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zenodo32/100

Replication package for: Network formation and efficiency in linear-quadratic games: An experimental study

This is a replication package for the manuscripted titled "Network formation and efficiency in linear-quadratic games: An experimental study" published in the Economic Journal.

opencc-by-4.0Jul 2024View details →

zenodo32/100

Replication Package for "Early Career Developers' Perceptions of Code Understandability. A Study of Complexity Metrics"

<div> <div> <div> <div> <div> <h2>Authors</h2> <ul> <li>Matteo Esposito, University of Oulu, Finland</li> <li>Andrea Janes, Free University of Bozen-Bolzano, Italy</li> <li>Terhi Kilamo, University of Tampere, Finland</li> <li>Valentina Lenarduzzi, University of Oulu, Finland</li> </ul> <h2>Content Overview</h2> This replication package contains the following materials: <ul> <li>Tables: Excel files that include all hypothesis testing data, including normality tests.</li> <li>Data: RAW Questionarie datasett.</li> </ul> <h2>Contact Information</h2> For any issues, questions, or further assistance, please do not hesitate to contact the authors of the paper. We are here to help! </div> </div> </div> </div> </div>

opencc-by-4.0Jul 2024View details →

zenodo32/100

Migration of Monolithic Systems to Microservices: A Systematic Mapping Study - Replication package

