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478 results for “artifact”
Artifacts for ASE 2022 Paper -- FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures
<p><strong>Artifacts for FuzzerAid: Grouping Fuzzed Crashes Based On Fault Signatures</strong></p> <p>Fuzzing has been an important approach for finding bugs and vulnerabilities in programs. Many fuzzers deployed in industry run daily and can generate an overwhelming number of crashes. Diagnosing such crashes can be very challenging and time consuming. Existing fuzzers typically employ heuristics such as code coverage or call stack hashes to weed out duplicate reporting of bugs. While these heuristics are cheap, they are often imprecise and end up still reporting many "unique" crashes corresponding to the same bug. In this paper, we present <em>FuzzerAid</em> that uses <em>fault signatures</em> to group crashes reported by the fuzzers. Fault signature is a small executable program and consists of a selection of necessary statements from the original program that can reproduce a bug. In our approach, we first generate a fault signature using a given crash. We then execute the fault signature with other crash inducing inputs. If the failure is reproduced, we classify the crashes into the group labeled with the fault signature; if not, we generate a new fault signature. After all the crash inducing inputs are classified, we further merge the fault signatures of the same root cause into a group. We implemented our approach in a tool called <em>FuzzerAid</em> and evaluated it on 3020 crashes generated from 15 real-world bugs and 4 large open source projects. Our evaluation shows that we are able to correctly group 99.1% of the crashes and reported only 17 (+2) "unique" bugs, outperforming the state-of-the-art fuzzers.</p> <p> </p> <p><strong>Change log for v1.0.1:</strong></p> <p>Fix wrong Bug ID for <em>sqlite</em> and add README clarification.</p> <p><strong>Change log for v1.0.2:</strong></p> <p>Added an example linking data in the repository to the table.</p>
Efficient Detection of Test Interference in C Projects (Artifact)
<p>This record provides research artifacts for the article "Efficient Detection of Test Interference in C Projects", accepted and to be presented at <a href="https://conf.researchr.org/home/ase-2024">ASE 2024</a>. Please refer to the README.md in the tgz file for details about the artifact and how it relates to the manuscript describing our study. Please also see our related Zenodo record with the container images used in the study: <a href="https://doi.org/10.5281/zenodo.7935821" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.7935821</a>.</p>
Artifact for "Does Task Complexity Moderate the Benefits of Liveness? - A Controlled Experiment"
<p>This artifact includes the setup, data, and analysis for the experiment described in the article "Does Task Complexity Moderate the Benefits of Liveness? - A Controlled Experiment" published in the journal "The Art, Science, and Engineering of Programming<em>" </em>Volume 9.</p> <p>The artifact has the following structure:</p> <ul> <li> <p>experiment-setup</p> <ul> <li>experiment-environment.zip: Archive including the fully configured Squeak/Smalltalk environment</li> <li>experiment-protocol.pdf: Full protocol for conducting the experiment</li> <li>system: Includes the base system used for the experiment without any seeded faults</li> <li>tasks <ul> <li>task-descriptions.txt: The task descriptions of steps to reproduce and symptoms for the tasks used in the experiment</li> <li>patches: Patch files for all generated tasks</li> </ul> </li> <li>infrastructure: Contains source code of the tools used for controlling tasks in the development environment</li> <li>questionnaire <ul> <li>survey.dfglive.2023-01-06.xml: Questionnaire configuration for a SoSci survey server, includes demographics, experience, and skill questionnaire</li> </ul> </li> </ul> </li> <li> <p>results</p> <ul> <li>data <ul> <li>export-NN.zip: Each archive contains the raw data from one run <ul> <li>starTrackData-NN: Contains the detailed event log</li> <li>experimentState.json: Contains the measurements and the final state for each task (completed, notStarted, etc.)