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478 results for “artifact”
SC20 Reproducibility Challenge Artifacts
<p>As a part of the reproducibility initiative at the SC20 conference, students participating in the Student Cluster Competition where given the reproducibility challenge, which is a directed reproducibility exercise that reproduces a paper from the previous year’s conference. At SC20, the paper, <em>MemXCT: memory-centric X-ray CT reconstruction with massive parallelization (</em><a href="https://doi.org/10.1145/3295500.3356220">https://doi.org/10.1145/3295500.3356220)</a>, from SC19 was used. In the directed exercise, students were asked to reproduce performance characteristics of the research on a variety of architectures. This file contains the experimental setup and outputs of their work from the reproducibility challenge. It also includes the direction that students were given to the students for the exercise.</p> <p>The students then published their reports as polished critiques of the reproducibility of the original paper along with a new revision of the original paper, titled <em>MemXCT: Design, Optimization, Scaling, and Reproducibility of X-Ray Tomography</em> <em>Imaging</em> (<a href="https://doi.org/10.1109/TPDS.2021.3128032">https://doi.org/10.1109/TPDS.2021.3128032</a>), in a Special Section of the IEEE Transactions on Parallel and Distributed Systems (TPDS). The paper along with the revised papers are available at <a href="https://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=9696253&punumber=71"> https://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=9696253&punumber=71</a>.</p>
Artifact of "Learning and Programming Challenges of Rust: A Mixed-Methods Study"
<p>This is the artifact of paper "Learning and Programming Challenges of Rust: A Mixed-Methods Study" accepted by ICSE 2022.</p> <p>Please refer to the README for more details. The arifact is also available on <a href="https://github.com/system-pclub/rust-pc">GitHub</a>.</p> <p>When evaluating the artifact with the provided VM, we recommend using Ubuntu as the OS for the host machine to avoid compatibility issues.</p> <p> </p> <p> </p>
Dataset provided with "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction"
<p>Dataset and scripts used in "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction".</p>
Applying Cognitive Principles to Model-Finding Output: The Positive Value of Negative Information (artifact)
<p>This is the artifact associated with the OOPSLA 2022 paper titled "Applying Cognitive Principles to Model-Finding Output: The Positive Value of Negative Information".</p> <p>It includes:</p> <ul> <li>A README file with in-depth instructions for accessing and using all artifact components.</li> <li>The raw and anonymized data for each participant in the quantitative studies.</li> <li>The raw and anonymized audio transcripts for each participant in the qualitative studies.</li> <li>Working versions of every user interface used in the experiments.</li> <li>An experimental model-finder, which is a modified version of the <a href="http://alloytools.org/">Alloy</a> model finder, which demonstrates the "2+1-" visualization mode from the paper.</li> </ul>
Artifact of Infrastructure and Tools in the Context of Deep Statistical Model Checking
<p>This artifact contains all infrastructure, tools, and additional material used in the context of Deep Statistical Model Checking (DSMC). This includes the DSMC implementation in modes of the Modest Toolset, the infrastructure used to perform case studies on DSMC, the environment and scripts in which the scalability study on DSMC has been performed, the integration of DSMC in MoGym, and also the TraceVis tool. The content has partially been covered in artifacts accompanying individual papers on DSMC, but is combined here based on a single infrastructure. Each folder contains its own readme describing how to use the scripts, tools, and infrastructure, often also with concrete instructions on how to execute exemplary experiments.</p>
Artifacts of Smart Selection
<p>These files are the artifacts of Smart Selection:</p> <ol> <li>benchmark_classes.csv: This file shows all Java classes to evaluate Smart Selection.</li> <li>binary.zip: This file contains the binary file that implements smart selection. The detailed introduction can be found in the README.md inside the zip.</li> <li>experimental_data.zip: This file contains the coverage statistics data of the experiments.</li> </ol>
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 "Accumulation Analysis"
<p>The artifact for the paper "Accumulation Analysis", published at ECOOP 2022. It contains the data for our literature survey of papers that use typestate specifications, as well as supporting evidence for miscellaneous claims. See the file `README.md` for details.</p>
Uninvited Guests: Analyzing the Identity and Behavior of Certificate Transparency Bots - Dataset Artifact
<p>Curated dataset of CT bots as described in our paper, "Uninvited Guests: Analyzing the Identity and Behavior of Certificate Transparency Bots".</p>
Artifacts for the 2022 ATVA Paper: SISL: Concolic Testing of Structured Binary Input Formats via Partial Specification
<p>Artifacts for the evaluation of the publication <em>SISL: Concolic Testing of Structured Binary Input Formats via Partial Specification,</em> which will be published as part of the 2022 <em>International Symposium on Automated Technology for Verification and Analysis </em>(<a href="https://atva-conference.org/2022/">ATVA</a>).</p> <p>Usage of the provided artifacts requires installing the SISL tooling first. In this regard, please refer to either the <a href="https://agra-uni-bremen.github.io/sisl/">SISL user manual</a> or the documentation in the <em><a href="https://github.com/agra-uni-bremen/sisl">sisl</a> </em>and <a href="https://github.com/agra-uni-bremen/sisl-vp"><em>sisl-vp</em></a> GitHub repositories. Both <em>sisl</em> and <em>sisl-vp</em> need to be installed to system-wide locations in order to be able to use the scripts provided as part of these artifacts.</p> <p>More information on using the artifacts is available in the README.md file.</p>
VizAPI: Visualizing Interactions between Java Libraries and Clients -- Dataset and Artifacts
