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15 results for “meta-model”
An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization
<p>This is the data and source code used in the paper below:</p> <p>Sibghat Ullah, Hao Wang, Stefan Menzel, Bernhard Sendhoff and Thomas Bäck, “An Empirical Comparison of Meta-Modeling Techniques for Robust Design Optimization”, in 2019 IEEE Symposium Series on Computational Intelligence (SSCI), Xiamen, China, 6-9 December 2019, doi: 10.1109/SSCI44817.2019.9002805</p> <p>This research investigates the potential of using meta-modeling techniques in the context of robust optimization namely optimization under uncertainty/noise. A systematic empirical comparison is performed for evaluating and comparing different meta-modeling techniques for robust optimization. The experimental setup includes three noise levels, six meta-modeling algorithms, and six benchmark problems from the continuous optimization domain, each for three different dimensionalities. Two robustness definitions: robust regularization and robust composition, are used in the experiments. The meta-modeling techniques are evaluated and compared with respect to the modeling accuracy and the optimal function values. The results clearly show that Kriging, Support Vector Machine and Polynomial regression perform excellently as they achieve high accuracy and the optimal point on the model landscape is close to the true optimum of test functions in most cases.</p>
Documentation artifacts for conversational SRS in chatbots: a systematic review and a new meta-model proposal and validation
<p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation, since there are not so many directions on how to register and test subjective requirements. </p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 studies that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we proposed the conversational integrated map and validated it by conducting a 2-step questionnaire among software practitioners experience in requirements engineering and chatbot requirement's specification.</p> <p>Conclusion: Future studies should investigate if existing artifacts are enough to address all complex aspects of chatbots' specific conversational requirements or require further adaptation. Future studies should investigate specific SRS needs for different types of softwares.</p>
Supplementary Material for Documentation artifacts for conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal
<p>This is a supplementary data of the tertiary systematic literature review conducted in the paper "Conversation-related requirements specification in chatbots: a systematic review and a meta-model proposal".</p> <p>Context: Chatbots are complex applications due to their capacity to engage and maintain a conversation with humans. However, the conversational-related requirements of chatbots are hard to elicit, document, and test. Another challenge is the documentation since there are not so many directions on how to register and test subjective requirements.</p> <p>Methods: We followed systematic literature review (SLR) guidelines and identified 42 relevant papers that address the artifacts used by practitioners to document conversational-related requirements in literature. We also investigated what conversational requirements are addressed in requirements documentation.</p> <p>Results: The main results indicate that UML diagrams, prototypes, tables of requirements, conversational flows, and scenarios are present in most chatbot documentation. Except for UML diagrams, those artifacts are used to document standard requirements or conversational requirements. In those artifacts, context-dependent behavior, assertivity, error handling, and human-like attitude are the most approached conversational requirements in the studies. In sequence, based on our findings, we propose the conversational integrated map, a meta-model solution as documentation of conversational requirements.</p>
Ecore version of the meta-model for information dashboards (v2)
<p>The dashboard metamodel is a M2-model instantiated from Ecore, a M3-model in the four-layer metamodel architecture of OMG. This version includes the addition of a task taxonomy and a goal taxonomy for information visualization.</p>
A literature-based business process meta-model: construction and ontological clarifications
<h1><strong>A literature-based business process meta-model: construction and ontological clarifications</strong></h1> <div> <div> <div> <h3>Greta Adamo, Chiara Di Francescomarino, Chiara Ghidini</h3> </div> </div> </div> <p>This folder contains all the relevant additional documents that have been produced and analysed for this work, e.g. sources, evaluation criteria, and tables, and that have not been included in the paper for a reason of space.</p> <p> </p>
Ecore version of the meta-model for information dashboards (v3)
<p>The dashboard metamodel is a M2-model instantiated from Ecore, a M3-model in the four-layer metamodel architecture of OMG. This version includes the some modifications to better support the definition of dashboards.</p>
A Meta-Model to Support the Migration and Evolution of CI/CD Pipelines