</li> <li>skillTestResult-NN.json: Contains the score reached in the skill test</li> <li>task-X-NN.cs: The submitted patch for task X</li> </ul> </li> </ul> </li> <li>questionnaire <ul> <li>data_dfglive-analysis.ods: Demographics and experience questionnaire results (gender column is redacted due to potential deanonymization)</li> <li>results of skill test are only available in graded form in the participant export files</li> </ul> </li> </ul> </li> <li> <p>analysis</p> <ul> <li>analysis-r: Main analysis scripts <ul> <li>main.R, main-contrast-based.R: Two versions of the main and moderation effect analysis using two different processes</li> <li>live-tools-usage-time.R: Main and moderation effect analysis on tool usage</li> <li>demographics.R: Analysis of demographics and rendering of charts</li> </ul> </li> <li>analysis-tool-usage-correlation: Includes scripts to extract tool usage frequencies from the event logs. Assumes that export-NN.zip files are in the parent folder.</li> </ul> </li> </ul>
Accepted Artifact for Profiling and Optimizing Java Streams
<p>The accepted artifact for the journal article "Profiling and Optimizing Java Streams" published in Volume 7, Issue 3 of <a href="https://programming-journal.org/"><em>The Art, Science, and Engineering of Programming</em></a>.</p> <p>Please refer to the README.md to work with the artifact and reproduce results from the article.</p>
Supplemental artifacts of the paper: Efficient Binary-Level Coverage Analysis
<p>NOTE: the official repository of bcov is: <a href="https://github.com/abenkhadra/bcov">https://github.com/abenkhadra/bcov</a></p> <p>This repository contains the artifacts accompanying our paper: "Efficient Binary-Level Coverage Analysis", which appeared in ESEC/FSE'20. The artifacts consists of two packages, namely, bcov-benchmarks.tar.gz and bcov-artifacts.tar.gz. The former package contains the complete list of binaries described in our experiments. The artifacts of the latter package are organized as follows:</p> <p> - <strong>sample-binaries</strong>. Folder that contains sample binaries patched with bcov.</p> <p> - <strong>dataset.tar.gz</strong>. Package containing experimental data in csv format.</p> <p> - <strong>figures</strong>. Folder that contains the python script used to generate the figures<br> of our paper. It assumes that the dataset was first extracted to the folder `dataset`.</p> <p> - <strong>install.sh</strong>. This script builds and installs bcov together with its dependencies.</p> <p> - <strong>experiment-01.sh</strong>. This script patches our sample binaries and shows how coverage<br> data can be collected. It assumes that bcov was installed using the previous script.</p> <p> - <strong>bcov.tar.gz</strong>. Source code of the first public version of `bcov`. The tool is distributed under an MIT license.<br> </p>
Accepted Artifact for Privacy-Respecting Type Error Telemetry at Scale
<p>This artifact packages the data for the paper: <em>Privacy-Respecting Type Error Telemetry at Scale</em></p> <p>There are two files on Zenodo:</p> <ul> <li>data.tar.gz has the original Luau telemetry data</li> <li>artifact.tar.gz has a result PDF, intermediate data, and scripts for processing the data</li> </ul> <p>The artifact code and the source for the paper are also on GitHub:</p> <ul> <li><a href="https://github.com/bennn/luau-telemetry">https://github.com/bennn/luau-telemetry</a></li> </ul> <p>This artifact is primarily a **dataset**. It shows how we reached the conclusions in the paper.</p> <p>The scripts in this artifact are provided as-is for completeness. They may have bugs. They may not work as advertised.</p>
Fast Symbolic Computation of Bottom SCCs - TACAS 2024 artifact
<p>This is the artifact for the paper "<em>Fast Symbolic Computation of Bottom SCCs</em>", by Anna B. Jakobsen, Rasmus S. M. Jørgensen, Jaco van de Pol and Andreas Pavlogiannis, appearing in TACAS 2024.</p> <p>The artifact contains the LTSmin toolset, extended with an implementation of the algorithms from the paper, to compute the Bottom Strongly Connected Components of a directed graph, provided symbolically by BDDs (Binary Decision Diagrams).</p> <p>The artifact also contains the data set, consisting of directed graphs (state spaces) specified in DVE (Divine), PNML (Petri Nets) and BN (Boolean Networks).</p> <p>The file README.md contains the instructions how to setup the artifact on Ubuntu and how to run the experiment scripts.</p>
