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94 results for “Generative AI”
Automated Generation of Code Contracts - Generative AI to the Rescue?
<p>This replication package provides the setup and results to generate OpenJML code contracts for Java source code by fine-tuning and employing the resulting CodeT5 and CodeT5+ transformer models. Our code contract generation setup involved the training of the AI models and application. Furthermore, we analyzed the generated annotations wrt. thier logical validity and the type of OpenJML compilation errors. Both methods, together with the results are similarly provided.</p> <p><strong>Source Code Repository (see also scripts-sources.tar): </strong></p> <ul> <li><a href="https://github.com/SEG-UNIBE/auto-generated-code-contracts">https://github.com/SEG-UNIBE/auto-generated-code-contracts</a></li> <li><a href="../doi/10.5281/zenodo.13356451">https://zenodo.org/doi/10.5281/zenodo.13356451</a></li> </ul> <p><strong>Replication Package: </strong>contains the following [folders]</p> <ul> <li><strong>Scripts:</strong> <ul> <li>[scripts-sources.tar]: source codes of the following scripts <ul> <li>Python scripts that we used for training and adding the OpenJML code contracts to the Java methods</li> <li>automated analyses of the studied source code classes and the type of compilation errors</li> </ul> </li> </ul> </li> <li><strong>Sourcegraph Search Results:</strong> <ul> <li>[sourcegraph-results.tar]: the results of the Sourcegraph search queries </li> </ul> </li> <li><strong>Datasets:</strong> <ul> <li>[dataset.tar]: the dataset including the weka-project which contributes two-thirds of the contracts</li> <li>[dataset-withoutweka.tar]: the dataset without weka, which is significantly smaller and was used to examine the performance bias when training and testing without weka</li> </ul> </li> <li><strong>CodeT5 Models:</strong><br> <ul> <li>[codet5-contracts.tar]: the best performing CodeT5 model which was fine-tuned to create OpenJML annotations for methods</li> <li>[codet5p-contracts.tar]: the best performing CodeT5+ model which was fine-tuned to create OpenJML annotations for methods</li> <li>[codet5p-contracts-withoutweka.tar]: the CodeT5+ model which was trained without weka on the same task</li> </ul> </li> <li><strong>Analysis Results:</strong> <ul> <li>[analysis-results.tar/compilability-analysis]: the results of the compilability analysis <ul> <li>the subjects to which we applied the best performing CodeT5+</li> <li>the compilation results and their analysis</li> </ul> </li> <li>[analysis-results.tar/logical-analysis] the results of the logical analysis <ul> <li>the analysis of logic validity of SimpleStack and SimpleTicTacToe</li> </ul> </li> </ul> </li> </ul>
The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies
<p>This is the dataset for the paper: The Impact of Generative AI-Powered Code Generation Tools on Software Engineer Hiring: Recruiters' Experiences, Perceptions, and Strategies</p> <p> This paper was accepted for publication at the 58th Hawaii International Conference on System Sciences (HICSS) - Software Technology Track</p>
Synthetic Datasets for "Binary Classification Optimisation with AI-Generated Data"
<p>Images of melanomas and Basal Cell Carcinoma generated with a stylegan2. Dataset corresponding to the article "Binary Classification Optimisation with AI-Generated Data"</p>
Datasets and scripts related to the paper: "*Can Generative AI Help us in Open Coding of Software Engineering Data?*"
<p>This replication package contains datasets and scripts related to the paper: "<em>Can Generative AI Help us in Open Coding of Software Engineering Data?</em>"</p> <p>The replication package is organized into two directories:</p> <ul> <li> <p><code>manual_analysis</code>: This directory contains all sheets used to perform the manual analysis for RQ1, RQ2, and RQ3.</p> </li> <li> <p><code>stats</code>: This directory contains all datasets, scripts, and results metrics used for the quantitative analyses of RQ1 and RQ2.</p> </li> </ul> <p>In the following, we describe the content of each directory:</p> <h2>manual_analysis</h2> <ul> <li> <p><code>manual_analysis_rq1</code>: This directory contains all sheets used to perform manual analysis for RQ1 (independent and incremental coding).