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118 results for “Software Modelling”
The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts-Figure 2. The UML representation of the artefact as it exists as a metaphor
<p>The accumulation of energy that exists in each ingenious metaphor is progressively released, thus contributing to the transformation of a theoretical promise into effective reality. The artefact successively goes through several maturation stages, as the creator is preoccupied with obtaining an as precise and as close as possible description of the artefact as it exists as a metaphor. The completion of these successive stages is achieved through a methodic abstraction process, while leaving open the possibility to innovate and targeting three main objectives: — broadening the abstraction scope; — adding new details; — detecting and eliminating abstraction errors.</p>
Data Set Used in Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS
<p>This document contains the data set used for the study Combinatorial Modeling and Test Case Generation for Industrial Control Software using ACTS that is currently in submission.</p>
SLDeep: Statement-Level Software Defect Prediction Using Deep-Learning Models on Static Code Features
<p>Software defect prediction (SDP) seeks to estimate fault-prone areas of the code to focus testing activities on more suspicious portions. Consequently, high-quality software is released with less time and effort. The current SDP techniques however work at coarse-grained units, such as a module or a class, putting some burden on the developers to locate the fault. To address this issue, we propose Statement-Level software defect prediction using Deep-learning model (SLDeep). To reify our proposal, we defined a suite of 32 statement-level metrics, such as the number of binary and unary operators used in a statement. Then, we applied as learning model, long short-term memory (LSTM). The significance of SLDeep for intelligent and expert systems is that it demonstrates a novel use of deep-learning models to the solution of a practical problem faced by software developers. We conducted experiments using more than 100,000 C/C++ programs within the Code4Bench. The programs total 2,356,458 lines of code with 292,064 faulty lines. The benchmark comprises diverse set of programs and versions, written by thousands of developers. Therefore, it tends to give a model that can be used for cross-project SDP. In the experiments, our trained model could successfully classify the unseen data with average performance measures 0.945, 0.971, and 0.976 in terms of recall, precision, and accuracy, respectively. These experimental results suggest that SLDeep is effective for statement-level SDP. The impact of this work is twofold. Working at statement-level further alleviates developer’s burden in pinpointing the fault locations. Second, cross-project feature of SLDeep helps defect prediction research become more industrially-viable</p> <p>for more information visit <a href="https://github.com/sldeep/SLDeep">https://github.com/sldeep/SLDeep</a></p>
Supplemental material for: Software System Testing assisted by Large Language Models: An Exploratory Study
<p>This is the supplemental material of the paper titled as “Software System Testing Assisted by Large Language Models: An Exploratory Study” presented at the 36th International Conference on Testing Software and Systems.</p> <p>It contains the raw execution data generated by both models, GPT-4o and GPT-4omini, during the exploratory study. The supplementary material includes the following files:</p> <ul> <li><em>GPT-4ominiRQ1-2ExecutionData.zip</em>: contains the JSON outputs from the OpenAI API for the GPT-4o mini model. Each output is labeled according to the research question number and the corresponding timestamp (for RQ1) or the requested test case (for RQ2), all provided in plain text format.</li> <li><em>GPT-4oRQ1-2ExecutionData.zip</em>: contains the JSON outputs from the OpenAI API for the GPT-4o model. Like the previous file, each output is named in plain text format based on the research question number and timestamp (for RQ1) or the requested test case (for RQ2).</li> </ul> <p>To cite this work: </p> <p>C. Augusto, J. Morán, A. Bertolino, C. de la Riva and J. Tuya, “S<em>oftware System Testing assisted by Large Language Models: An Exploratory Study</em>”, in <em>Testing Software and Systems</em> (pp. 239–255). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-80889-0_17</p>
Urheberrechtsgesetz (UrhG): software and data related definitions depicted using UML class modeling — Release 10
<p>Diagram depicting definitions and conceptual relationships within the German Copyright and Related Rights Act or Urheberrechtsgesetz (UrhG) up to and including the amendments of 28 November 2018. The diagram focuses on software and data related definitions and presents these using Unified Modeling Language (UML) class modeling. Please contact the author is you require the underlying Inkscape SVG vector art.</p>
