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478 results for “artifact”
ATC'24 Artifact Evaluation of Monarch
Open the record for dataset details and reuse information.
Artifacts of the paper "DogeFuzz: A Simple Yet Efficient Grey-box Fuzzer for Smart Contracts."
<div> <h1>Artifacts of the paper "DogeFuzz: A Simple Yet Efficient Grey-box Fuzzer for Smart Contracts."</h1> <a href="https://github.com/faustocarva/dogefuzzsbes_artifact/tree/main#artifacts-of-the-paper-dogefuzz-a-simple-yet-efficient-grey-box-fuzzer-for-smart-contracts"></a></div> <p>This repository contains artifacts for the experiments in the paper DogeFuzz: A Simple Yet Efficient Grey-box Fuzzer for Smart Contracts. The <code>dataset</code> directory includes 2 benchmarks: Bench72 and Bench500. The other directories are <code>dogefuzz-results</code> for processing Dogefuzz-related result files and <code>Smartian-Artifact</code> for processing Smartian-related replication studies.</p>
Artifact supplement for 'An Oracle-Guided Approach to Constrained Controller Synthesis Under Uncertainty'
<p>Artifact supplement for submission "An Oracle-Guided Approach to Constrained Controller Synthesis Under Uncertainty".</p> <p>The attached file includes:</p> <ul> <li>docker image containing our tool and the models considered in our article</li> <li>log files from the conducted experiments</li> </ul>
Software artifacts corresponding to the paper "Pragmatic Random Sampling of the Linux Kernel: Enhancing the Randomness and Correctness of the conf Tool"
<div><strong>Software artifacts corresponding to the paper "Pragmatic Random Sampling of the Linux Kernel: Enhancing the Randomness and Correctness of the conf Tool"</strong></div> <div> </div> <div> <div> <div>This repository is organized in two main folders:</div> <br> <div>1. <strong>randconfig+</strong> includes the source code of our tool randconfig+, which improves the randomness and correctness of the conf tool (a built-in Linux kernel tool for generating random samples of kernel configurations). It has two subfolders:</div> - <strong>source</strong>: includes the source code of randconfig+. <div> - <strong>bin</strong>: includes the compiled version of randconfig+.</div> <br> <div>2. <strong>experimental_validation</strong> includes the experimental validation of randconfig+ reported in the paper.</div> - <strong>systems</strong>: includes the 10 Linux Kernel versions analyzed in the paper. <div> - <strong>scripts_for_sample_generation</strong>: Bash shell scripts to generate the data.</div> <div> - <strong>data</strong>: includes the following data generated by the experimental validation:</div> <div> + Generated samples (subfolder configuration_samples). The samples are available in two formats: CSV and <a href="https://r4ds.hadley.nz/arrow">Apache Arrow</a>.</div> <div> + Entropies and number of distinct values per configuration option.</div> <div> + Data regarding the correctness of the samples and the time spent to generate them (subfolder correctness_and_runtime).</div> <div> - <strong>statistical_analysis</strong>: R scripts to perform the statistical analysis of the data (subfolder statistical_analysis).</div> <div> - <strong>plots</strong>: graphs produced from the data statistical analysis.</div> </div> </div>
Artifact for "Towards Deterministic Compilation of Binary Decision Diagrams From Feature Models"
<p>This artifact supplies the tooling and original data used in the evaluation of the corresponding submission #52 `Towards Deterministic Compilation of Binary Decision Diagrams From Feature Models'' accepted at SPLC'24. The artifact allows reproducing all the figures and tables of the submission. In addition, the artifact allows for easy replicating of the results for additional input models.</p>
ProB - B2SAT Artifact for FM'2024
<p>This artifact accompanies the paper "B2SAT: A Bare-Metal Reduction of B to SAT" to appear in the proceedings of the FM'2024 conference. The artefact enables one to reproduce the results in the paper.</p> <p>B2SAT is a new SAT backend for the B-Method to enable new applications of formal methods.<br>The new backend interleaves low-level SAT solving with high-level constraint solving.<br>The backend is integrated into ProB, not as a general purpose backend, but<br>as a dedicated backend for solving hard constraint satisfaction and optimisation problems<br>on complex data. <br>A documentation page for B2SAT is available at:<br> <a href="https://prob.hhu.de/w/index.php?title=B2SAT">https://prob.hhu.de/w/index.php?title=B2SAT</a></p> <p>To experiment or reproduce the results download and unpack this artifact (<code>b2sat_artifact.tgz</code>) then you can type the following commands to see help (make help), install ProB inside the folder (make install_prob), run a simple test (make simple) and then re-run the experiments:</p> <p><code>cd benchmarks<br>make help<br>make install_prob<br>make simple<br>make bench<br>make cdclt</code></p> <p>The Makefile of the artifact will download the version 1.13.1-beta1 of ProB for your platform. Note: on macOS you may to have to run make post_install after make install_prob. You also need to have Java 8 or newer installed on your machine.