</p> <ul> <li> <p>The sub-directory <code>incremental_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_incremental.csv</code>, <code>DL_Faults_ISSUE_incremental.csv</code>, <code>DL_Fault_SO_incremental.csv</code>, <code>DRL_Challenges_incremental.csv</code> and <code>Functional_incremental.csv</code>). All these .csv files contain the following columns:</p> <ul> <li><em>Link</em>: The link to the instances</li> <li><em>Prompt</em>: Prompt used as input to GPT-4-Turbo</li> <li><em>ID</em>: Instance ID</li> <li><em>FinalTag</em>: Tag assigned by the human in the original paper</li> <li><em>Chatgpt_output_memory</em>: Output of GPT-4-Turbo with incremental coding</li> <li><em>Chatgpt_output_memory_clean</em>: (only for the DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li><em>Author1</em>: Label assigned by the first author</li> <li><em>Author2</em>: Label assigned by the second author</li> <li><em>FinalOutput</em>: Label assigned after the resolution of the conflicts</li> </ul> </li> <li> <p>The sub-directory <code>independent_coding</code> contains .csv files for all datasets (<code>DL_Faults_COMMIT_independent.csv</code>, <code>DL_Faults_ISSUE_ independent.csv</code>, <code>DL_Fault_SO_ independent.csv</code>, <code>DRL_Challenges_ independent.csv</code> and <code>Functional_ independent.csv</code>), containing the following columns:</p> <ul> <li><em>Link</em>: The link to the instances</li> <li><em>Prompt</em>: Prompt used as input to GPT-4-Turbo</li> <li><em>ID</em>: Specific ID for the instance</li> <li><em>FinalTag</em>: Tag assigned by the human in the original paper</li> <li><em>Chatgpt_output</em>: Output of GPT-4-Turbo with independent coding</li> <li><em>Chatgpt_output_clean</em>: (only for DL Faults datasets) output of GPT-4-Turbo considering only the label assigned, excluding the text</li> <li><em>Author1</em>: Label assigned by the first author</li> <li><em>Author2</em>: Label assigned by the second author</li> <li><em>FinalOutput</em>: Label assigned after the resolution of the conflicts.</li> </ul> </li> <li> <p>Also, the sub-directory contains sheets with inconsistencies after resolving conflicts. The directory <code>inconsistency_incremental_coding</code> contains .csv files with the following columns:</p> <ul> <li><em>Dataset</em>: The dataset considered</li> <li><em>Human</em>: The label assigned by the human in the original paper</li> <li><em>Machine</em>: The label assigned by GPT-4-Turbo</li> <li><em>Classification</em>: The final label assigned by the authors after resolving the conflicts. Multiple classifications for a single instance are separated by a comma “,”</li> <li><em>Final</em>: final label assigned after the resolution of the incompatibilities</li> </ul> </li> <li> <p>Similarly, the sub-directory <code>inconsistency_independent_coding</code> contains a .csv file with the same columns as before, but this is for the case of independent coding.</p> </li> </ul> </li> <li> <p><code>manual_analysis_rq2</code>: This directory contains .csv files for all datasets (<code>DL_Faults_redundant_tag.csv</code>, <code>DRL_Challenges_redundant_tag.csv</code>, <code>Functional_redundant_tag.csv</code>) to perform manual analysis for RQ2.</p> <ul> <li> <p>The <code>DL_Faults_redundant_tag.csv</code> file contains the following columns:</p> <ul> <li><em>Tags Redundant</em>: tags identified as redundant by GPT-4-Turbo</li> <li><em>Matched</em>: inspection by the authors to see if the tags are redundant matching or not</li> <li><em>FinalTag</em>: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> <p>The <code>Functional_redundant_tag.csv</code> file contains the same columns as before</p> </li> <li> <p>The <code>DRL_Challenges_redundant_tag.csv</code> file is organized as follows:</p> <ul> <li><em>Tags Suggested</em>: The final tag suggested by GPT-4-Turbo</li> <li><em>Tags Redundant</em>: tags identified as redundant by GPT-4-Turbo</li> <li><em>Matched</em>: inspection by the authors to see if the tags redundant matching or not with the tags suggested</li> <li><em>FinalTag</em>: final tag assigned by the authors after the resolution of the conflict</li> </ul> </li> <li> <p>The sub-directory <code>code_consolidation_mapping_overview</code> contains .csv files (<code>DL_Faults_rq2_overview.csv</code>, <code>DRL_Challenges_rq2_overview.csv</code>, <code>Functional_rq2_overview.csv</code>) organized as follows:</p> <ul> <li><em>Initial_Tags</em>: list of the unique initial tags assigned by GPT-4-Turbo for each dataset</li> <li><em>Mapped_tags</em>: list of tags mapped by