<p> </p> <h1><strong>Reproducibility Package for “A Meta-Model for Reengineering CI/CD Pipelines”</strong></h1> <h2><strong>Abstract</strong></h2> <p>In modern industrial software development, DevOps has become the leading approach for managing highly iterative software production processes. DevOps integrates development and operations activities, with Continuous Integration, Continuous Delivery, and Continuous Deployment (CI/CD) playing a crucial role in ensuring the iterative delivery of high-quality software.</p> <p>CI/CD relies on pipelines composed of various automated activities, often implemented using commercial tools. However, due to the rapid evolution of these tools, CI/CD pipelines frequently require migration to newer versions or entirely different platforms. Since this migration process is predominantly manual, it is both time-consuming and error-prone.</p> <p>To assist software engineers in this challenge, we propose a novel Model-Driven Engineering (MDE) approach to automate the migration of CI/CD pipelines. Inspired by the traditional reengineering horseshoe model, our method abstracts existing CI/CD pipeline artifacts into an intermediate meta-model representation. Using this meta-model, we can generate semantically equivalent pipelines for different CI/CD tools.</p> <p>Our main contribution is a meta-model designed to represent the structure of existing CI/CD pipelines, building the foundation for MDE-based migration.</p> <h2><strong>Contents of the Reproducibility Package</strong></h2> <p>This package is provided inside the <code>reproducibility.zip</code> file, organized into the following folders:</p> <h3><strong>1. <code>devops2</code> – CI/CD Meta-Model</strong></h3> <ul> <li>Our CI/CD meta-model, built using the Eclipse Modeling Framework (EMF).</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF).</li> <li><strong>Usage:</strong> Import into EMF or any Ecore-compatible library..</li> </ul> <h3><strong>2. <code>org.xtext.example.mydsl11</code> – Xtext-Based GitHub Actions Parser</strong></h3> <ul> <li>An Xtext-based DSL for parsing GitHub Actions configuration files.</li> <li>Used in an initial version of our research; the PyEcore parser (included below) provides better results.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Xtext.</li> <li><strong>Usage:</strong> Import into EMF with Xtext installed.</li> </ul> <h3><strong>3. <code>GitHubActionsDataset</code> – Dataset of GitHub Actions Configurations</strong></h3> <ul> <li>A collection of 200 randomly selected GitHub Actions configuration files used to validate our initial GitHub Actions parser.</li> </ul> <h3><strong>4. <code>org.xtext.example.mydsl13</code> – Xtext-Based CircleCI Parser</strong></h3> <ul> <li>An Xtext-based DSL for parsing CircleCI configuration files.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Xtext.</li> <li><strong>Usage:</strong> Import into EMF with Xtext installed.</li> </ul> <h3><strong>5. <code>org.eclipse.acceleo.module.sample7</code> – Acceleo-Based Code Generator</strong></h3> <ul> <li>An Acceleo-based generator that translates our meta-model into GitHub Actions configurations.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Acceleo.</li> <li><strong>Usage:</strong> Import into EMF with Acceleo installed.</li> </ul> <h3><strong>6. <code>codegeneration</code> – Example Configuration Files</strong></h3> <ul> <li>Example configurations used to validate the syntactical correctness of our models.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Acceleo.</li> <li><strong>Usage:</strong> Import into EMF with Acceleo installed.</li> </ul> <h3><strong>7. <code>modelequivalence</code> – Validation of Model Equivalence</strong></h3> <ul> <li>Contains CircleCI scripts used for validating model equivalence, including: <ul> <li>Their representations in our meta-model.</li> <li>The corresponding GitHub Actions configurations generated from these models.</li> </ul> </li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) or any other Ecore-compatible library.</li> <li><strong>Usage:</strong> Import into EMF or any Ecore-compatible library.</li> </ul> <h3><strong>8. <code>casestudy</code> – Migration Case Study (CircleCI → GitHub Actions)</strong></h3> <ul> <li>A case study demonstrating how our meta-model supports migration from CircleCI to GitHub Actions.</li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) and Acceleo.</li> <li><strong>Usage:</strong> Import into EMF with Acceleo installed.</li> </ul> <h3><strong>9. <code>pyecoreparser</code> – PyEcore-Based Parser (GitHub Actions & Travis CI)</strong></h3> <ul> <li>A PyEcore-based parser for extracting CI/CD configurations from GitHub Actions and Travis CI, converting them into our meta-model.</li> <li><strong>Includes:</strong> <ul> <li>The 200 GitHub Actions and Travis CI configuration files used for validation.</li> <li>The 10 randomly selected GitHub Actions and Travis CI configuration files used for comparison with manual modeling.</li> <li>A README file with installation instructions and usage guidelines.</li> </ul> </li> <li><strong>Requirements:</strong> Python, PyEcore library, PyYAML library.</li> <li><strong>Usage:</strong> Follow the README file for setup and usage instructions.</li> </ul> <h3><strong>10. <code>Mapping</code> – Platform-to-Meta-Model Mapping</strong></h3> <ul> <li>A mapping that aligns different CI/CD platforms with our meta-model.</li> </ul> <h3><strong>11. <code>automaticmanualvalidation</code> – Automatic vs. Manual Model Validation</strong></h3> <ul> <li>A set of 20 pipelines (10 from GitHub Actions, 10 from Travis CI) modeled in two ways: <ul> <li>Automatically using the PyEcore parser.</li> <li>Manually by the authors for comparison.</li> </ul> </li> <li><strong>Requirements:</strong> Eclipse Modeling Framework (EMF) or any other Ecore-compatible library.</li> <li><strong>Usage:</strong> Import into EMF or any Ecore-compatible library.</li> </ul>
Evaluation of a Meta-Modeling Platform for AR and VR Using the Example of ArchiMate