</p> <p>The artifact's Makefile works on macOS and Linux. Even though ProB also works on Windows this artifact is <strong>not</strong> designed for Windows (you will need to adapt the Makefile or by extracting the zip archive manually using Explorer and updating the PATH of your shell manually using `export PATH=$PATH:PROB`; there may also be issues launching the Kodkod backend on Windows).</p> <p>The Linux version of ProB is also available as another artifact:<br> <a href="../records/12166295">ProB Linux 1.13.1.beta1 Zenodo artifact</a> (https://zenodo.org/records/12166295).</p> <p>This version of ProB can be run inside the ifm2022 virtual box machine (<a href="../records/5794839">https://zenodo.org/records/5794839</a>).</p> <p>These are the steps needed to run the benchmarks in the <a href="../records/5794839">iFM2022 virtual machine:</a></p> <p><br><code>sudo apt install curl</code><br><code>mkdir b2sat</code><br><code>cd b2sat</code><br><code>curl -L https://zenodo.org/records/12180216/files/b2sat_artifact.tgz?download=1 -o artifact.tgz</code><br><code>shasum -a 256 artifact.tgz</code><br><code>tar -xvzf artifact.tgz</code><br><code>cd benchmarks</code><br><code>make install_prob</code><br><code>make simple</code><br><code>make bench</code><br><code>make cdclt</code><br><br></p> <p>Note: sometimes on Linux Z3 unfortunately runs out of memory or generates the assertion violation UNEXPECTED CODE WAS REACHED during make bench. You can run <code>make sat</code> to just run B2SAT on the benchmark models. Depending on your Linux system you may also have to set the dynamic library path before running make bench:</p> <p><code>LD_LIBRARY_PATH=PROB/ProB/lib/ make bench</code></p> <p>ProB is based on research and implementation efforts by many people, see <a href="https://prob.hhu.de/w/index.php?title=Team">https://prob.hhu.de/w/index.php?title=Team</a>.</p> <p>To cite ProB you can use this article: <a href="http://dx.doi.org/10.1007/s10009-007-0063-9">http://dx.doi.org/10.1007/s10009-007-0063-9</a>.<br>The original article was published in 2003 at FME: <a href="https://rdcu.be/cKLIv">https://rdcu.be/cKLIv</a>.</p>
Artifact for InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference
<p>This is the artifact for the paper,</p> <p>InferType: A Compiler Toolkit for Implementing Efficient Constraint-Based Type Inference.</p> <p> </p> <p>It contains:</p> <p>+ infertype.tar: a saved docker image</p> <p>+ artifact.zip: this artifact including all the materials</p> <p>+ readme.md: a document for the included materials</p>
Artifact for "Understanding Misconfigurations in ROS: An Empirical Study and Current Approaches"
Open the record for dataset details and reuse information.
Artifacts to Reproduce "16 Years of SPEC Power: An Analysis of x86 Energy Efficiency Trends"
<p>Dataset to reproduce the paper "16 Years of SPEC Power: An Analysis of x86 Energy Efficiency Trends".</p> <p>Contains raw & processed data, as well as scripts for data sourcing, extraction and processing, including figure generation.</p> <p>Please note that different licenses apply to different parts of the archive, as the included raw results from SPEC are subject to SPEC-defined rules.</p> <p>For additional information please refer to the readme.</p>
FlakyDoctor Artifact
<p>This is the artifact for the paper "Neurosymbolic Repair of Test Flakiness" at ISSTA2024. </p> <p>Please refer to `README.md` for more details. The same content can be found at <a href="https://github.com/dserfe/FlakyDoctor">https://github.com/dserfe/FlakyDoctor</a></p> <div><br><br></div>
Sequence and assembly artifacts: GRCr8 and Illumina reads from BN
<p>GRCr8 rat reference genome assembly polishing data.</p>
Artifact for 'Unfolding State Variables Improves Model Checking'
<p>This is a reproduction package for the experiments that were performed as part of the work 'Unfolding State Variables Improves Model Checking'.</p>
IRFuzzer artifacts