GPT-4-Turbo</li> <li><em>Unmatched_tags</em>: list of unmatched tags by GPT-4-Turbo</li> <li><em>Aggregating_tags</em>: list of consolidated tags</li> <li><em>Final_tags</em>: list of final tags after the consolidation task</li> </ul> </li> </ul> </li> <li> <p><code>prompt_for_each_rq</code>: This directory contains: - (i) the history of prompts used in each dataset (<code>prompts_history.txt</code>) -(ii) all final prompt used for the analysis of each dataset, prompt used for incremental coding, prompt used in rq2 to consolidate redundant codes, prompt used in rq3 to create taxonomy (<code>generic_prompt.txt</code>) -(iii) all .csv files in which there are indicate, for each dataset, the link and the prompt used (<code>prompt_DL_Faults_COMMIT.csv</code>, <code>prompt_DL_Faults_ISSUE.csv</code>, <code>prompt_DL_Faults_SO.csv</code>, <code>prompt_DRL_Challenges.csv</code>). For the Functional Dataset .csv file contains, instead, Question, Answer and Prompt used (<code>prompt_Functional.csv</code>)</p> </li> <li> <p><code>rq3</code>: This directory contains the taxonomies obtained from GPT-4-Turbo for the DL Faults and for the DRL Challenges (<code>taxonomy_DL_Faults.txt</code>,<code>taxonomy_DRL_Challenges.txt</code>)</p> </li> </ul> <h2>stats</h2> <ul> <li> <p><code>RQ1</code>: contains script and datasets used to perform metrics for RQ1. The analysis calculates all possible combinations between Matched, More Abstract, More Specific, and Unmatched.</p> <ul> <li><code>RQ1_Stats.ipynb</code> is a Python Jupyter nooteook to compute the RQ1 metrics. To use it, as explained in the notebook, it is necessary to change the values of variables contained in the first code block.</li> <li><code>independent-prompting</code>: Contains the datasets related to the independent prompting. Each line contains the following fields: <ul> <li><em>Link</em>: Link to the artifact being tagged</li> <li><em>Prompt</em>: Prompt sent to GPT-4-Turbo</li> <li><em>FinalTag</em>: Artifact coding from the replicated study</li> <li><em>chatgpt_output_text</em>: GPT-4-Turbo output</li> <li><em>chatgpt_output</em>: Codes parsed from the GPT-4-Turbo output</li> <li><em>Author1</em>: Annotator 1 evaluation of the coding</li> <li><em>Author2</em>: Annotator 2 evaluation of the coding</li> <li><em>FinalOutput</em>: Consolidated evaluation</li> </ul> </li> <li><code>incremental-prompting</code>: Contains the datasets related to the incremental prompting (same format as independent prompting)</li> <li><code>results</code>: contains files for the RQ1 quantitative results. The files are named <code>RQ1\_<<Dataset>>\_<<Prompt method>>\_<<ExcludingNegative>>\_<<MetricAggregation>>.csv</code>, where <em>Dataset</em> is the dataset name, <em>Prompt method</em> indicates whether results are for independent or incremental prompting, <em>Excluding Negatives</em> (for datasets where this applies) whether results have been obtained by excluding negative instances, and <em>MetricAggregation</em> (where it applies) how metrics have been aggregated (macro or weighted average). The files report columns indicating the <em>Dataset</em>, the <em>Matching type</em>, the <em>Accuracy</em>, <em>Precision</em>, <em>Recall</em>, <em>F1 Score</em>, and <em>Cohen's Kappa</em>.</li> </ul> </li> <li> <p><code>RQ2</code>: contains the script used to perform metrics for RQ2, the datasets it uses, and its output.</p> <ul> <li><code>RQ2_SetStats.ipynb</code> is the Python Jupyter notebook to perform the analyses. The scripts takes as input the following types of files, contained in the directory contains the script used to perform the metrics for RQ2. The script takes in input:</li> <li>RQ1 Data Files (<code>RQ1_DLFaults_Issues.csv</code>, <code>RQ1_DLFaults_Commits.csv</code>, and <code>RQ1_DLFaults_SO.csv</code>, joined in a single .csv <code>RQ1_DLFaults.csv</code>). These are the same files used in RQ1.</li> <li>Mapping Files (<code>RQ2_Mappings_DRL.csv</code>, <code>RQ2_Mappings_Functional.csv</code>, <code>RQ2_Mappings_DLFaults.csv</code>). These contain the mappings between human tags (<em>HumanTags</em>), GPT-4-Turbo tags (<em>Final Tags</em>), with indicated the type of matching (<em>MatchType</em>).