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478 results for “artifact”
Artifact of Program Selection from Large Language Models
<p>Artifact of <em>Program Selection from Large Language Models</em>, including documentation, source code, and experimental data.</p>
Artifacts of the paper under review by ISSTA
<h1>Artifacts of the paper under review by ISSTA</h1> <div>Welcome to the online repository of the ISSTA 2024 Submission 1305. We release the source code, dataset, and outputs of our study.</div> <h2>Getting Started</h2> <h3>Requirements</h3> <div>To run the project, please ensure that you have the following dependencies installed:</div> <div> </div> <div><code> pytorch=2.0.0;</code></div> <div><code> torchvision=0.15.1;</code></div> <div><code> torchaudio;</code></div> <div><code> datasets==1.16.1;</code></div> <div><code> transformers==4.21.1;</code></div> <div><code> tensorboard==2.12.2;</code></div> <div><code> tree-sitter==0.19.1;</code></div> <div><code> nltk=3.8.1;</code></div> <div><code> scipy=1.10.1;</code></div> <div>You can either install these requirements manually or execute the following script:</div> <div><code>bash scripts/setup.sh</code></div> <h3>Dataset</h3> <div>We perform our experiments on following datasets:</div> <div> * CodeSearchNet from [here](https://github.com/github/CodeSearchNet).</div> <div> * BFP dataset from Tufano et al.'s TOSEM'19 <a href="https://dl.acm.org/doi/abs/10.1145/3340544">paper.</a></div> <div> * Defects4J dataset from René et al.'s ISSTA'14 <a href="https://dl.acm.org/doi/abs/10.1145/2610384.2628055">paper.</a></div> <div> * VulRD from Fu et al.'s FSE'22 <a href="https://dl.acm.org/doi/abs/10.1145/3540250.3549098">paper</a>.</div> <div>Download the pre-training dataset:</div> <div><code>python Dataset\pre-training\download.py</code></div> <h3>Pretrain the model</h3> <div><code>bash scripts/pre-train.sh -g [GPU_ID]</code></div> <h3>Task 1: bug repair</h3> <div><code>bash scripts/finetune_d4j.sh -g [GPU_ID]</code></div> <div>If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation.</div> <div>To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path.</div> <div> </div> <h3>Task 2: vulnerability repair</h3> <div><code>bash scripts/finetune_vul.sh -g [GPU_ID]</code></div> <div>If the evaluation during fine-tuning takes too long, you can adjust the "--evaluate_sample_size" parameter. This parameter refers to the number of cases in the validation set during evaluation.</div> <div>To evaluate the performance of a specific checkpoint, add the flag "-e" followed by the checkpoint path.</div> <h3>Outputs</h3> <div>The outputs of our tool are stored in the `outputs` folder.</div> <p><br><br></p>
Artifacts for Bachelor's Thesis: "Development and Integration of an Area Generator for the Gamify-IT Project"
<p>The project files and evaluation questionnaires and results for the Bachelor's Thesis "Development and Integration of an Area Generator for the Gamify-IT Project".</p> <p>The project files are part of the Gamify-IT project and have only been expanded in this work. </p> <p>The evaluation questionnaires and results are partially in German. </p>
UNCW Artifact of the Week #6
Artifact of the Week #6 UNCW Archaeology Lab 2021 Source: Objaverse 1.0 / Sketchfab
Artifact 5 - Conch Shell - Sanford Museum
Source: Objaverse 1.0 / Sketchfab
Interior of a House Containing Artifacts
A 3D model I made Took me 2 days Tell me if you like it :) Source: Objaverse 1.0 / Sketchfab
UNCW Artifact of the Week #5
Artifact of the Week #5 UNCW Archaeology Lab 2021 Source: Objaverse 1.0 / Sketchfab
# Artifact: Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?
<p>This artifact complements the ICSE'24 paper titled "Smart Contract and DeFi Security Tools: Do They Meet the Needs of Practitioners?" by providing comprehensive resources for reproducing and extending its research findings. The artifact includes 'retro.db', a database of 127 DeFi attacks discussed in sections 4.1 and 4.2 of the paper. This database comprises data on the attacks and the outcomes derived from various analysis tools applied to the vulnerable contracts. Additionally, it provides thorough instructions on how to reapply these tools to the dataset, as well as guidance on integrating and running additional tools. Finally, it includes anonymized responses from the survey participants, which are used to recreate the analysis presented in sections 4.3, 4.4, and 4.5. </p>
Cell2Doc Artifacts (dataset and model checkpoints)
<p>The repository contains the dataset and model checkpoints of Cell2Doc.</p>
Artifact for "Program Repair and LLMs for Intelligent Tutoring of Programming"
<p>This is the supplementary material for our work on "Program Repair and LLMs for Intelligent Tutoring of Programming". The corresponding paper has been submitted to the Technical Symposium on Computer Science Education (SIGCSE TS) 2025.</p> <p><span>Our material includes (1) the artifacts for our </span><strong><span>user evaluat</span><span>ions</span></strong><span> and </span><span>(beyond what we describe in our paper) (2) </span><span>additional information on the </span><strong><span>student </span><span>projects</span></strong><span> </span><span>used in our software engineering course. In this course, third-year CS students focus on developing the intelligent tutoring system.</span></p> <p><strong>1. Artifacts for the <em>User Evaluations</em></strong></p> <p>The <code>user_evaluation/students</code> folder includes the main artifacts for the conducted experiments with the first-year CS students:</p> <ul> <li><code>students_questionnaire_part1.pdf</code> -- It shows the PDF version of the web form of our survey for students before they solve the programming assignment.</li> <li><code>students_questionnaire_part2_groupA.pdf</code> -- It shows the PDF version of the web form of our survey for group A students after they solve programming assignments with ITS.</li> <li><code>students_questionnaire_part2_groupB.pdf</code> -- It shows the PDF version of the web form of our survey for group B students after they solve the programming assignments without ITS.