<div> <div># Appendices and artifacts</div> <br> <div>This file describes all the bugs we found, and the raw data (artifacts) in our evaluation.</div> </div> <div>## Edit for V3</div> <div>`<a href="13139630" target="_blank" rel="noopener noreferrer">fuzzing.baseline.tar.gz</a>` includes the new evaluation we did for IRFuzzer. For baseline fuzzers (AFL++, FuzzMutate), check `<a href="../records/10858882/files/baseline-fuzzing.tar.gz?download=1">baseline-fuzzing.tar.gz</a>` in V1/V2.</div> <div>`<a href="13139630" target="_blank" rel="noopener noreferrer">e2e.fuzzing.irfuzzer.tar.gz</a>` include the new evaluation we did for IRFuzzer.</div> <div><br> <div>## IRFuzzer</div> <br> <div>We have included the source code of IRFuzzer in <a href="13139630" target="_blank" rel="noopener noreferrer">IRFuzzer.tar.gz</a> for easier reproduction.</div> <div>You can download it and there is a README inside for guides on how to reproduce the data.</div> However, this code may be outdated when you see the artifact, please refer to our open-sourced version for reference. Source link will be in the paper by the time of publication.</div> <div><br> <div>## Metadata of evaluations</div> <br> <div>This bugs we found is listed in a separate file BugCount.xlsx.</div> <div>The coverage data of both baseline fuzzing and end-to-end fuzzing, the bugs we found can be found in metadata directory.</div> <br> <div>## Baseline evaluation</div> <br> <div>The coverage data is summarized in a separate file Baseline.xlsx.</div> <div>The seeds seeds.tar.gz we used can be downloaded.</div> <div>We randomly selected 92 seeds from LLVM's unit testing.</div> <div>The raw data baseline-fuzzing.tar.gz of our baseline evaluation can be downloaded.</div> <br> <div>## End to end fuzzing</div> <br> <div>The coverage data is summarized in a separate file End-to-end.xlsx.</div> <div>End to end fuzzing has extremely large data, as GrayC and CSmith tend to generate large amount of C files.</div> <div>We split raw data into three pieces: IRFuzzer e2e.fuzzing.irfuzzer.tar.gz.</div> <div>We downloaded GrayC's artifact from [here](https://zenodo.org/record/7978251)</div> <div>CSmith occured large amount of C files and can't be uploaded, thus the data is available upon request.</div> </div>
Verification Algorithms for Automated Separation Logic Verifiers (Artifact)
<p>This is the artifact for the CAV 2024 paper "Verification Algorithms for Automated Separation Logic Verifiers". For more information, see the description of the artifact in "artifact_overview.pdf". The data in the artifact (minus the implementations of the benchmarked algorithms) can also be found on GitHub: https://github.com/marcoeilers/CAV24-data.</p>
Replication Package for: "Redundancy-free Analysis of Multi-revision Software Artifacts"
<p>Researchers often analyze several revisions of a software project to obtain historical data about its evolution. For example, they statically analyze the source code and monitor the evolution of certain metrics over multiple revisions. The time and resource requirements for running these analyses often make it necessary to limit the number of analyzed revisions, e.g. by only selecting major revisions or by using a coarse-grained sampling strategy, which could remove significant details of the evolution. Most existing analysis techniques are not designed for the analysis of multi-revision artifacts and they treat each revision individually. However, the actual difference between two subsequent revisions is typically very small. Thus, tools tailored for the analysis of multiple revisions should only analyze these differences, thereby preventing re-computation and storage of redundant data, improving scalability and enabling the study of a larger number of revisions. In this work, we propose the Lean Language-Independent Software Analyzer (LISA), a generic framework for representing and analyzing multi-revisioned software artifacts. It employs a redundancy-free, multi-revision representation for artifacts and avoids re-computation by only analyzing changed artifact fragments across thousands of revisions. The evaluation of our approach consists of measuring the effect of each individual technique incorporated, an in-depth study of LISA's resource requirements and a large-scale analysis over 7 million program revisions of 4,000 software projects written in four languages. We show that the time and space requirements for multi-revision analyses can be reduced by multiple orders of magnitude, when compared to traditional, sequential approaches.</p>
Artifact Evaluation for AT-Opt PACT 2018
