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30 results for “COPILOT”
Dados abertos do artigo 'Inteligência artificial no levantamento bibliográfico em bases de dados científicos: comparando expressões de busca no ChatGPT, Copilot e Gemini'
<p>Resultado por IA de todas os comandos executados na pesquisa. Texto em formado PDF.<br><br><br>Artigo disponível em → https://doi.org/10.20396/rdbci.v23i00.8678378 ou https://periodicos.sbu.unicamp.br/ojs/index.php/rdbci/article/view/8678378</p>
Grounded Copilot: How Programmers Interact with Code-Generating Models
<p>Powered by recent advances in code-generating models, AI assistants like Github Copilot promise to change the face of programming forever. But what is this new face of programming? We present the first grounded theory analysis of how programmers interact with Copilot, based on observing 20 participants---with a range of prior experience using the assistant---as they solve diverse programming tasks across four languages. Our main finding is that interactions with programming assistants are bimodal: in acceleration mode, the programmer knows what to do next and uses Copilot to get there faster; in exploration mode, the programmer is unsure how to proceed and uses Copilot to explore their options. Based on our theory, we provide recommendations for improving the usability of future AI programming assistants.</p> <p> </p> <p>This artifact contains:<br> - The scripts to generate our plots</p> <p>- Detailed study information to re-run our user study</p> <p>- Livestreams that we observed and included in our dataset</p> <p>- Our codebook</p>
Empirical Study on Test Generation Using GitHub Copilot --- Replication Package
<p>This replication package contains the data and scripts used in the "Empirical Study on Test Generation Using GitHub Copilot" thesis. </p>
StackPilot: Contrasting Code Snippets from Stack Overflow and GitHub Copilot
<p>Copy-paste programming via Stack Overflow and code generation via GitHub Copilot both define a query/prompt-based programming model. To enable systematic comparison of code copied from Stack Overflow and code generated by GitHub Copilot, we provide a dataset of 30,746 code snippets that Stack Overflow and GitHub Copilot produced in response to the same 2,636 queries/prompts.</p>
Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study
<p>Este conjunto de dados foi gerado como parte do estudo "Code on Demand: A Comparative Analysis of the Efficiency, Understandability, and Self-Correction Capability of Copilot, ChatGPT, and Gemini - Data resulting from the study". O estudo focou na avaliação do desempenho das ferramentas Copilot, ChatGPT e Gemini, utilizando problemas do LeetCode em quatro linguagens de programação: Python, Java, JavaScript e C.</p> <p>O conjunto de dados atualizado está organizado nas seguintes pastas:</p> <ol> <li> <p><strong>c_programs</strong>: Esta pasta contém os scripts Python utilizados para calcular a complexidade ciclomática e a complexidade cognitiva do código C gerado pelas ferramentas.</p> <ul> <li><code>calculate_cyclomatic_complexity.py</code>: Script para calcular a complexidade ciclomática.</li> <li><code>calculate_cognitive_complexity.py</code>: Script para calcular a complexidade cognitiva.</li> </ul> </li> <li> <p><strong>codes_suggested_by_the_tools</strong>: Esta pasta contém as sugestões de código geradas pelo Copilot, ChatGPT e Gemini para cada problema do LeetCode.</p> <ul> <li>Subpastas: <code>ChatGPT</code>, <code>Copilot</code>, <code>Gemini</code>, cada uma contendo as sugestões de código correspondentes nos formatos das linguagens.</li> </ul> </li> <li> <p><strong>complexity_of_codes</strong>: Esta pasta contém dois arquivos CSV que fornecem os resultados da análise de complexidade para o código gerado.</p> <ul> <li><code>AI analysis results table - Cognitive.csv</code>: Resultados da complexidade cognitiva do código gerado.</li> <li><code>AI analysis results table - Cyclomatic.csv</code>: Resultados da complexidade ciclomática do código gerado.</li> </ul> </li> </ol> <p>Este conjunto de dados atualizado oferece insights valiosos sobre o desempenho das ferramentas de geração de código com IA e pode ser utilizado para análises futuras ou estudos de replicação.</p>
Dataset for "Deep Dive into the Verifiability of Code Generated by GitHub Copilot"
<p>A collection of GitHub-Copilot-generated Python solutions and their translations to Dafny with verification attempts.</p>
Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot
<p>Replication Package for "Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot"</p>
Dataset for "Exploring the Verifiability of Code Generated by GitHub Copilot"