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →

zenodo32/100

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> This replication package contains the necessary tools, data, and scripts for reproducing the results of our paper: "Do LLMs Provide Links to Code Similar to what they Generate? A Study with Gemini and Bing CoPilot". Below is a detailed description of the directory structure and the contents of this package. <h2>Contents</h2> The replication package is organized into two main directories: <ul> <li> <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. </li> <li> <code>script</code>: This directory contains all scripts for RQ1 and RQ2. </li> </ul> In the following, we describe the content of each directory: <h2><code>assets</code></h2> This directory contains the tools and resources required for our study. <h3><code>dataset</code>: Contains the main datasets used in the study.</h3> <ul> <li> <code>annotationStore.csv</code>: Input dataset for our analyses, originating from the CODESEARCHNET dataset. </li> <li> <code>queries.csv</code>: .csv file containing the queries used for the experiments filtered from the CODESEARCHNETdataset. This file contains the following columns: <ul> <li>Language: Programming language of the query</li> <li>Query: Query used for the experiment</li> <li>GitHubUrl: GitHub URL related to a snippet that addresses the query</li> <li>Relevance: 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> <code>queries.csv</code>: General input queries. This file contains the following columns: <ul> <li>Language: Programming language of the query</li> <li>Query: Query used for the snippet generation</li> <li>Prompt: LLM prompt generated for the query as: You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></li> </ul> </li> <li> <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: <ul> <li>Language: Programming language of the query</li> <li>Query: Query used for the snippet generation</li> <li>Prompt: LLM prompt generated for the query as: You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></li> <li>Notes: General notes that provide additional context or information about the query or prompt.</li> <li>Gemini_Answer(n): The generated code snippets by Gemini.</li> <li>Gemini(n): The external links provided by Gemini.</li> <li>Prompt (repeated)</li> <li>Note: Notes that provide additional context or information about the query or prompt.</li> <li>Copilot_Answer(n): The generated code snippets by Bing-Copilot.</li> <li>Copilot_Bing(n): 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> <code>external_links.csv</code>: External links extracted from the LLMs output. </li> <li> <code>external_links_filled.csv</code>: Snippets extracted from the external links. <ul> <li>index: Query ID</li> <li>source: Snippet ID</li> <li>url: Link URL</li> <li>note: Notes that provide additional context or information about the query or prompt</li> <li>code(n): 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>index: Query ID</li> <li>query: Query used for the snippet generation</li> <li>generatedsnippet(n): The n-th code snippet generated by the LLM-based assistant</li> <li>trivial_1: Manual analysis of whether or not the snippet was trivial (validator 1)</li> <li>trivial_2: Manual analysis of whether or not the snippet was trivial (validator 2)</li> <li>trivial_final: Manual analysis of whether or not the snippet was trivial (final classification if there is a disagreement)</li> <li>source: URL to analyze</li> <li>sourcetype1: Type of the source (validator 1)</li> <li>sourcetype2: Type of the source (validator 2)</li> <li>sourcetypefinal: Type of the source (final classification if there is a disagreement)</li> <li>relatedtoquery_1: Relevance of the link to the query (validator 1)</li> <li>relatedtoquery_2: Relevance of the link to the query (validator 2)</li> <li>relatedtoquery_final: Relevance of the link to the query (final classification if there is a disagreement)</li> <li>relatedtosnippets_1: Relevance of the generated snippet to those in the link (validator 1)</li> <li>relatedtosnippets_2: Relevance of the generated snippet to those in the link (validator 2)</li> <li>relatedtosnippets_final: 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>ID_query: Query ID</li> <li>query: Query used for the snippet generation</li> <li>language: Programming language of the query</li> <li>generated_snippet: The generated code snippet by the LLM-based assistant</li> <li>IDgensnippet: The index of the generated code snippet</li> <li>LOCgensnippet: The number of lines of code of the generated code snippet</li> <li>ID_source: Source ID</li> <li>source: Source URL</li> <li>source_snippet: Code snippet extracted from the source</li> <li>IDsourcesnippet: ID of the code snippet extracted from the source</li> <li>LOCsourcesnippet: The number of lines of code of the code snippet extracted from the source</li> <li>note: 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>ID_query: The index of the query</li> <li>query: Query used for the snippet generation</li> <li>language: The programming language of the query</li> <li>generated_snippet: The generated code snippet by the LLM-based assistant</li> <li>IDgensnippet: The index of the generated code snippet</li> <li>LOCgensnippet: The number of lines of code of the generated code snippet</li> <li>ID_source: Source ID</li> <li>source: Source URL</li> <li>source_snippet: Code snippet extracted from the source</li> <li>IDsourcesnippet: ID of the code snippet extracted from the source</li> <li>LOCsourcesnippet: The number of lines of code of the code snippet extracted from the source</li> <li>note: Notes that provide additional context or information about the query or prompt</li> <li>clone_detected: bBolean value that indicates whether a clone has been detected (1 = detected, 0 = not detected)</li> <li>cloning_ratio: 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>cloned_lines: 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>query_id: Query ID</li> <li>snippet_id:ID the generated code snippet</li> <li>source_id: ID of the source</li> <li>sourcesnippetid: ID of the code snippet extracted from the source <ul> <li>cosine_similarity: 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>id: Query ID</li> <li>query: The query</li> <li>url: 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>ID_query: Query ID</li> <li>query: The query</li> <li>language: The programming language of the query</li> <li>generated_snippet: The generated code snippet by the LLM-based assistant</li> <li>IDgensnippet: The ID of the generated code snippet</li> <li>LOCgensnippet: The number of lines of code of the generated code snippet</li> <li>ID_source: The index of the source</li> <li>source: The source URL</li> <li>source_snippet: The code snippet extracted from the source</li> <li>IDsourcesnippet: The index of the code snippet extracted from the source</li> <li>LOCsourcesnippet: The number of lines of code of the code snippet extracted from the source</li> <li>note: Notes that provide additional context or information about the query or prompt</li> <li>clone_detected: Boolean value that indicates if a clone has been detected(1 = detected, 0 = not detected)</li> <li>cloning_ratio: 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>cloned_lines: 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>cosine_similarity: 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>Language: Programming language of the query</li> <li>Query: Query used for the snippet generation</li> <li>Prompt: LLM prompt generated for the query as: You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></li> </ul> </li> <li><code>chain_of_thought.csv</code>: <ul> <li>Language: Programming language of the query</li> <li>Query: Query used for the snippet generation</li> <li>Prompt: LLM prompt generated for the query as: You are a Senior <code><Language></code> developer. Then give me a <code><Language></code> code snippet about: <code><Query></code></li> <li>Clone_fonud: Boolean value that indicates if a clone has been detected (Yes = detected, No = not detected)</li> <li>Note: Notes that provide additional context or information about the performed analysis</li> </ul> </li> <li><code>data_check.csv</code>: <ul> <li>Link: URL of the source provided by the LLM</li> <li>Post_date: 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>Note: 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>id: Query ID</li> <li>language: The programming language of the query</li> <li>llm_link: The external links provided by the LLM</li> <li>llmlinksize: The number of external links provided by the LLM</li> <li>overlap_links: The overlapping links between the LLM and the search engine</li> <li>overlap_size: The number of overlapping links between the LLM and the search engine</li> <li>nonoverlaplinks: The non-overlapping links between the LLM and the search engine</li> <li>union_size: The size of the union set links between the LLM and the search engine</li> <li>jaccard: 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>id: The index of the query</li> <li>query: The query used for the experiment</li> <li>trivial_final(n): The final assignment for the triviality of the n-th generated code snippet</li> <li>source: The URL of the source</li> <li>sourcetypefinal: The final assignment for the type of the source</li> <li>relatedtoquery_final: The final assignment for the relevance of the generated code snippet to the query</li> <li>relatedtosnippets_final: The final assignment for the relevance of the generated code snippet to the source</li> <li>cloning_ratio: The maximum cloning ratio between the generated code snippet and all the code snippets extracted from the source</li> <li>cosine_similarity: The cosine similarity related to the snippets with maximum cloning ratio</li> </ul> </li> </ul> <h3><code>cccfindersw-configuration-files</code>:</h3> 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. <h3><code>appendix.tex</code>: The appendix of the paper containing:</h3> <ul> <li>Table 1: 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> This directory contains our scripts (mostly Python, an R script and an Applescript) to preprocess data and run the clone detection analyses. <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 CodeSearchNet 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>