</li> <li><code>students_responses.xlsx</code> -- It shows all students' responses.</li> </ul> <p>The <code>user_evaluation/tutors</code> folder includes the main artifacts for the conducted surveys with the CS tutors:</p> <ul> <li><code>tutors_questionnaire_part1.pdf</code> -- It shows the PDF version of the web form of our survey for tutors before they grade students' programming assignments.</li> <li><code>tutors_questionnaire_part3.pdf</code> -- It shows the PDF version of the web form of our in-person interview for tutors after they grade students' programming assignments.</li> <li><code>tutors_responses.xlsx</code> -- It shows all tutors' responses.</li> </ul> <p><br><strong>2. Course Project Descriptions</strong></p> <p>The <code>projects</code> folder includes the main artifacts for the conducted experiments with the first-year CS students:</p> <ul> <li><code>2022_project.pdf</code> -- It includes the project descriptions, which have been provided to the third-year students in the first instance of our Software Engineering course.</li> <li><code>2023_project.pdf</code> -- It includes the project descriptions, which have been provided to the third-year students in the second instance of our Software Engineering course.</li> <li><code>project_schedule.pdf</code> -- It includes the overview of the assignments in our course illustrating the project's overall schedule.</li> </ul>
Data and code for 'Fast and artifact-free excitation multiplexing using synchronized image scanning'
<p>Data and source code for the publication 'Fast and artifact-free excitation multiplexing using synchronized image scanning'.</p>
Artifact for A Survey on Techniques to Profile the Energy Consumption of Android Applications
<p>To ensure availability and reproducibility, we have made all study artifacts publicly available for "<strong>A Survey on Techniques to Profile the Energy Consumption of Android Applications</strong>", a paper submitted to <strong><em>ACM Computing Surveys</em></strong>. In this study, we review state-of-the-art software-based energy profilers tailored for Android applications and propose a novel taxonomy for comparative analysis. We provide a brief overview of these energy profilers and compare their features. Based on the conducted literature review, we present conceptual architectures of the different types of software-based energy profilers to help build a tool that addresses the existing limitations in energy profiling.</p> <p>This repository contains the following files:</p> <ul> <li>Coding list generated from the Nvivo project (Files compared by number of codes). This file contains the codes used in taxonomy and the number of code references.</li> <li>Coding hierarchy generated from the Nvivo project (Codes compared by number of coding references). This file shows the hierarchy of codes and their share visually.</li> <li>List of selected papers (generated from the Nvivo project). This file presents the list of selected papers and metadata about these papers including publishing year, publisher, type of publication, etc.</li> <li>Search queries (SearchQueries). This file contains the search queries that are used to obtain the publications for review.</li> <li>Summary of the papers generated from the Nvivo project (Summary). This file contains the summary of the papers.</li> </ul>
Artifact for the paper "Towards Model-Driven Heat Pump Control in a Multi-Story Building"
<p>This is a reproducibility package for the paper "Towards Model-Driven Heat Pump Control in a Multi-Story Building".</p> <p>Domestic heating systems can provide significant energy flexibility when integrated with heat pumps and hot water buffer tanks, especially with fluctuating day-ahead energy prices. However, optimizing these systems in large buildings with shared resources poses crucial challenges. While most existing studies target single-room or single-family house systems, this study explores the complexities within a three-story building housing six apartments. The building’s heating system consists of a hot water buffer tank, mixing loop, radiant floor heating system, and a Ground Source Heat Pump (GSHP) controlled by a weather-compensated control strategy (WCS). Our approach aims to tackle challenges like integrating real sensor data, scalability, varying weather effects, and diverse resident heat use preferences. We employ the CTSMR software to identify thermal behaviour and use reinforcement learning to design an intelligent/model-driven UPPAAL STRATEGO<br>controller. Our results reveal a 43% reduction in energy costs while maintaining comfort levels compared to a WCS. The temporal validity of the estimated thermal models is also analyzed.</p>
SCC24 Repro All Artifacts
Open the record for dataset details and reuse information.
SC24 Repro Artifacts A1
Open the record for dataset details and reuse information.
SC24 Repro Artifacts A2
Open the record for dataset details and reuse information.
RT-BarnesHut Artifact
Open the record for dataset details and reuse information.
Data Artifact for "Gaze into the Pattern: Characterizing Spatial Patterns with Internal Temporal Correlations for Hardware Prefetching"
<p>This dataset contains the Ligra, PARSEC, GAP, and QMM traces used in our paper "Gaze into the Pattern: Characterizing Spatial Patterns with Internal Temporal Correlations for Hardware Prefetching", which is accepted by HPCA'25. These traces are provided as part of the AE proces.</p>
Artifacts for paper "PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing" submitted to TOSEM
<p>The project includes the data and code used in the submitted TOSEM paper titled "PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing"</p>
Artifact from "A Little Goes a Long Way: Tuning Configuration Selection for Continuous Kernel Fuzzing"
<p>Artifact from "A Little Goes a Long Way: Tuning Configuration Selection for Continuous Kernel Fuzzing"</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.