<p>Collection of Python implementations and translations to Dafny with verification attempts.</p>
Gemini 1.0 Pro, Claude 3 Sonnet, Microsoft Copilot, and ChatGPT-4 responses on the Test of Understanding Graphs in Kinematics (TUG-K), April 2024
<p>The data contains 30 responses from each chatbot to 26 items on the TUG-K survey. The chatbots tested were Google Gemini (freely available version, Gemini Pro 1.0), Claude 3 Sonnet, Microsoft Copilot (freely available version, balanced setting) and ChatGPT-4 (subscription-based, ChatGPT Plus). The prompts consisted of screenshots of the test items and the sentence "Answer the question in the image" for Copilot and Gemini 1.0 Pro. For Claude 3 Sonnet and ChatGPT-4 the prompt consisted of the screenshot only. The data was collected in April 2024.</p> <p>The data is a continuation of the research data on ChatGPT-4's performance on the TUG-K (10.5281/zenodo.10429075).</p>
Dataset of the Paper "Copilot-in-the-Loop: Fixing Code Smells in Copilot-Generated Python Code using Copilot"
<p>This dataset contains a list of 102 code smells detected from Copilot-generated Python code, along with Python code files generated by Copilot from the <em>Repositories</em> and <em>Code</em> label, respectively. This dataset also includes Copilot Chat’s responses to fixing the 102 detected code smells. A brief description of each document and folder in the dataset is provided below:</p> <p><strong>1. files folder</strong></p> <p>contains 311 Python code files generated by Copilot. In the 311 Python files, 171 are retrieved under the <em>Repositories</em> label, indicating Python code files entirely generated by Copilot, and 140 are retrieved under the <em>Code</em> label, indicating Python code snippets generated by Copilot.</p> <p><strong>2. results of RQ1.xlsx</strong></p> <p>contains a list of 102 code smells detected from Copilot-generated Python code.</p> <p><strong>3. results of RQ2.xlsx</strong></p> <p>contains Copilot Chat’s responses to fixing the detected 102 code smells instructed by three prompts of varying detail levels.</p>
[DataSet] Avaliação Comparativa do GitHub Copilot e do Amazon CodeWhisperer na Geração Automatica de Código-Fonte
Open the record for dataset details and reuse information.
Replication package of the paper "Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot"
<h1>Replication Package</h1> <p>This replication package contains the necessary tools, data, and scripts for reproducing the results of our paper: "<em>Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot</em>". Below is a detailed description of the directory structure and the contents of this package.</p> <h2>Contents</h2> <p>The replication package is organized into two main directories:</p> <ul> <li> <p><code>assets</code>: This directory contains all .csv files used as input for the script and the outputted .csv file used to perform the manual and automated analyses for RQ1 and RQ2.</p> </li> <li> <p><code>script</code>: This directory contains all scripts for RQ1 and RQ2.</p> </li> </ul> <p>In the following, we describe the content of each directory:</p> <h2><code>assets</code></h2> <p>This directory contains the tools and resources required for our study.</p> <h3><code>dataset</code>: Contains the main datasets used in the study.</h3> <ul> <li> <p><code>annotationStore.csv</code>: Input dataset for our analyses, originating from the <em>CODESEARCHNET</em> dataset.</p> </li> <li> <p><code>queries.csv</code>: .csv file containing the queries used for the experiments filtered from the <em>CODESEARCHNET</em>dataset. This file contains the following columns:</p> <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the experiment</li> <li><em>GitHubUrl</em>: GitHub URL related to a snippet that addresses the query</li> <li><em>Relevance</em>: Relevance of the linked GitHub snippet to the query</li> </ul> </li> </ul> <h3><code>data</code>: Contains the datasets and results of all analyses.</h3> <ul> <li> <p><code>queries.csv</code>: General input queries. This file contains the following columns:</p> <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the snippet generation</li> <li><em>Prompt</em>: LLM prompt generated for the query as: <em>You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></em></li> </ul> </li> <li> <p><code>queries_filled.csv</code>: Similar to the previous file, but also containing the output produced by the LLM-based assistants. This file contains the following columns:</p> <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the snippet generation</li> <li><em>Prompt</em>: LLM prompt generated for the query as: <em>You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></em></li> <li><em>Notes</em>: General notes that provide additional context or information about the query or prompt.