opencc-by-4.0Nov 2024View details →

zenodo32/100

Replication package: Code Comprehension Confounders: A Study of Intelligence and Personality

Replication package for: S. Wagner and M. Wyrich, "Code Comprehension Confounders: A Study of Intelligence and Personality," in IEEE Transactions on Software Engineering, vol. 48, no. 12, pp. 4789-4801, 1 Dec. 2022, doi: 10.1109/TSE.2021.3127131. - The `data` folder contains dataset and R analysis script. We recommend calling `setwd()` before running the script contents, so that the dataset can be properly loaded. - the `materials` folder contains the experimental code snippets and the translated task sheets to evaluate code comprehension performance. Please note that the raw data does not contain the complete data set as we only make the data of those participants public that explicitly agreed to it (which applies to 130 of the 135 participants).

opencc-by-4.0Jun 2021View details →

zenodo32/100

How Tertiary Studies perform Quality Assessment of Secondary Studies in Software Engineering - Replication Package

Replication Package for the paper: D. Costal, C. Farré, X. Franch, C. Quer. 2021. How Tertiary Studies perform Quality Assessment of Secondary Studies in Software Engineering. CIbSE 2021. Please refer to the above paper if you want to cite/use this data.

opencc-by-4.0May 2021View details →

zenodo32/100

Inclusion and Exclusion Criteria in Software Engineering Tertiary Studies: A Systematic Mapping and Emerging Framework - Replication Package

Replication Package for the paper: D. Costal, C. Farré, X. Franch, C. Quer. 2021. Inclusion and Exclusion Criteria in Software Engineering Tertiary Studies: A Systematic Mapping and Emerging Framework. ESEM '21, <a href="https://doi.org/10.1145/3475716.3484190">https://doi.org/10.1145/3475716.3484190</a> Please refer to the above paper if you want to cite/use this data.

opencc-by-4.0Jul 2021View details →

zenodo32/100

Replication package for the paper "Do Comments follow Commenting Conventions? A case study in Java and Python"