USENIX'24 Artifact Datasets: With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors
<p>This dataset contains the measurements and analysis results for our USENIX Security '24 paper 'With Great Power Come Great Side Channels: Statistical Timing Side-Channel Analyses with Bounded Type-1 Errors'.</p>
Jacdac: Service-based Prototyping of Embedded Systems (PLDI 2024 Artifact Evaluation)
<p>This artifact allows others to reproduce and explore the results seen in "Jacdac: Service-based Prototyping of Embedded Systems". The artifact contains a prebuilt docker image and the Dockerfile source used to produce the prebuilt docker image. Evaluators should follow the README contained in this artifact for complete instruction.</p>
Additional Artifacts - Supplements to: A Resilient Workflow to Control a Biomedical HPC Simulation in an Urgent Computing Setting
<p>In this dataset, we have collected supplementary artifacts to support an understanding of the workflow presented in the submission cited (see related identifiers).</p> <p>These artifacts are (cf. README.md in the main folder of the tar.gz archive):</p> <p>A1: modified HemoFlow code (cf. https://github.com/gzavo/hemoflow) for our workflow experiments (subfolder "hemoflowcfd");<br>A2: workflow descriptions in python for Apache Airflow (subfolder "workflow");<br>A3: inputs (.xml/.npz) and output (.txt) for the example (subfolder "case").</p> <p> </p>
Analyzing and Mitigating (with LLMs) the Security Misconfigurations of Helm Charts from Artifact Hub
<p>In the corresponding scientific paper, we proposed a pipeline to mine Helm charts from Artifact Hub, a popular centralized repository, and analyze them using state-of-the-art open-source tools like Checkov and KICS. First, such a pipeline runs several chart analyzers and identifies the common and unique misconfigurations reported by each tool. Secondly, it uses LLMs to suggest mitigation for each misconfiguration. Finally, the chart refactoring previously generated is analyzed again by the same tools to see whether it satisfies the tool's policies.</p> <p>In this dataset, you can find all the Helm chart templates downloaded from Artifact Hub (available in June 2024), all the outputs of the tools analyzing such templates, the CSV result files with all LLM queries and answers, and the snippets selected for the manual analysis.</p>
Github BPMN Artifacts Dataset 2021
<p>Information about 327,436 potential BPMN artifacts identified in all public Github repositories referenced in the <a href="https://ghtorrent.org/">GHTorrent</a> dump from <a href="https://ghtorrent-downloads.ewi.tudelft.nl/mysql/mysql-2021-03-06.tar.gz">March 2021</a>. </p> <p>The data file is in line-delimited JSON format, with each row containing an array with the following six elements:</p> <ol> <li>GHTorrent project ID</li> <li>GitHub user name</li> <li>GitHub repository name</li> <li>GitHub branch name</li> <li>Path to file inside repository</li> <li>SHA1 hash of the file's contents</li> </ol> <p>To get a list of retrievable URLs, use e.g. the following Python one-liner:</p> <pre><code class="language-bash">python3 -c 'import json; import sys; print(*[f"https://raw.githubusercontent.com/{u}/{r}/{b}/{f}" for _, u, r, b, f, _ in map(json.loads, sys.stdin)], sep="\n")' < bpmn-artifacts.jsonl > urls.txt</code></pre> <p>(using the hashes to filter out duplicates first is recommended though)</p>
Artifact for "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"
<p>Artifact for the paper "BDDs Strike Back - Efficient Analysis of Static and Dynamic Fault Trees"</p> <p>The package contains:</p> <ul> <li>example files for all static and dynamic fault tree models</li> <li>installation instructions for the three tools</li> <li>scripts to perform the benchmarking</li> <li>detailed result tables</li> </ul>
Artifacts for the ISSTA 2022 Paper: An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection
