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15 results for “Research Artifacts”
Data Artifact: Rebasing Microarchitectural Research with Industry Traces
<p>Data Artifact of the paper "Rebasing Microarchitectural Research with Industry Traces", published at the 2023 IEEE International Symposium on Workload Characterization. It includes the original CVP-1 traces used in the paper.</p><p><i>Note</i>: the improved converted traces used in the paper are available at https://doi.org/10.5281/zenodo.10199624.</p><p><i>Abstract</i>: Microarchitecture research relies on performance models with various degrees of accuracy and speed. In the past few years, one such model, ChampSim, has started to gain significant traction by coupling ease of use with a reasonable level of detail and simulation speed. At the same time, datacenter class workloads, which are not trivial to set up and benchmark, have become easier to study via the release of hundreds of industry traces following the first Championship Value Prediction (CVP-1) in 2018. A tool was quickly created to port the CVP-1 traces to the ChampSim format, which, as a result, have been used in many recent works. We revisit this conversion tool and find that several key aspects of the CVP-1 traces are not preserved by the conversion. We therefore propose an improved converter that addresses most conversion issues as well as patches known limitations of the CVP-1 traces themselves. We evaluate the impact of our changes on two commits of ChampSim, with one used for the first Instruction Championship Prefetching (IPC-1) in 2020. We find that the performance variation stemming from higher accuracy conversion is significant.</p>
Research Artifact: How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem
<p><strong>Research Artifact: How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem</strong></p> <p><a href="https://github.com/yusufsn/EclipseForumData">https://github.com/yusufsn/EclipseForumData</a></p> <p>This is a research artefact for the paper: <strong>How are Project-Specific Forums Utilized? A Study of Participation, Content, and Sentiment in the Eclipse Ecosystem</strong>. This artifact is a repository consisting of collected dataset including (i) 289,061 threads, (ii) 216,864 extracted links from threads, (iii) 2,170 contributors, and the results of our qualitative analysis, (i) 1,142 manually annotated type of discussion and (ii) 1,142 manually annotated sentiment analysis. This artefact aims to enable researchers to replicate our mixed-methods quantitative results of the paper and reuse the dataset for further software engineering research.</p> <p>Contents</p> <ul> <li>dataset: <ul> <li><code>289061_threads.csv.zip</code> - 289,061 collected main threads of all users without post from webmaster (.zip format)</li> <li><code>216864_links.csv.zip</code> - 216,864 extracted links from collected threads (.zip format)</li> <li><code>2170_contributors.csv</code> - 2,170 list of contributions (.csv format)</li> </ul> </li> <li>Results of manual analysis: <ul> <li><a href="https://docs.google.com/spreadsheets/d/e/2PACX-1vQoRpcbrV66OEB4vaCG9Njq65zW7XpLRITYG3BlUOoa_DmeOKcdQIgYJ8y2aSlmL3y9bCUhjpP3rYmT/pubhtml">Manual annotation of discussion type</a></li> <li><a href="https://docs.google.com/spreadsheets/d/e/2PACX-1vQfyvsP1Zq3b9p_BYwkkgYXuEePJB6sIroC47jOUMcR5P8t7DCJFNpOJD565SPgqi--L3AAIQWs2kC5/pubhtml">Manual annotation of sentiment analysis</a></li> </ul> </li> </ul> <p>Authors</p> <ul> <li><a href="https://yusufsn.github.io/">Yusuf Sulistyo Nugroho</a></li> <li><a href="https://syful-is.github.io/">Syful Islam</a></li> <li>Keitaro Nakasai</li> <li><a href="https://ifrazrehman.github.io/">Ifraz Rehman</a></li> <li><a href="https://hideakihata.github.io/">Hideaki Hata</a></li> <li><a href="https://raux.github.io/">Raula Gaikovina Kula</a></li> <li><a href="https://cs.uwaterloo.ca/~m2nagapp/">Meiyappan Nagappan</a></li> <li><a href="https://matsumotokenichi.github.io/">Kenichi Matsumoto</a></li> </ul>
Supplemental Material: Research Artifacts for Human-Oriented Experiments in Software Engineering: An ACM Badges–driven Structure Proposal