<pre><code class="language-markdown"># RP-comment-convention-adherence-Java-Python Replication Package for the paper "Do Comments follow Commenting Conventions? A case study in Java and Python". It uses the dataset provided by Rani et.al.'s work [How to identify class comment types? A multi-language approach for class comment classification](https://github.com/poojaruhal/RP-class-comment-classification). ## Structure ``` RQ1/ RQ1_Java_Rules.xlsx RQ1_Python_Rules.xlsx RQ2/ RQ1_Java_Comments_Validated.xlsx RQ1_Python_Comments_Validated.xlsx Raw-projects/ Java_projects/ eclipse.zip guava.zip guice.zip hadoop.zip spark.zip vaadin.zip Python_projects/ django.zip ipython.zip Mailpile.zip pandas.zip pipenv.zip pytorch.zip requests.zip Style-guides ``` ## Contents of the Replication Package --- - **RQ1/** - contains the data used to answer RQ1 - `RQ1_Java_Rules.xlsx` - contains comment-related rules extracted from various Java style guidelines. Various tabs in the sheet represent the rules extracted from standard or project-specific guidelines. Oracle and Google are the standard guidelines, and the remaining are specific to the projects. - `RQ1_Python_Rules.xlsx` - contains comment-related rules extracted from various Python style guidelines. Various tabs in the sheet represent the rules extracted from standard or project-specific guidelines. PEP, Numpy, and Google are the standard guidelines and the remaining are specific to the projects. - **RQ2/** - contains the data used to answer RQ2 - `RQ2_Java_Comments_Validated.xlsx` - contains Java comment dataset used from the previous work and validated against the rules from their corresponding guidelines. Various tabs in the sheet represent various Java projects used in the work. The rows in each tab show the sample class comments used to validate against the rules. The rules are shown in the columns. - `RQ2_Python_Comments_Validated.xlsx` - contains Python comment dataset used from the previous work and validated against the rules from their corresponding guidelines. Various tabs in the sheet represent various Java projects used in the work. The rows in each tab show the sample class comments used to validate against the rules. The rules are shown in the columns. - **Raw-projects/** contains the raw projects of each language that are used to analyze class comments. - **Java_projects/** - `eclipse.zip` - Eclipse project downloaded from the GitHub. More detail about the project is on https://github.com/eclipse - `guava.zip` - Guava project downloaded from the GitHub. More detail about the project is on https://github.com/google/guava - `guice.zip` - Guice project downloaded from the GitHub. More detail about the project is on https://github.com/google/guice - `hadoop.zip` - Apache Hadoop project downloaded from the GitHub. More detail about the project is on https://github.com/apache/hadoop - `spark.zip` - Apache Hadoop project downloaded from the GitHub. More detail about the project is on https://github.com/apache/spark - `vaadin.zip` - Vaadin project downloaded from the GitHub. More detail about the project is on https://github.com/vaadin/framework - **Python_projects/** - `django.zip` - Django project downloaded from the GitHub. More detail about the project is on https://github.com/django. - `ipython.zip` - IPython project downloaded from the GitHub. More detail about the project is on https://github.com/ipython/ipython - `Mailpile.zip` - Mailpile project downloaded from the GitHub. More detail about the project is on https://github.com/mailpile/Mailpile - `pandas.zip` - pandas project downloaded from the GitHub. More detail about the project is on https://github.com/pandas-dev/pandas - `pipenv.zip` - Pipenv project downloaded from the GitHub. More detail about the project is on https://github.com/pypa/pipenv - `pytorch.zip` - PyTorch project downloaded from the GitHub. More detail about the project is on https://github.com/pytorch/pytorch - `requests.zip` - Requests project downloaded from the GitHub. More detail about the project is on https://github.com/psf/requests/ - **Style-guides/**- contains the style guidelines used for the selected projects. ---</code></pre>

opencc-by-4.0Aug 2021View details →

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Replication package for Seawalls and Stilts: A Quantitative Macro Study of Climate Adaptation

This replication package contains the data sets and code necessary to reproduce the results in Fried, Stephie. "Seawalls and Stilts: A Quantitative Macro Study of Climate Adaptation." accepted for publication at the Review of Economic Studies.

opencc-by-4.0Aug 2021View details →

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Replication Kit: "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects"

Replication Kit for the Paper "Are Unit and Integration Test Definitions Still Valid for Modern Java Projects? An Empirical Study on Open-Source Projects" This additional material shall provide other researchers with the ability to replicate our results. Furthermore, we want to facilitate further insights that might be generated based on our data sets. Structure The structure of the replication kit is as follows: <ul> <li>additional_visualizations: contains additional visualizations (Venn-Diagrams) for each projects for each of the data sets that we used</li> <li>data_analysis: contains python scripts that we used to analyze our raw data</li> <li>data_collection_tools: contains all source code used for the data collection, including the used versions of the <a href="https://github.com/comfort-framework">COMFORT framework</a>, the <a href="https://github.com/ftrautsch/BugFixClassifier">BugFixClassifier</a>, and the used tools of the <a href="https://github.com/smartshark">SmartSHARK environment</a>;</li> <li>mongodb_no_authors: Archived dump of our MongoDB that we created by executing our data collection tools. The "comfort" database can be restored via the mongorestore command.</li> </ul> Additional Visualizations We provide two additional visualizations for each project: 1) <project_name>\_disj\_ieee\_venn (visualizations for the DISJ data set) 2) <project_name>\_all\_ieee\_venn (visualizations for the ALL data set) For each of these data sets there exist one visualization for each project that shows four Venn-Diagrams for each of the different defect types. These Venn-Diagrams show the number of defects that were detected by either unit, or integration tests (or both). Furthermore, we added boxplots for each of the data sets (i.e., ALL and DISJ) showing the scores of unit and integration tests for each defect type. Analysis scripts Requirements: - python3.5 - tabulate - scipy - seaborn - mongoengine - pycoshark - pandas - matplotlib Both python files contain all code for the statistical analysis we performed. Data Collection Tools We provide all data collection tools that we have implemented and used throughout our paper: <ul> <li>BugFixClassifier: Used to classify our defects.</li> <li>comfort-core: Core of the comfort framework. Used to classify our tests into unit and integration tests and calculate different metrics for  these tests.</li> <li>comfort-jacoco-listner: Used to intercept the coverage collection process as we were executing the tests of our case study projects.</li> <li>jSHARK: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Java). </li> <li>pycoSHARK: Library that contains models for the used ORM mapper that is used inside the SmartSHARK environment (for Python).</li> <li>tools-changedistiller: Version of ChangeDistiller that we used within our comfort-core framework.</li> <li>vcsSHARK: Used to collect data from the VCSs of the projects.</li> </ul>

opencc-by-sa-4.0Dec 2018View details →

zenodo32/100

Leveraging the Crowd to Assess the Risk of Automated Dependency Updates: A Study on the Compatibility Score - Replication Package

