customers is currently in its 30th year. In all that time, none of its many hundreds of users has ever had to manually edit a model file to solve a co-evolution issue. What is the reason, and is it possible to make a research career based on it? We will look at the evolutionary pressures that drive modeling language, modeling tool, and language workbench evolution. How do they differ between academia and industry, and are those differences necessary or useful? And finally, why is no co-evolution often the best form of evolution? Our hunt for answers will take us from Beijing to San Diego, from the Arctic Circle to Cape Town, before landing fully carbon-compensated back in Linz.

Slides

Towards Rapid Design of Compartmental Models

Authors: Zahra Fiyouzisabah, Jessie Galasso, Marios Fokaefs, Michalis Famelis

Presenter: Zahra Fiyouzisabah

11:00-11:30

In times of crisis, epidemiologists can come under great pressure to model rapidly evolving diseases and to produce analyses about the effects of potential public health interventions. Taking previ- ously developed, tested, and validated model components as the base on which to prototype new infectious disease models can save precious time and effort. However, there is currently no systematic process for quickly navigating a corpus of existing epidemiological models or identifying and reusing their most useful components. In this paper, we propose a vision to accelerate the creation of pro- totype compartmental models for infectious diseases. We outline a semi-automated process that epidemiologists can use to create pro- totypes that have been partially completed with reused fragments from existing models. Epidemiologists can thus focus on modelling the novel aspects of an ongoing public health crisis, as opposed to aspects of it that are already more or less well understood in previous work. Our approach comprises steps to identify useful components in a corpus of infectious disease models, generate po- tential candidate prototypes, and organize them in a formal data structure that allows navigation and exploration by the modellers. We outline the challenges ahead and discuss potential solutions based on formal modelling techniques.

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Towards the Estimation of Quality Attributes on System Model Histories

Authors: Konstantin Rupert Blaschke, Simon Barner

Presenter: Konstantin Rupert Blaschke

11:30-12:00

Companies increasingly rely on Model-Based Systems Engineering to develop Cyber-Physical Systems such as cars, aircraft, or medical devices. The quality of engineering model artifacts is key to efficient collaboration in systems engineering with multi-tier supply chains. Ensuring model artifact quality and comprehensibility for practitioners is challenging. Manual reviews are time- and cost-intensive and subject to bias, whereas existing automated methods based on syntactical rules and model metrics are limited in scope. The paper presents work towards swift quality feedback to system engineers during modeling. The concept allows domain and project-specific context and is applicable to industry-size model artifacts. We implement a data-driven estimation prototype that combines automated model metric extraction with expert quality assessments. We leverage the system model version history of assisted driving functions from an open-source miniature automotive demonstrator. We assess the model versions’ comprehensibility and showcase a semi-automated pipeline to train and deploy a model quality attribute estimator. We achieve our best estimation with a random forest approach with an average accuracy of 0.94 on our assessment data.

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Digital Twin Evolution for Sustainable Smart Ecosystems

Authors: Judith Michael, Istvan David, Dominik Bork

Presenter: Judith Michael

12:00-12:30

Smart ecosystems are the drivers of modern society. They control in- frastructures of socio-techno-economic importance, ensuring their stable and sustainable operation. Smart ecosystems are governed by digital twins—real-time virtual representations of physical in- frastructure. To support the open-ended and reactive traits of smart ecosystems, digital twins need to be able to evolve in reaction to changing conditions. However, digital twin evolution is challenged by the intertwined nature of physical and software components, and their individual evolution. As a consequence, software practitioners find a substantial body of knowledge on software evolution hard to apply in digital twin evolution scenarios and a lack of knowledge on the digital twin evolution itself. The aim of this paper, conse- quently, is to provide software practitioners with tangible leads toward understanding and managing the evolutionary concerns of digital twins. Concretely, we use four distinct digital twin evolution scenarios, contextualized in a citizen energy community case to illustrate the usage of the 7R taxonomy of digital twin evolution. By that, we aim to bridge a significant gap in leveraging software engineering practices to develop robust smart ecosystems.

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Towards the Co-evolution of Models, Code, and Assurance Cases

Authors: Patrik Meijer, Nag Mahadevan, Mark Wutka, Gabor Karsai

Presenter: Gabor Karsai

14:00-14:30

Model-based software development is rarely performed with mod- els only, and not everything is generated from the model(s). Hand- crafted source code, documentation, simulation, data, tests, and assurance cases are the obvious (but not the only) examples. These artifacts, just like models, are version controlled, but kept in various, disjoint repositories. As they are often dependent on each other, the challenge is to maintain and manage the consistent co-evolution of such artifacts in the context of an agile development process. Some of the artifacts are related to software assurance – the construction of logical arguments, called assurance cases, that demonstrate why the software (or system) is safe and performant – which need to be continuously revised and updated in a deadline-driven develop- ment process. The Continuous Assurance-Integrated Development (CAID) tool framework has been constructed to address the chal- lenge stated above. The framework integrates and links together various software engineering artifacts models, source code, doc- uments, assurance cases, etc. and provides dependency tracking and change management functions. The framework is based on an open server-database/client-adapter architecture, where various repositories can be interwoven. A prototype of the framework has been created that integrates git repositories, a model database, and assurance case documents. The prototype has been published under an open source license.

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Building Deduplicated Model Repositories to Assess Domain-Specific Languages Evolution

Authors: Alexandre Lachance, Sébastien Mosser

Presenter: Alexandre Lachance

14:30-15:00

Software evolution and maintenance is a real challenge in modern software engineering. In the context of model-driven development, which heavily rely on interconnected (meta-)models, tools and generators, the evolution of both models and/or their associated meta-models is a tricky task. Such a problem also appears in language engineering, when evolving the language (its grammar, its semantic) needs to stay aligned with the existing models that already exist. In this paper, we explore how techniques inspired by repository mining can help a model designer/language engineer to build a deduplicated dataset of existing models available on open source repository. Deduplication is essential to ensure the evolution made on the metamodel/language can be efficiently assessed. We apply the method to the P4 language, an industrial domain-specific language (Intel, Linux foundation) used to model software defined network.

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A Delta-Oracle for Fast Model Merge Conflict Estimation using Sketch-Based Critical Pair Analysis

Authors: Karl Kegel, Andreas Domanowski, Kevin Feichtinger, Romain Pascualm, Uwe Aßmann

Presenter: Karl Kegel

15:00-15:30

Conflicting changes are a major challenge in branch-based development and modeling. State-of-the-art research proposes continuous analysis via attempted three-way merges to find potential merge conflicts early on. These approaches are computation-heavy due to the necessity of comparing all variant combinations, ideally for each change. Thus, such approaches are infeasible for large models. This work proposes a conflict approximation algorithm (oracle) for quick feedback. A modeling tool can track each collaborator’s changes in delta sequences. The oracle approximates conflicts using critical pair analysis on these delta sequences, providing a quick feedback loop. The oracle is paired with a classical slow-but-precise full model comparison algorithm, which is run occasionally to validate the oracle’s results. This work contributes the Sketch-based Critical Pair Analysis (SCPA) approach for fast merge conflict estimation. SCPA’s runtime depends only on the number of changes and not the model size. We evaluate SCPA against EMFCompare in different simulated model evolution scenarios. We found that for the investigated model sizes, SCPA is faster by a magnitude while the number of found conflicts strongly correlates with EMFCompare.

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