<p>Artifact Evaluation Reproduction for "Optimizing Remote Data Transfers in X10", PACT 2018.</p> <p>------------------------------------------------------------------------------------------</p> <p>This repository contains artifacts and source codes<br> to reproduce experiments from the PACT 2018 research paper<br> titled "Optimizing Remote Data Transfers in X10"</p> <p>Our evaluations are done on Linux based Operation system.</p> <p><br> Hardware pre-requisities:<br> -------------------------<br> We recommend the following architectures any of the following architectures:<br> * An Intel System with two nodes, each node having 32 cores (32 CPUs). Total 64 cores.<br> * An AMD System, with two nodes, each node having 16 cores (16 CPUs). Total 32 cores.<br> </p> <p><br> Software pre-requisites:<br> ------------------------<br> 1) For Evaluation using Virtual Machine required software are already installed in the provided image.<br> A VirtualBox (from Oracle) is required to install the image and do evaluation.</p> <p>2) For Manual evalution, the below required software needs to be installed in the system.<br> * g++ (preferred version 5.4.0)<br> * Apache ant software (preferred version 1.9.6 )<br> * Java software<br> - preferred jvm jdk1.8.0_151 (or) java-8-oracle<br> - After installation, set JAVA_HOME path in ~/.bashrc to point to the installed JAVA bin</p> <p><br> Benchmarks:<br> -----------<br> 1) Taken from IMSuite Benchmark kernels (http://www.cse.iitm.ac.in/~krishna/imsuite/). Already, included in the package.</p> <p><br> Installation, Execution and Validation of results:<br> --------------------------------------------------<br> 1) For Evaluation using Virtual Machine see README.md file in ./AE/VM folder</p> <p>2) For Evaluation using Manual method see README.md file in ./AE/Manual folder</p> <p>3) For Evaluating AT-Opt technique againt varing input sizes (not discussed in the paper), look at<br> web page http://www.cse.iitm.ac.in/~krishna/imsuite/. In the ./AE/Manual folder, "x10-base" folder is<br> the baseline compiler (after bulid) and "x10-atOpt" (after build) folder is the AT-Opt compiler (has our techniques implemented). </p> <p><br> If anything in unclear, or any unexpected results occur, please report it to the authors</p>
Artifact Evaluation for "Optimizing Remote Data Transfers in X10" - PACT'18
<p>Artifact Evaluation Reproduction for "Optimizing Remote Data Transfers in X10", PACT 2018.</p> <p>--------------------------------------------------------------------------------------------------------------------</p> <p>This repository contains artifacts and source codes<br> to reproduce experiments from the PACT 2018 research paper<br> titled "Optimizing Remote Data Transfers in X10"</p> <p>Our evaluations are done on Linux based Operation system.</p> <p><br> Hardware pre-requisities:<br> ------------------------------<br> We recommend the following architectures any of the following architectures:<br> * An Intel System with two nodes, each node having 32 cores (32 CPUs). Total 64 cores.<br> * An AMD System, with two nodes, each node having 16 cores (16 CPUs). Total 32 cores.<br> </p> <p><br> Software pre-requisites:<br> ----------------------------<br> 1) For Evaluation using Virtual Machine required software are already installed in the provided image.<br> A VirtualBox (from Oracle) is required to install the image and do evaluation.</p> <p>2) For Manual evaluation, the below required software needs to be installed in the system.<br> * g++ (preferred version 5.4.0)<br> * Apache ant software (preferred version 1.9.6 )<br> * Java software<br> - preferred jvm jdk1.8.0_151 (or) java-8-oracle<br> - After installation, set JAVA_HOME path in ~/.bashrc to point to the installed JAVA bin</p> <p><br> Benchmarks:<br> ---------------<br> 1) Taken from IMSuite Benchmark kernels (http://www.cse.iitm.ac.in/~krishna/imsuite/). Already, included in the package.</p> <p><br> Installation, Execution and Validation of results:<br> ---------------------------------------------------------<br> 1) For Evaluation using Virtual Machine see README.md file in ./AE/VM folder</p> <p>2) For Evaluation using Manual method see README.md file in ./AE/Manual folder</p> <p>3) For Evaluating AT-Opt technique againt varing input sizes (not discussed in the paper), look at<br> web page http://www.cse.iitm.ac.in/~krishna/imsuite/. In the ./AE/Manual folder, "x10-base" folder is<br> the baseline compiler (after bulid) and "x10-atOpt" (after build) folder is the AT-Opt compiler (has our techniques implemented). </p> <p><br> If anything in unclear, or any unexpected results occur, please report it to the authors.</p> <p> </p>
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>
Artifact for "Are My Invariants Valid? A Learning Approach"
<p>This is the preliminary artifact for our paper on learning to judge the validity of invariants (specifically, method pre- and post-conditions), including data, code and output logs. Please see the README for details on the contents and how to run the code.</p>
Capturing and Reporting Provenance Information of Simulation Studies Based on an Artifact-Based Workflow Approach - Appendix Provenance Graph
<p>Provenance graph for a fictional ecological simulation study</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)
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.