<p>This Research Artifact contains supplemental material from the study: "Supplemental Material: Research Artifacts for Human-Oriented Experiments in Software Engineering: An ACM Badges–driven Structure Proposal". The supplementary material contains:</p> <ol> <li>The list of the 106 primary studies classified by journals.</li> <li>The list of the 12 research artifacts classified by conferences.</li> <li>The .xlsx file of the dataset used to analyze the RQs.</li> <li>The .xlsx file of the dataset used to analyze the research artifacts problems (Table 3). </li> <li>The list of the figures published in the scientific article.</li> </ol>
Research data, sources and documents for thesis on Exploring Complexity Metrics for Artifact-Centric Business Process Models
<p>Research data, sources and documents for thesis on Exploring Complexity Metrics for Artifact-Centric Business Process Models This repository contains the supplemental material for the <a href="https://pqdtopen.proquest.com/pubnum/10759956.html">thesis "Exploring Complexity Metrics for Artifact-Centric Business Process Models" by Marin, Mike A., Ph.D., University of South Africa (South Africa), 2017.</a></p>
Research Artifact: Dockerfile Meta-Maintenance for Updating Multiple Version-Pinned Packages
<p>This is a research artifact for the paper "Dockerfile Meta-Maintenance for Updating Multiple Version-Pinned Packages". This artifact is a data repository including a list of studied 7,914 repositories on GitHub, 17,139 dockerfiles, 363 repositories containing comparable package sets, and 335 repositories that provide the packages included in the identified 385 package sets.</p>
Artifact for research regarding Ranged Program Analysis
<div> </div> <div>This artifact is a summary of data regarding ranged program analysis.</div> <div>It contains the most important results from the following three papers:</div> <p> </p> <div>- Jan Haltermann, Marie-Christine Jakobs, Cedric Richter, and Heike Wehrheim. “Parallel Program Analysis via Range Splitting”. In: Proc. FASE. Vol. 13991. 2023, pp. 195–219. doi: 10.1007/978-3-031-30826-0_11.</div> <div>- Jan Haltermann, Marie-Christine Jakobs, Cedric Richter, and Heike Wehrheim. “Ranged Program Analysis via Instrumentation”. In: Proc. SEFM. Vol. 14323. 2023, pp. 145–164. doi: 10.1007/978-3-031-47115-5_9.</div> <div>- Jan Haltermann, Marie-Christine Jakobs, Cedric Richter, and Heike Wehrheim. “Parallel program analysis on path ranges”. In: Sci. Comput. Program. 238 (2024). doi: 10.1016/j.scico.2024.103154.</div> <p> </p> <div>More precisely, it contains all the results from the PhD-thesis of Jan Haltermann.</div> <div>In more detail, it contains all experimental data collected during the experiments, the run-configs to re-run the experiments, the instructions on how to regenerate the BenchExec tables that are used for analysis of the data, and the scripts to reconstruct all the findings from the thesis.</div> <p> </p> <div>It is structured as follows:</div> <div>- `configs` contains all the BenchExec run configs needed to rerun the experiments.</div> <div>- `data` contains the raw data collected during the experiments.</div> <div>- `figures` contains the generated figures</div> <div>- `macros` contains the macros for the Latex document with the evaluation results</div> <div>- `overview-table.xml` is needed to generate the BenchExec result tables.</div> <div>- `scripts` contains the scripts to analyze the results and generate the figures</div> <div>- `tables` contains the tables generated using the raw data and are used for analyzing the results</div> <div>- `used-tools` contains the executables of the tools used for the evaluation (in this case only CPAchecker)</div> <div>- `validation_of_incorrect_results.zip` contains the results of the manual analysis of the incorrect alarms generated, as explained in the paper.</div> <p> </p> <div>The version of CoVeriTeam used can be found [on GitLab](https://gitlab.com/sosy-lab/software/coveriteam/-/tree/rangedAnalysis?ref_type=heads) </div> <div>The latest source code of CPAchecker used can be found [on GitLab](https://gitlab.com/sosy-lab/software/cpachecker/-/tree/ranged_program_analysis)</div> <p><br><br><br><br></p>
Comparing the Use of Research Resource Identifiers and Natural Language Processing for Citation of Databases, Software and Other Digital Artifacts