</li> <li><em>Gemini_Answer(n)</em>: The generated code snippets by Gemini.</li> <li><em>Gemini(n)</em>: The external links provided by Gemini.</li> <li><em>Prompt (repeated)</em></li> <li><em>Note</em>: Notes that provide additional context or information about the query or prompt.</li> <li><em>Copilot_Answer(n)</em>: The generated code snippets by Bing-Copilot.</li> <li><em>Copilot_Bing(n)</em>: The external links provided by Bing-Copilot.</li> </ul> </li> </ul> <h4><code>copilot</code> || <code>gemini</code>: Contains the data related to the specific LLM. These two subdirectories have the same internal structure.</h4> <ul> <li><code>queries.csv</code>: The <code>queries_filled.csv</code> file, filtered for the specific LLM.</li> <li><code>queries_noTrivial.csv</code>: Contains only the queries with at least one nontrivial generated snippet.</li> <li> <p><code>external_links.csv</code>: External links extracted from the LLMs output.</p> </li> <li> <p><code>external_links_filled.csv</code>: Snippets extracted from the external links.</p> <ul> <li><em>index</em>: Query ID</li> <li><em>source</em>: Snippet ID</li> <li><em>url</em>: Link URL</li> <li><em>note</em>: Notes that provide additional context or information about the query or prompt</li> <li><em>code(n)</em>: The n-th code snippet extracted from the source</li> </ul> </li> </ul> <h4><code>manual_analysis</code>: Manual analysis results.</h4> <ul> <li><code>manual_analysis.csv</code>: <ul> <li><em>index</em>: Query ID</li> <li><em>query</em>: Query used for the snippet generation</li> <li><em>generatedsnippet(n)</em>: The n-th code snippet generated by the LLM-based assistant</li> <li><em>trivial_1</em>: Manual analysis of whether or not the snippet was trivial (validator 1)</li> <li><em>trivial_2</em>: Manual analysis of whether or not the snippet was trivial (validator 2)</li> <li><em>trivial_final</em>: Manual analysis of whether or not the snippet was trivial (final classification if there is a disagreement)</li> <li><em>source</em>: URL to analyze</li> <li><em>sourcetype1</em>: Type of the source (validator 1)</li> <li><em>sourcetype2</em>: Type of the source (validator 2)</li> <li><em>sourcetypefinal</em>: Type of the source (final classification if there is a disagreement)</li> <li><em>relatedtoquery_1</em>: Relevance of the link to the query (validator 1)</li> <li><em>relatedtoquery_2</em>: Relevance of the link to the query (validator 2)</li> <li><em>relatedtoquery_final</em>: Relevance of the link to the query (final classification if there is a disagreement)</li> <li><em>relatedtosnippets_1</em>: Relevance of the generated snippet to those in the link (validator 1)</li> <li><em>relatedtosnippets_2</em>: Relevance of the generated snippet to those in the link (validator 2)</li> <li><em>relatedtosnippets_final</em>: Relevance of the generated snippet to those in the link (final classification if there is a disagreement)</li> </ul> </li> <li><code>manual_analysis_noTrivial.csv</code>: As in the previous file, but only the queries with at least one nontrivial generated code snippet.</li> </ul> <h4><code>clone_detector</code>: Output and intermediate files for clone detection with Copilot data.</h4> <ul> <li><code>copilot_tokens || gemini_tokens</code>: Contains the output the tokenization of the generated code snippets and the code snippets extracted from the external links.</li> <li><code>merged_llm_ext_link.csv</code>: All possible pairs (Cartesian product) (code snippet extracted from the external links, generated code snippet). This file is the input of the clone detection tool. <ul> <li><em>ID_query</em>: Query ID</li> <li><em>query</em>: Query used for the snippet generation</li> <li><em>language</em>: Programming language of the query</li> <li><em>generated_snippet</em>: The generated code snippet by the LLM-based assistant</li> <li><em>IDgensnippet</em>: The index of the generated code snippet</li> <li><em>LOCgensnippet</em>: The number of lines of code of the generated code snippet</li> <li><em>ID_source</em>: Source ID</li> <li><em>source</em>: Source URL</li> <li><em>source_snippet</em>: Code snippet extracted from the source</li> <li><em>IDsourcesnippet</em>: ID of the code snippet extracted from the source</li> <li><em>LOCsourcesnippet</em>: The number of lines of code of the code snippet extracted from the source</li> <li><em>note</em>: Notes that provide additional context or information about the query or prompt</li> </ul> </li> <li><code>clone_detection_output.csv</code>: Contains the clone detection results. <ul> <li><em>ID_query</em>: The index of the query</li> <li><em>query</em>: Query used for the snippet generation</li> <li><em>language</em>: The