<p>This repository contains the evaluation script and the corresponding data of the ISSTA'22 paper "An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection".</p>
OOPSLA 2022 Artifact for "A Fast in-place Interpreter for WebAssembly"
<p>This archive includes</p> <ul> <li>source-code snapshots of 3 Web Engines for executing WebAssembly</li> <li>3 non-Web engines: Wasm3, the WebAssembly Micro-Runtime, and the Wizard Research Engine</li> <li>binary builds for Linux x86-64</li> <li>build instructions</li> <li>PolyBenchC benchmark Wasm binaries</li> <li>benchmarking setup and scripts</li> <li>data collected from experiments included in the OOPSLA 2022 paper</li> <li>instructions for running the benchmarks on Linux systems</li> </ul>
A Systematic Survey of Datacenter Scheduling: Data Artifacts
<p>This release contains the raw search results of the survey conducted in the paper <em>A Systematic Survey of Datacenter Scheduling</em>. </p>
Artifact for the ESEC/FSE 2019 Paper: Effects of Explicit Feature Traceability on Program Comprehension
<p>This is the artifact for our ESEC/FSE 2019 paper "Effects of Explicit Feature Traceability on Program Comprehension", providing 1) the experiment as reusable source code and 2) the anonymized results of our experiment. For more details on how to use the source code and interpret the data, please refer to the readme file.</p>
Artifact Description/Artifact Evaluation/Computational Artifact for paper, entitled Analytic Roofline Modeling and Energy Analysis of the LULESH Proxy Application on Multi-Core Clusters
We provide reproducibility initiative dependencies (Artifact Description or Artifact Evaluation or Computational Results Analysis) appendix. To allow a third party to duplicate the findings, this article provides our extensive performance data artifact and describes further details regarding the software environments, experimental design, and methodology employed for the results shown in the paper. The computational artifacts will enable experienced performance engineers to reproduce and interpret the data shown in the paper in the appropriate way and to follow the conclusions we draw from it.
Artifacts for ISSTA 2021 Paper : Validating Static Warnings via Testing Code Fragments
<p><strong>This data set is for ISSTA 2021 Paper: Validating Static Warnings via Testing Code Fragments</strong></p> <p>Static analysis is an important approach for finding bugs and vulnerabilities in software. However, inspecting and confirming static warnings are challenging and time-consuming. In this paper, we present a novel solution that automatically generates test cases based on static warnings to validate true and false positives. We designed a syntactic patching algorithm that can generate syntactically valid, semantic preserving executable code fragments from static warnings. We developed a build and testing system to automatically test code fragments using fuzzers, KLEE and Valgrind. We evaluated our techniques using 12 real-world C projects and 1955 warnings from two commercial static analysis tools. We successfully built 68.5% code fragments and generated 1003 test cases. Through automatic testing, we identified 48 true positives and 27 false positives, and 205 likely false positives. We matched 4 CVE and real-world bugs using Helium, and they are only triggered by our tool but not other baseline tools. We found that testing code fragments is scalable and useful; it can trigger bugs that testing entire programs or testing procedures failed to trigger.</p>
Artifacts for Towards Achieving Trust Through Transparency and Ethics [RE 21]
<p>Included in this repository are the Open and Axial/Selective Coding Matrices for the Grounded Theory Methodology for "Towards Achieving Trust Through Transparency and Ethics".</p> <p>Also included are the Softgoal Interdependency graphs developed as a result of the Grounded Theory Literature Review.</p> <p>The methodology is described in the paper, and this dataset exists for anybody to review and re-use these artifacts created as a result of the methodology. </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.