<p><strong>The Research Resource Identifier was introduced in biomedicine in 2014 to more precisely identify the reagents and tools used in published biomedical research and to track use of tools across the breadth of the biomedical literature. The current RRID specification covers key biological and digital resources. Authors are instructed to include an RRID after the first mention of any resource used. RRIDs are designed to be easy to find using a full text search search engine. </strong></p> <p><strong>The published data sets were used in our comparative study where comparing the output of our RRID curation workflow with the outputs of automated text mining systems that have been used to identify mentions of resources in the text of publications. All files in tab-separated format (tsv). </strong></p> <p><strong>Scibot.tsv: Records of the RRID curation workflow using SciBot. </strong></p> <p>Each record shows that a resource RRID was identified in paper PMID with curator tags (Tag1, Tag2, both optional)</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Tag1: Curator tags (optional)</p> <p> Tag2: Additional curator tags (optional)</p> <p><strong>rdwsorted.tsv: Records of the output from RDW, a text mining software. </strong></p> <p>RDW identifies mentions of research resources in papers. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p><strong>rridbyrdw05282019.tsv: Records of the output of the RRID-by-RDW in RDW. </strong></p> <p>RRID-by-RDW is a component in RDW that identifies mentions of research resources in papers by matching patterns of RRID specifications. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Context: Snippet where the RRID was found</p> <p><strong>resource_metadata20190418.tsv: Metadata of RRIDs</strong></p> <p>This file contains metadata of resources and their RRIDs. See file header for column definitions.</p> <p><strong>RRIDCUR-definitions.tsv: Definitions of curator tags used in Scibot.tsv.</strong></p> <p><strong> </strong>tag: Tag name</p> <p> definition: Definition of the tag</p>
Comparing the Intensity of Variability Changes in Software Product Line Evolution - Related Research Artifacts
<p>This archived open repository contains open science material related to the following submission to the <a href="https://www.journals.elsevier.com/journal-of-systems-and-software/call-for-papers/software-reuse-for-the-next-generation">Journal of Systems and Software (JSS) special issue <em>Software Reuse for the Next Generation</em></a>:</p> <p>C. Kröher, L. Gerling, K. Schmid, <em>Comparing the intensity of variability changes in software product line evolution</em>, Journal of Systems and Software. Submitted November 2022.</p> <p>The paper presents the application of a fine-grained, variability-centric analysis approach to four different software product lines: <a href="https://github.com/torvalds/linux">Linux kernel</a>, <a href="https://github.com/coreboot/coreboot">coreboot firmware</a>, <a href="https://github.com/mirror/busybox">BusyBox UNIX utilities</a>, and <a href="https://sourceforge.net/projects/axtls/">axTLS embedded SSL</a>. The approach is based on the differentiation between artifact-specific and variability information in code, build, and variability model artifacts to identify the intensity (the frequency and the amount) with which developers change variability information in practice.</p> <p>In order to complement the results presented in the submission and support reproducibility as well as reuse, the following artifacts are available:</p> <ul> <li><strong>JSS-VM_2022-11-01.zip</strong>: a compressed archive containing the virtual machine in which the analysis was executed. This virtual machine was created with <a href="https://www.vmware.com/content/vmware/vmware-published-sites/us/products/workstation-player/workstation-player-evaluation.html.html">VMware Workstation 16 Player</a> based on <a href="https://ubuntu.com/download/desktop">Ubuntu Desktop 22.04.1</a> (username and password: jss). It provides all installed software, configuration files, data sets, and results as described in the submission. Download, extract, and start the virtual machine to access the detailed description of its content and usage on the desktop.