programming language of the query</li> <li><em>generated_snippet</em>: The generated code snippet by the LLM-based assistant</li> <li><em>IDgensnippet</em>: The index of the generated code snippet</li> <li><em>LOCgensnippet</em>: The number of lines of code of the generated code snippet</li> <li><em>ID_source</em>: Source ID</li> <li><em>source</em>: Source URL</li> <li><em>source_snippet</em>: Code snippet extracted from the source</li> <li><em>IDsourcesnippet</em>: ID of the code snippet extracted from the source</li> <li><em>LOCsourcesnippet</em>: The number of lines of code of the code snippet extracted from the source</li> <li><em>note</em>: Notes that provide additional context or information about the query or prompt</li> <li><em>clone_detected</em>: bBolean value that indicates whether a clone has been detected (1 = detected, 0 = not detected)</li> <li><em>cloning_ratio</em>: Ratio of the number of lines of code of the generated code snippet has been detected as a clone in the code snippet extracted from the source</li> <li><em>cloned_lines</em>: The number of lines of code of the generated code snippet that has been detected as a clone in the code snippet extracted from the source</li> </ul> </li> </ul> <h4><code>cosine_sim</code>: Cosine similarity results.</h4> <ul> <li><code>cosine_sim_output.csv</code>: Contains the cosine similarity results <ul> <li><em>query_id</em>: Query ID</li> <li><em>snippet_id</em>:ID the generated code snippet</li> <li><em>source_id</em>: ID of the source</li> <li><em>sourcesnippetid</em>: ID of the code snippet extracted from the source <ul> <li><em>cosine_similarity</em>: The cosine similarity between the generated code snippet and the code snippet extracted from the source</li> </ul> </li> </ul> </li> </ul> <h4><code>quant_analysis</code>: Quantitative analysis results.</h4> <ul> <li><code>topN_links_se.csv</code>: Contains the top-N links extracted from the search engine. <ul> <li><em>id</em>: Query ID</li> <li><em>query</em>: The query</li> <li><em>url</em>: Link URL</li> </ul> </li> <li><code>merged_clone_cosine.csv</code>: Contains the merged results of the clone detection and cosine similarity. <ul> <li><em>ID_query</em>: Query ID</li> <li><em>query</em>: The query</li> <li><em>language</em>: The programming language of the query</li> <li><em>generated_snippet</em>: The generated code snippet by the LLM-based assistant</li> <li><em>IDgensnippet</em>: The ID of the generated code snippet</li> <li><em>LOCgensnippet</em>: The number of lines of code of the generated code snippet</li> <li><em>ID_source</em>: The index of the source</li> <li><em>source</em>: The source URL</li> <li><em>source_snippet</em>: The code snippet extracted from the source</li> <li><em>IDsourcesnippet</em>: The index of the code snippet extracted from the source</li> <li><em>LOCsourcesnippet</em>: The number of lines of code of the code snippet extracted from the source</li> <li><em>note</em>: Notes that provide additional context or information about the query or prompt</li> <li><em>clone_detected</em>: Boolean value that indicates if a clone has been detected(1 = detected, 0 = not detected)</li> <li><em>cloning_ratio</em>: The ratio of the number of lines of code of the generated code snippet has been detected as a clone in the code snippet extracted from the source</li> <li><em>cloned_lines</em>: The number of lines of code of the generated code snippet that has been detected as a clone in the code snippet extracted from the source</li> <li><em>cosine_similarity</em>: The cosine similarity between the generated code snippet and the code snippet extracted from the source</li> </ul> </li> </ul> <h4><code>other_analysis</code>: Contains more performed analysis.</h4> <ul> <li><code>sample_queries.csv</code>: Contains a sample of five queries for language used for perform the chain of thought experiment. <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the snippet generation</li> <li><em>Prompt</em>: LLM prompt generated for the query as: <em>You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></em></li> </ul> </li> <li><code>chain_of_thought.csv</code>: <ul> <li><em>Language</em>: Programming language of the query</li> <li><em>Query</em>: Query used for the snippet generation</li> <li><em>Prompt</em>: LLM prompt generated for the query as: <em>You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></em></li> <li><em>Clone_fonud</em>: Boolean value that indicates if a clone has been detected (Yes = detected, No = not detected)</li> <li><em>Note</em>: Notes that provide additional context or information about the performed analysis</li> </ul> </li> <li><code>data_check.csv</code>: <ul> <li><em>Link</em>: URL of the source provided by the LLM</li> <li><em>Post_date</em>: Indicates if the date of the post is before/after the date of training of the LLM (before 2023, after 2023, not provided)</li> <li><em>Note</em>: Notes that provide additional context or information about the performed analysis</li> </ul> </li> </ul> <h4><code>results</code>: Final analysis results.