<p>This dataset contains all interactions (static and dynamic) across software components that happen when you run unit tests of projects. Interactions include method calls, fields, instantiations, subtyping and annotations.</p>
Artifact: Reflecting on Recurring Failures in IoT Development
<p>Artifact for Reflecting on Recurring Failures in IoT Development</p>
Artifact for ESEC/FSE Paper: "23 Shades of Self-Admitted Technical Debt: An Empirical Study on Machine Learning Software"
<p>Artifact for ESEC/FSE paper entitled "23 Shades of Self-Admitted Technical Debt: An Empirical Study on Machine Learning Software"</p>
Artifact for the Scalability Study of the STTT Paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking"
<p>Scripts and infrastructure for the scalability study on DSMC published in the STTT paper "Analyzing Neural Network Behavior through Deep Statistical Model Checking".</p>
Artifacts for "Did I Vet You Before? Assessing the Chrome Web Store Vetting Process through Browser Extension Similarity"
<p>This record contains the artifacts for the aforementioned paper.</p> <p>All artifacts are provided in <a title="https://parquet.apache.org/" href="https://parquet.apache.org/">Apache Parquet</a> format. We recommend using Python's <a title="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html" href="https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_parquet.html">pandas library</a> to open these files.</p> <p>The following is a summary of the provided artifacts:</p> <ul> <li><code>ground-truth.parquet</code>: Contains pairs of extensions used as ground truth for evaluating the pipeline employed in our research.</li> <li><code>infringing-extensions.parquet</code>: Metadata of vetted extensions labeled by Google and infringing extensions found by our pipeline. Includes the cluster assigned by HDBSCAN and the UMAP 2D coordinates used for visualizations.</li> <li><code>embeddings.parquet</code>: Vector embeddings of infringing extensions generated by our pipeline.</li> </ul>
Dataset for "Is the linear relationship between the slope and intercept observed in field emission S-K plots an artifact?"
<p>The dataset are in text format, the files to analyze and generate the figures are in jupyter format for python with a copy in pdf format.</p> <p>Description of files and folders :</p> <p><strong>I) Analyzed</strong></p> <p>This folder contains Fowler-Nordheim analysis of the I-V data in the folder Data, that where obtained in the publication "All field emission experiments are noisy, ... are any meaningful ?"</p> <p><strong>II) Data</strong></p> <p>This folder contains the different experimental I-V data</p> <p><strong>III) Numerical data<br></strong></p> <p>This folder contains the different noisy I-V data generated by the file "Simul"</p> <p><strong>IV) Fig1</strong></p> <p>This file generates the figure "MySK4.svg" based on the data in the folders <strong>I) Analyzed</strong> and <strong>III) Numerical data</strong></p> <p><strong>V) Fig2</strong></p> <p>This file analyses the data in the folder <strong>II) Data</strong> and then extract the parameters of their SK plot.</p> <p>It gathers the SK plot parameters of several work in the litterature.</p> <p>It plots "Slopm4.svg" and "Intm4.svg".</p> <p><strong>VI) Simul</strong></p> <p>This file generates the different noisy I-V data necessary fir Fig1</p> <p><strong>VII) FigSuppl</strong></p> <p>This file shows the different experimental Fowler-Nordheim plots obtained from the files in the folder <strong>II) Data</strong></p>
Artifacts for paper "Code Refinement with the Assistance of Conversation-Aided Large Language Models"
<p>All the conversation data collected and the source code for our method are inside the uploaded zip file.</p>
Artifact of the paper: Light-weight prediction for improving energy consumption in HPC platforms
<p>Please refer to the <a href="../records/11208389/files/artifact-overview.pdf?download=1&preview=1" target="_blank" rel="noopener">artifact-overview.pdf</a> file in this dataset for instructions to reproduce the experiments we have conducted for this article, or for more context about the article.</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>
Artifact to accompany "An Empirical Examination of Fuzzer Mutator Performance" (ISSTA 2024 article)
<p>This artifact contains scripts and data to supplement the ISSTA 2024 article "An Empirical Examination of Fuzzer Mutator Performance":</p> <p> </p> <p>Over the past decade, hundreds of fuzzers have been published in top-tier security and software engineering conferences. Fuzzers are used to automatically test programs, ideally creating high-coverage input corpora and finding bugs. Modern "greybox'' fuzzers evolve a corpus of inputs by applying <em>mutations</em> to inputs and then executing those new inputs while collecting coverage. New inputs that are "interesting'' (e.g. reveal new coverage) are saved to the corpus. Given their non-deterministic nature, the impact of each design decision on the fuzzer's performance can be difficult to predict. Some design decisions (e.g. "should the fuzzer perform deterministic mutations of inputs'') are exposed to end-users as configuration flags, but others (e.g. "what kinds of random mutations to apply to inputs?'') are typically baked-in to the fuzzer code itself. This paper describes our over 12.5-CPU-year evaluation of the set of mutation operators employed by the popular AFL++ fuzzer including the <em>havoc</em> phase, splicing and \redqueen, exploring the impact of adjusting some of those unexposed configurations.</p> <p>In this experience paper, we propose a methodology for determining different fuzzers' behavioral diversity with respect to branch coverage and bug detection using rigorous statistical methods. Our key finding is that, across a range of targets, disabling certain mutation operators (some of which were previously "baked-in'' to the fuzzer) resulted in inputs that cover different lines of code and reveal different bugs. A surprising result is disabling certain mutators leads to more diverse<strong> </strong>coverage and allows the fuzzer to find more bugs faster. We call for researchers to investigate seemingly simple design decisions in fuzzers more thoroughly and encourage fuzzer developers to expose more configuration parameters pertaining to these design decisions to end-users.</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.