</li> <li>Additional codes creating during the consolidation (<code>RQ2_newCodes_DRL.csv</code>, <code>RQ2_newCodes_Functional.csv</code>, <code>RQ2_newCodes_DLFaults.csv</code>), annotated with the matching: <em>new code</em>,<em>old code</em>,<em>human code</em>,<em>match type</em></li> <li>Set files (<code>RQ2_Sets_DRL.csv</code>, <code>RQ2_Sets_Functional.csv</code>, <code>RQ2_Sets_DLFaults.csv</code>). Each file contains the following columns: <ul> <li><em>HumanTags</em>: List of tags from the original dataset</li> <li><em>InitialTags</em>: Set of tags from RQ1,</li> <li><em>ConsolidatedTags</em>: Tags that have been consolidated,</li> <li><em>FinalTags</em>: Final set of tags (results of RQ2, used in RQ3)</li> <li><em>NewTags</em>: New tags created during consolidation</li> </ul> </li> <li><code>RQ2_Set_Metrics.csv</code>: Reports the RQ2 output metrics (Precision, Recall, F1-Score, Jaccard).</li> </ul> </li> </ul>
Artifacts for "FLAG: Finding Line Anomalies (in code) with Generative AI"
<p>Artifacts for our work used to detect defects in code using LLM consistency checking. Please read README.md file in repository to start.</p>
Artifacts for "FLAG: Finding Line Anomalies (in code) with Generative AI"
<p>Artifacts for our work used to detect defects in code using Large Language Models. Please read README.md file in repository to start.</p>
Effectiveness of Original vs. AI-Generated Plain Language Summaries of Systematic Reviews
ClinicalTrials.gov study NCT07140107. IPD Sharing: YES. Countries: 1. Publications: 18.
DHL Survey on Generative AI for MyChart Messaging
ClinicalTrials.gov study NCT06108037. IPD Sharing: NO. Countries: 1. Publications: 1.
Evaluating an AI-Generated Health Podcast
ClinicalTrials.gov study NCT06891495. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Use of a Generative AI (Gen-AI) Chatbot for Anxiety and Depression Among Persons With Cannabis Use
ClinicalTrials.gov study NCT06920238. IPD Sharing: NO. Countries: 1. Publications: 1.
AI-generated Feedback in Social Robotic Virtual Patients
ClinicalTrials.gov study NCT07277829. IPD Sharing: YES. Countries: 1. Publications: 2.
Video-Based AI-Generated Peri-operative Instructions for Mitral Valve Surgery
ClinicalTrials.gov study NCT07036926. IPD Sharing: NO. Countries: 0. Publications: 10.
Generative AI Radiologist's Workstation
ClinicalTrials.gov study NCT07057830. IPD Sharing: NO. Countries: 1. Publications: 0.
The motivational factors of AI technology that influence milliennials and members of Generation Z in online transactions
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Generative AI enhances individual creativity but reduces the collective diversity of novel content
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Aesthetic Indigenous Forms: Generative AI Imagery Dataset
<p>The past, present, and future of our city communities in the United States exist within colonization's ongoing violence - a perpetual state of lived aftermath to stolen lands, white supremacy, genocide, slavery, and anthropogenic climate change. Our land and water relations remember how we treat them with sewage, chemicals, and trash, and they influence the artistic expressions and world's of Black Philadelphia writers, philosophers, artists. The <strong>Aesthetic Indigenous Forms Dataset</strong> includes 29 generative artificial intelligence (Gen AI) images, historical research, and curatorial prose that complicates the stories of climate racism, Indigeneity, and climate change that Philadelphia's public art and environmental histories tell. This data is integrated within the Post Colonial Dreams Museum, one of two distinct, yet interconnected virtual museums in <a href="https://tinyurl.com/thecreativecollabproject"><i>Relational Possibilities: A Remix of Aesthetic Forms Through Indigeneity and Blackness</i></a>. <br><br>Curated by Dana Reijerkerk, B.A., M.I.S.<br>The Creative CoLab Project: Relational Possibilities, LEADING Fellow 2023-2024. <br>This work is licensed under: <a href="https://creativecommons.org/licenses/by-nc-nd/4.0/">CC BY-NC-ND 4.0</a>. </p>
Generative AI in Education and Research: A systematic mapping and bibliometric analysis of published articles
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Exploring Ethical Implications: Unraveling Factors Influencing Data Governance Awareness Behavior in Generative AI Chatbot
<p>Data set</p>
Generative AI in Education and Research: A systematic mapping of published articles
<p>This file contains an appendix of the analysis. </p>
Dataset for MedCodER: A Generative AI Assistant for Medical Coding
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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.