</li> <li><strong>ComAnI-Applications.zip</strong>: a compressed archive containing the technical realization of the analysis approach and the configuration files used to apply it to the individual software product lines. This includes: <ul> <li><em>ComAnI_Guide.pdf</em>: the guide explaining the application and its usage in general</li> <li><em>ComAnI.jar</em>: the Java executable archive file representing the main application for starting an analysis</li> <li><em>ComAnI-PS.jar</em>: a modified version of the previous Java executable archive file, which provides the number of code, build, and variability model artifacts as specified by the regular expressions of a given configuration file as well as their total number of lines based on the current state of a repository only (no history)</li> <li><em>DeadCodeChangeAnalyzer.jar</em>: a commit analyzer plug-in for detecting changes relevant to dead code detection (not used in the submission)</li> <li><em>GitCommitExtractor.jar</em>: a commit extraction plug-in for extracting commits from Git repositories</li> <li><em>SvnCommitExtractor.jar</em>: a commit extraction plug-in for extracting commits from Subversion (SVN) repositories</li> <li><em>VariabilityChangeAnalyzer.jar</em>: a commit analyzer plug-in for detecting changes to artifact-specific and variability information in code, build, and variability model artifacts (realization of the commit analysis process described in the submission)</li> <li><em>busybox-commit-list</em>: a plain text file containing a subset of the BusyBox commits as required by the application to extract and analyze only those parts of its entire history relevant for the submission (quote from the submission: <em>"For BusyBox, we had to further exclude commits before the complete migration to Kbuild and after the introduction of a script for extracting variability model information from code artifacts, which initiated defining variability information of the variability model as part of comments in code artifacts. This mixing of information and artifact types is not supported by our tooling"</em>)</li> <li><em>axtls.properties</em>: the configuration (file) defining the required properties for extracting and analyzing commits of the axTLS history</li> <li><em>busybox.properties</em>: the configuration (file) defining the required properties for extracting and analyzing all commits of the BusyBox history</li> <li><em>busybox-subset.properties</em>: the configuration (file) defining the required properties for extracting and analyzing the commits specified in the busybox-commit-list file only, resulting in the respective subset of the BusyBox history</li> <li><em>coreboot.properties</em>: the configuration (file) defining the required properties for extracting and analyzing all commits of the coreboot history</li> <li><em>linux.properties</em>: the configuration (file) defining the required properties for extracting and analyzing all commits of the Linux kernel history</li> <li><em>template.properties</em>: the configuration (file) template including descriptions of each property and its valid values</li> </ul> </li> <li><strong>ComAnI-Results.zip</strong>: a compressed archive containing the (raw) analysis results from applying the approach via its realization as provided by the previous archive to each of the software product lines. Hence, for each subject, a directory with the respective name exist, which in turn contains the following artifacts: <ul> <li><em>VariabilityChangeAnalyzer-Results_2022-09-[…]</em>: the directory containing the respective analysis results and some automated visualizations. Please note that some files are not correctly labeled, .e.g. some files for axTLS and BusyBox are prefixed with "coreboot", while containing the correct data for the respective subjects. Further, for BusyBox, two directories exist, which the additional readme-file explains</li> <li><em>project-size-trace.txt</em>: the complete trace from starting ComAnI-PS.jar (see description above) with a specific configuration file to its final output</li> </ul> </li> <li><strong>JSS23_Extended-Evolution-Analysis_Statistics.ods</strong>: a <a href="https://www.libreoffice.org/discover/calc/">LibreOffice Calc</a> spreadsheet containing data derived from the raw ones of the ComAnI-Results.zip and the respective visualizations as presented in the submission. Further, some sheets include additional data preparations used to write certain parts of the result and discussion sections.</li> </ul> <p>The additional <strong>LICENSE</strong> file defines <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache License Version 2.0, January 2004</a> to apply for all artifacts in this repository.</p> <p>This work is partially supported by the Evoline project, funded by the DFG (German Research Foundation) under Priority Programme SPP 1593 and by the ITEA3 project REVaMP², funded by the BMBF (German Ministry of Research and Education) under grant 01IS16042H. Any opinions expressed herein are solely by the authors and not of the DFG or BMBF.</p>