Reuse of Model Transformations for Propagating Variability Annotations in Annotative Software Product Lines - Evaluation Data
<p>This package contains all data that was produced for and used in the doctoral thesis for evaluating commutativity of propagating annotations in model-driven product lines.<br> This includes the implementation that conducts the evaluation, the measured results, and the input subjects.</p>
Dataset to run the NeuralFRG software for the t-t' Hubbard model on the square lattice "Phys. Rev. Lett. 129, 136402 (2022)"
<p>This hdf5 repository contains the fRG vertices for the t-t' Hubbard model on the square lattice required to reproduce the results shown in the publication</p> <p>Di Sante et al., Phys. Rev. Lett. 129, 136402 (2022)</p> <p>by means of the NeuralFRG software (https://github.com/BITMAPdds/NeuralFRG).</p> <p>A train-test split can be performed with:</p> <p>python3 train_validation_split.py NeuralFRG_train_and_validation_data.h5 --verbose</p> <p>and training can be started with:</p> <p>python3 train.py path/to/file_train.h5 (...) #Additional flags here</p>
Analyzing the Impact of Workloads on Modeling the Performance of Configurable Software Systems (Supplementary Material)
<p>This repository provides supplementary material to the ICSE 2023 paper "Analyzing the Impact of Workloads on Modeling the Performance of Configurable Software Systems". We provide the following material:</p> <p>- The experimental setup, including the performance and measurement scripts.</p> <p>- (Aggregated) measurement data and configurations used in our analysis as well as the raw code coverage measurements.</p> <p>- An interactive dashboard to reproduce and reenact our analyses/findings, re-create all visualizations used in the original paper and those omitted due to space limitations.<br> <br> The repository is structured as follows:<br> <br> - accepted_paper.pdf: Camera-ready version of the original paper for reference.</p> <p>- coverages_raw.tar.gz: Raw coverage reports as compressed CSV files (uncrompressed: ~60 GB)</p> <p>- artifacts_excluding_raw_coverage.zip: aggregated measurement data, interactive dashboard, and experimental setup</p> <p>- README.md: A detailed documentation of all the material provided.</p>
Modelling Guidance in Software Engineering: A Systematic Literature Review
<p>The dataset contains three supporting documents covering the paper selection details (including selection criteria and extracted papers after each round) from three rounds of selection for the literature review titled- Modelling Guidance in Software Engineering: A Systematic Literature Review. There are three excel sheets named: <strong>search_s1</strong>,<strong> search_s2</strong> and <strong>snowballing_bpm</strong>. The file "<strong>search_s1</strong>" contains details of the search with the search string: {("modeling" OR "modelling" OR "model-driven" OR "model-based") AND ("guidelines" OR "training" OR "styles" OR "creation") AND ("software engineering")}. The second file, "<strong>search_s2</strong>", contains details of the search with the string: {("modeling" OR "modelling" OR "model-driven" OR "model-based") AND ("approach" OR "process" OR "method" OR "template") AND ("software engineering")}. The third file, "<strong>snowballing_bpm</strong>", contains the backward and forwards snowballing search results. </p>
AFRP22 Tomographic Model & MTZ Receiver Functions: Data, Software, Plotting
<p>AFRP22 (Boyce et al., 2023), is an adaptively parameterized, absolute P-wave tomographic model for Africa provided with and without crustal correction. Also provided is mantle transition zone Receiver Functions for new stations in the Turkana Depression (data embargo lifted 2024-05-24).</p> <p>Citation:</p> <p>Boyce, A., Kounoudis, R., Bastow, I. D., Cottaar, S., Ebinger, C. J., Ogden, C. S. Mantle Wavespeed and Discontinuity Structure below East Africa: Implications for Cenozoic Hotspot Tectonism and the Development of the Turkana Depression (under revision) Geochemistry, Geophysics, Geosystems.</p> <p>This repository accompanies the publication of the tomographic model on the IRIS-EMC that can be found here: https://ds.iris.edu/ds/products/emc-afrp22/ or with the following doi: <a href="https://doi.org/10.17611/dp/emc.2023.afrp22.1">https://doi.org/10.17611/dp/emc.2023.afrp22.1</a>. Please find AFRP22 and its related files for a detailed description of the distributed model.</p> <p>The AFRP22_RFs_SHARE.tar repository contains:</p> <ul> <li>Plotting: Codes and necessary files to reproduce figures in main manuscript.</li> <li>tomography <ul> <li>Tomography_AFRP22: Implementation of MIT inversion algorithm used to produce AFRP22 including formatted data.