</h4> <ul> <li><code>jaccard_analysis.csv</code>: Contains the results of the Jaccard analysis comparing the provided external links by the LLMs with the top-N links extracted from the corresponding search engine. <ul> <li><em>id</em>: Query ID</li> <li><em>language</em>: The programming language of the query</li> <li><em>llm_link</em>: The external links provided by the LLM</li> <li><em>llmlinksize</em>: The number of external links provided by the LLM</li> <li><em>overlap_links</em>: The overlapping links between the LLM and the search engine</li> <li><em>overlap_size</em>: The number of overlapping links between the LLM and the search engine</li> <li><em>nonoverlaplinks</em>: The non-overlapping links between the LLM and the search engine</li> <li><em>union_size</em>: The size of the union set links between the LLM and the search engine</li> <li><em>jaccard</em>: The Jaccard similarity between the LLM and the search engine</li> </ul> </li> <li><code>merged_analysis.csv</code>: Contains the merged results of the manual and quantitative analyses. <ul> <li><em>id</em>: The index of the query</li> <li><em>query</em>: The query used for the experiment</li> <li><em>trivial_final(n)</em>: The final assignment for the triviality of the n-th generated code snippet</li> <li><em>source</em>: The URL of the source</li> <li><em>sourcetypefinal</em>: The final assignment for the type of the source</li> <li><em>relatedtoquery_final</em>: The final assignment for the relevance of the generated code snippet to the query</li> <li><em>relatedtosnippets_final</em>: The final assignment for the relevance of the generated code snippet to the source</li> <li><em>cloning_ratio</em>: The maximum cloning ratio between the generated code snippet and all the code snippets extracted from the source</li> <li><em>cosine_similarity</em>: The cosine similarity related to the snippets with maximum cloning ratio</li> </ul> </li> </ul> <h3><code>cccfindersw-configuration-files</code>:</h3> <p>Contains additional configuration files for the CCFinderSW clone detection tool. The files are <code>javascript_comment.txt</code> and <code>javascript_reserved.txt</code>. They must be placed in the tool's <code>comment/</code> and <code>reserved/</code> directories.</p> <h3><code>appendix.tex</code>: The appendix of the paper containing:</h3> <ul> <li><em>Table 1</em>: Number of links of different types provided by Gemini and Bing CoPilot</li> </ul> <h3><code>appendix.pdf</code>: The appendix of the paper in PDF format.</h3> <h2><code>script</code></h2> <p>This directory contains our scripts (mostly Python, an R script and an Applescript) to preprocess data and run the clone detection analyses.</p> <ul> <li><code>1_dateset_filtering.py</code>: Script to filter the dataset. The input of this script is the <code>annotationStore.csv</code> file, and the output is the <code>queries.csv</code> file.</li> <li><code>2_prompt_generation.py</code>: Script to generate prompts for the LLM-based assistants. The input of this script is the <code>queries.csv</code> file, and the output is the <code>queries_filled.csv</code> file.</li> <li><code>3_gen_sheet_sources_extraction.py</code>: Script to split the external links provided by the LLM, one for each row. The input of this script is the <code>queries_filled.csv</code> file, and the output is the <code>external_link.csv</code> file.</li> <li><code>4_ext_link_snippet_extraction.py</code>: Script to extract the snippets from Web URLs. It only works for the most popular domains. The input of this script is the <code>external_links.csv</code> file, and the output is the <code>external_links_filled.csv</code> file.</li> <li><code>5_top_n_link_SearchEngine.py</code>: Script to perform top-N link search using the corresponding search engines (Google Search and Bing). The input of this script is the <code>queries_filled.csv</code> file. It executes the <code>browser_bot.scpt</code>. The output is the <code>topN_links_se.csv</code> file. <ul> <li><code>browser_bot.scpt</code>: Script for browser automation (AppleScript).</li> </ul> </li> <li><code>6_se_vs_llm.py</code>: Script to compare (using the Jaccard metric) the links returned by the corresponding search engines with those provided by the LLM-based assistants. The input of this script is the <code>external_links.csv</code> file and the <code>topN_links_se.csv</code> file. The output is the <code>jaccaard_analysis.csv</code> file.