CoCon: A Data Set on Combined Contextualized Research Artifact Use
<p>CoCon is a large graph data set reflecting the combined use of research artifacts, contextualized in academic publications’ full-text. It comprises 35 k artifacts (data sets, methods, models, and tasks) and 340 k publications.</p> <p>The data set is generated from <a href="https://github.com/paperswithcode/paperswithcode-data">Papers With Code</a> and <a href="https://github.com/IllDepence/unarXive">unarXive</a>.</p> <p>You can find a Python package for loading the data as a NetworkX or Pytorch Geometric graph <a href="https://github.com/IllDepence/contextgraph">in this GitHub repository</a><br> </p>
Artifact: SoK: Evaluations in Industrial Intrusion Detection Research
<p>This collection resembles the artifact of our publication "SoK: Evaluations in Industrial Intrusion Detection Research," published in the Journal of Systems Research 2023, in which we performed a systematic mapping study on the literature of Industrial Intrusion Detection Systems (IIDSs). Within this paper, we systematically analyzed the evaluation methodologies of this field to understand the current state of industrial intrusion detection research. This artifact contains our data extracted from the 609 publications under consideration in the survey. For further information, please refer to the respective publication.</p>
Dataset for paper: Research Artifacts in Secondary Studies: A Systematic Mapping in Software Engineering
Open the record for dataset details and reuse information.
Research Artifacts - An Anomaly-based Approach for Detecting Modularity Violations on Method Placement
<p>This record contains research artifacts of our paper.</p> <p>The description of each file is as follows.</p> <ul> <li>case_study.xlsx: a spreadsheet that contains a list of our detected cases and results of our manual inspection</li> <li>java-large+.tar.gz: a preprocessed dataset used for training our detection model</li> <li>model_parameters.ckpt.gz: trained parameters and a word dictionary of our neural network model</li> <li>src.tar.gz: source code of our neural network model written in Python and a dataset preprocessor written in Java</li> </ul>
Research Artifact: 9.6 Million Links in Source Code Comments
<p>This is a research artifact for the ICSE'19 paper <strong>9.6 Million Links in Source Code Comments: Purpose, Evolution, and Decay</strong>. This artifact is a data repository including all 9,654,702 links associated with the information of languages and comment location (GitHub links including account names, repository names, commit hashes, file paths, and line numbers). The purpose of this artifact is enabling researchers to replicate our mixed-methods quantitative results of the paper, and to reuse our around 9.6 million links in source code comments for further software engineering research.</p>
Research Artifact for paper "Decompiling x86 Deep Neural Network Executables"
<p>Research Artifact for USENIX Security 2023 paper "Decompiling x86 Deep Neural Network Executables"</p>
Research artifacts from "Hardening and Speeding Up Zero-interaction Pairing and Authentication"
<p>This record contains <strong>research artifacts (data and results)</strong> from the paper "Hardening and Speeding Up Zero-interaction Pairing and Authentication" by Mikhail Fomichev, Timm Lippert, and Matthias Hollick in Proceedings of 2023 International Conference on Embedded Wireless Systems and Networks (EWNS' 23). </p> <p>The dataset contains raw data of illuminance, RGB, CO2, and TVOC sensors recorded in two scenarios: Home and Office. While the results include computed entropy and dynamic time warping (DTW) distances of the above data collected by colocated (in the same Home or Office) and non-colocated devices in our two scenarios. </p> <p>The results additionally include entropy of audio data recorded by the colocated and non-colocated devices as well as audio similarity of such recordings. For details about audio similarity metrics and further information, see <a href="https://arxiv.org/pdf/2306.04458.pdf">our paper</a>. </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.