<p>Enterprise models are often used to deal with the increasing complexity of enterprise architectures, but their potential is far from being fully exploited. One reason for this is that enterprise modeling is seen as an expert discipline. In order to further exploit the potential, enterprise modeling should evolve from an expert discipline to a common practice. By embedding modeling in everyday work practices, also non-experts in modeling become enabled to engage in the modeling process. Augmented reality can help to achieve this vision. Therefore, a meta-meta-modeling framework for augmented and virtual reality-based conceptual modeling has been developed and implemented in a prototypical tool. By conducting a design science research project, this work evaluated the prototype by using the example of ArchiMate. Subsequently, requirements for the further development of the platform were derived. The results showed that a subset of the ArchiMate standard was successfully implemented in the meta-modeling platform. However, the platform still needs to provide further functionalities in order to fully address the ArchiMate conformance requirements and to mitigate the weaknesses of the platform, especially in the area of usability. It can be concluded that the further development of the platform is crucial in order to offer it to a broader user group, and, therefore, to get closer to the vision of enterprise modeling evolving from an expert discipline to a common practice. </p>
Meta-model for Patagonian grass steppe dynamics - library of transition matrices for steppe dynamics under sheep grazing
<p><span>One of the central problems in ecology is how to scale from small-scale observations and experiments to large-scale patterns and processes. One approach to such upscaling is to use dynamic simulation models, but their application to large scales relevant for management is limited by computational costs, and their outputs are difficult to analyse without a systematic strategy. Our general objective is to propose such a strategy. The idea is to approximate the dynamics of detailed simulation models through a set of states, external drivers, and transition matrices, and then use Markov chain and network analysis of the resulting transition matrices to gain insights into the dynamics of the underlying detailed model. We used the individual-based model COIRON, which simulates the dynamics of semiarid grass steppes in Patagonia (Argentina) under alternative grazing management, as example. Our specific objectives are to identify pathways of degradation and rehabilitation, as well as critical grazing thresholds and early-warning vegetation states to guide sustainable grazing management in these steppes. Our results indicate nonlinear effects of stocking rate and grazing season on steppe dynamics. Markov chain analysis suggests benefits of seasonal over continuous grazing at intermediate stocking rates, and network analysis of recovery and degradation trajectories shows that intermediate stocking rates maximize differences between grazing seasons. Finally, our analysis identified specific vegetation states as early warning signals that indicate a high risk of irreversible vegetation changes. Patagonian grass steppes should ideally be managed with multi-paddock grazing at moderate stocking rates around 0.5 sheep·ha<sup>-1</sup>. The transition matrices summarize the relevant key features of the detailed model for larger scales, and applying Markov and network theory provides a systematic strategy to analyse its dynamics to respond to biological questions, both are often difficult to obtain by direct analysis of the detailed model. </span></p>
Meta-model for Patagonian grass steppe dynamics - library of transition matrices for steppe dynamics under sheep grazing
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Data from: Extending the use of ecological models without sacrificing details: a generic and parsimonious meta-modelling approach
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Meta-models for Software Quality and Its Evaluation: A Systematic Literature Review (Article Pool)
<p>This pdf includes all of the articles that analyzed in the study: "Meta-models for Software Quality and Its Evaluation: A Systematic Literature Review".</p>
Extending the Palladio Meta-Model to Support Memory Hierarchy
<p>This is a general overview of the gathered data.<br> The structure of the data is shown in FullDataProcessingPipeline.pdf</p> <p>Each step and folder contains its own readme.</p> <p>The steps 1 and 2 are combined because the python conversion script is dependent on multiple data sources of the previous steps.<br> Therefore, file paths are dependent on each other.<br> In the Step1and2 folder are also the outputs of "lscpu" and "perf list" for each server.</p> <p>The DataFlow in the diagram should also indicate in which step folder the required data can be found. </p> <p>In general, for all python scripts Python3(we used 3.8.3), pandas and NumPy are required.<br> <br> The project to create the parallelized matrix is located in the Palladio-modeling-memory-bandwidth-on96Cores.zip.<br> At the end of its Readme.MD a description on how to use the ./buildExperimentJars is described. </p>
Systematic Literature Review on Meta-Models in BPM: Material
<p>Material related to the Systematic Literature Review on Meta-Models in Business Process Management</p>
Raw Data of the Survey on the Practitioners' Expectations from the Meta-modeling Tools
<p>Here, the survey questions and the answers of 103 different participants can be found. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.