</li> <li><code>7_results_manual_analysis.py</code>: Script to extract results and statistical analyses performed on the manual analysis and reported in the tables in the paper.</li> <li><code>8_merge_gen_source_snippets.py</code>: This script takes as input: <code>{llm}/queries.csv</code> and <code>{llm}/external_link_filled.csv</code> to merge them and generates an expanded one, i.e., one in which we have on each line a snippet extracted from the source, this will be the input of our final script for clone detection. The output is the <code>merged_llm_ext_link.csv</code> file.</li> <li><code>9_clone_detection.py</code>: Script to perform clone detection. The input of this script is the <code>merged_llm_ext_link.csv</code> file, and the output is the <code>clone_detection_output.csv</code> file.</li> <li><code>10_cosine_sim_check.py</code>: Script to compute the code snippets' cosine similarity. The script takes as input the tokenized files from the <code>{llm}_tokens</code> directory. The output is the <code>cosine_sim_output.csv</code>file.</li> <li><code>11_merger_clone_cosine.py</code>: Script to merge clone detection and cosine similarity results. The input of this script is the <code>clone_detection_output.csv</code> and the <code>cosine_sim_output.csv</code> files, and the output is the <code>merged_clone_cosine.csv</code> file.</li> <li><code>12_merge_manual_quantitative_analysis.py</code>: Script to merge manual and quantitative analysis results. The inputs of this script are the <code>manual_analysis.csv</code> and the <code>merged_clone_cosine.csv</code>files, and the output is the <code>merged_analysis.csv</code> file.</li> <li><code>13_sample_for_COT_analysis.py</code>: Script to collect the sample of queries on which we perform the chain of thought analysis, the output is the <code>sample_queries.csv</code> file.</li> <li><code>14_llm_vs_csn.py</code>: Script to check the overlap between the links provided by the LLM and the one associated with the related query in the <em>CodeSearchNet</em> dataset. The input of this script is the <code>queries.csv</code>and <code>queries_noTrivial.csv</code> file.</li> <li><code>cloningGraph.R</code>: R script to generate the cloning graph. The input of this script is the <code>merged_analysis.csv</code> file.</li> </ul>
Copilot CWE Scenarios Dataset
<p>The dataset and source code and result generation framework used in the paper 'Asleep at the Keyboard? Assessing the Security of GitHub Copilot’s Code Contributions'. It includes 89 different scenarios.</p>
Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic? - Data
<p>Generated programs from <a href="https://arxiv.org/abs/2210.14699">Piloting Copilot and Codex: Hot Temperature, Cold Prompts, or Black Magic?</a></p>
Replication Package of "Code Generative Techniques and TODO Comments: Friends or Foes? An Evaluation of GitHub Copilot"
<p>The data and scripts contributed by the efforts in "Code Generative Techniques and TODO Comments: Friends or Foes? An Evaluation of GitHub Copilot".</p>
Dataset of the Paper "Demystifying Practices, Challenges and Expected Features of Using GitHub Copilot"
<p>This dataset collected from Stack Overflow (SO) and GitHub was used to conduct an empirical study on investigating the practices, challenges, and expected features of using GitHub Copilot. We provide below a brief description of each file in the dataset:</p> <p><strong>1. Dataset (SO).xlsx</strong></p> <p>contains the IDs and URLs of the labelled posts which are related to Copilot from SO, and the data extracted from these related SO posts.</p> <p><strong>2. Dataset (GitHub).xlsx</strong></p> <p>contains the discussion IDs and URLs in the Copilot category of GitHub Discussions, and the data extracted from the relevant discussions.</p> <p><strong>3. Extracted Data (SO+GitHub).xlsx</strong></p> <p>provides the final results of the data extracted from SO posts and GitHub discussions.</p>
Replication Package for "Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot"
<p>This is the replication package accompanying the submission of "Are Prompt Engineering and TODO Comments Friends or Foes? An Evaluation on GitHub Copilot"</p>
COPILOT-HF: Cooperative Program for ImpLementation of Optimal Therapy in Heart Failure
ClinicalTrials.gov study NCT05734690. IPD Sharing: NO. Countries: 1. Publications: 1.
AI-Assisted Analgesia Copilot System
ClinicalTrials.gov study NCT07253012. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Collaborative Power Mobility Innovative Learning OpporTunity (CoPILOT) - A Pilot Study of a New Training Approach (Phase 1)
ClinicalTrials.gov study NCT02320786. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.