</li> <li>Raw_data: Hand-picked and processed seismic data (.SAC) from new African seismic networks.</li> <li>Original_documentation: Documentation for original distribution of inversion code from MIT global seismology group </li> </ul> </li> <li>receiver_functions <ul> <li>Processing codes (SMURFPy) already available at doi: <a href="https://doi.org/10.5281/zenodo.4337257">10.5281/zenodo.4337257</a></li> <li>Raw_data - Data files for P, PP and PKP RFs, CCP stacked volumes produced by SMURFPy code and picked discontinuity files</li> </ul> </li> </ul> <p>The following authors contributed to this work:</p> <p>A. Boyce: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> R. Kounoudis: Department of Earth Science and Engineering, Royal School of Mines, Prince Consort Road, Imperial College London, London, UK.<br> I. D. Bastow: Department of Earth Science and Engineering, Royal School of Mines, Prince Consort Road, Imperial College London, London, UK.<br> S. Cottaar: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK.<br> C. J. Ebinger: Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA 70118, USA.<br> C. S. Ogden: School of Geography and Geology, University of Leicester, Leicester, LE1 7RH, UK.<br> </p>
Proactive Conflict Detection for Collaborative Model-driven Software Engineering (Evaluation Data)
<p>Results of the evaluation for the paper "Proactive Conflict Detection for Collaborative Model-driven Software Engineering"</p>
A Conceptual Model to Support Teaching of Software Engineering Controlled (Quasi-)Experiments - Evaluation of the Concept Model
<p>A Conceptual Model to Support Teaching of Software Engineering Controlled (Quasi-)Experiments - Evaluation of the Concept Model</p>
phyddle: Software for exploring phylogenetic models with deep learning
Open the record for dataset details and reuse information.
Adaptations of Scrum roles in software projects: Survey and Representation Tentative with Feature Models
<p>Vídeo short paper sobre adapatações dos papéis do Scrum</p>
Code-Level Model Checking in the Software Development Workflow -- Replication Package
<p>This experience report describes a style of applying symbolic model checking developed over the course of four years at Amazon Web Services (AWS). Lessons learned are drawn from proving properties of numerous C-based systems, e.g., custom hypervisors, encryption code, boot loaders, and an IoT operating system. Using our methodology, we find that we can prove the correctness of industrial low-level C-based systems with reasonable effort and predictability. Furthermore, AWS developers are increasingly writing their own formal specifications. All proofs discussed in this paper are publicly available on GitHub. All proofs and specifications described in the paper are available, under the Apache 2.0 license, on the GitHub repository located at <a href="https://github.com/awslabs/aws-c-common/">https://github.com/awslabs/aws-c-common/</a> This is the master repository for AWS C Common library, and is in active use by the AWS C Common development team. The description of the contents of this repository are based off commit <code>b0ea9f35df8934f9e03fc3bab3919d55efd69b88</code>, although they are not expected to change significantly in the future.</p>
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Article Pool)
<p>This pdf includes all of the articles that analyzed in the study: "Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review".</p>
Experimental Data for: Research Perspective on Supporting Software Engineering via Physical 3D Models
<p>Experimental data for the experiment presented in the technical report 1507: "Research Perspective on Supporting Software Engineering via Physical 3D Models"</p>
The Effects of Education on Students’ Perception of Modeling in Software Engineering
<p>The attached file accompanies the paper titled "The Effects of Education on Students’ Perception of Modeling in Software Engineering". This file contains both the raw data and summary data from the survey conducted at the three institutions, NAU, BGU, and Concordia.</p>
Filling the Gap: A Tool to Automate Parameter Estimation for Software Performance Models
<p>The data part of this release support the results <br /> presented in the paper <br /> "Filling the Gap: A Tool to Automate Parameter Estimation<br /> for Software Performance Models", by W. Wang, J. F. Perez, and G. Casale, accepted <br /> to QUDOS workshop 2015. </p>
Adjudicating between face-coding models with individual-face fMRI responses: Data and analysis software
<p>Computational model fits to human neuroimaging data. Please see included readme.txt file.</p>
ScienceDex guides
Understand access before you commit
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)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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.