Replication package for FSE 2023 submission: Leveraging the Crowd to Assess the Risk of Automated Dependency Updates: A Study on the Compatibility Score Includes datasets used for the study and additional appendix material.

opencc-by-4.0Jan 2023View details →

zenodo32/100

Replication package for our TOSEM paper entitled "An Empirical Study on GitHub Pull Requests' Reactions"

This package contains our dataset and the source code used to collect data from the the top 10,000 most starred GitHub repositories, and the selected six repositories (i.e., Cataclysm-DDA, Julia, Laravel, Node, RPCS3 and Rust), as well as the source code to analyze the data and generate all the figures in the paper.  Please carefully read the README.md file for more details.

opencc-by-4.0Feb 2023View details →

dryad32/100

Restoration and replication: a case study on the value of computational reproducibility assessment

Open science is vital to the interdisciplinary field of ecology due to its integrative nature and use of longitudinal datasets that build upon earlier data collections. To highlight the importance of open science in the rapidly growing discipline of restoration ecology, we conducted a 'computational reproducibility' assessment of a publication on a mining restoration program spanning several decades and over 250 km2 in a global biodiversity hotspot. Open data and code provided alongside the original publication were assessed for consistency with the results and conclusions of the original publication, as were potential limitations in findings due to the methodology. The impacts of inconsistencies and limitations were qualitatively assessed against the key findings from the publication and data were re-analysed where impacts were potentially significant. Of the six inconsistencies and limitations identified, two had a significant impact on five of the 11 key findings of the original publication, and one new key finding was made. The impact of this is of high ecological significance as the findings related to key restoration parameters: species richness (similarity of species richness between forest and 25-year-old restoration), functional diversity (correlation of species richness and functional diversity), and the restoration trajectory (long term trends and restored areas' resilience to disturbance). These outcomes highlight the importance of open data and the value of detailed third-party data reviews, particularly in restoration ecology which relies on research findings to inform decision-making and policy and drive adaptive management.

opencc-zeroJun 2023View details →

zenodo32/100

Replication Package for "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code"

This repository contains the replication package for the paper "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code" by Max Hort, Anastasiia Grishina, and Leon Moonen, accepted for publication in the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023). The paper is deposited on arXiv, will be available later at the publisher's site (<a href="https://ieeexplore.ieee.org/Xplore/home.jsp">IEEE</a>), and a copy is included in this repository. The replication package is archived on Zenodo with DOI: <a href="https://doi.org/10.5281/zenodo.8058667">10.5281/zenodo.8058667</a>. The data is distributed under the CC BY 4.0 license.   Citation If you build on this data or code, please cite this work by referring to the paper: <pre><code>@inproceedings{hort2023:sharing, title = {An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code}, author = {Max Hort and Anastasiia Grishina and Leon Moonen}, booktitle = {17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023)}, year = {2023}, publisher = {IEEE} note = {To appear. Pre-print on arXiv.} }</code></pre>

opencc-by-4.0Jul 2023View details →

zenodo32/100

Replication package for "An Exploratory Eye Tracking Study on How Developers Classify and Debug Python Code in Different Paradigms"

See the README.md file for more details.

opencc-by-4.0Sep 2023View details →

zenodo32/100

Replication Package of the Paper: An Empirical Study of Untangling Patterns of Two-Class Dependency Cycles

This is a replication package of the paper: "An Empirical Study of Untangling Patterns of Two-Class Dependency Cycles".This replication package contains:<ul><li>all-projects.xlsx: list 38 projects' demographic information.</li><li>rq1-recurring-patterns: contains successful untangling cases into each pattern we manually inspected. In one case, it contains:<ul><li>src/: src code before and after a commit</li><li>cycle-dependency-change/: cyclic files presented in dependency graph before and after a commit</li><li>all-modified-files-dependency-change/: all modified files presented in dependency graph before and after a commit</li><li>diff.txt: code diff changes</li><li>refactor.json: mined refactoring operations from RefactoringMiner 2.0.0.</li></ul></li><li>rq3-counterintuitive-solutions: contain counterintuitive cases dependency cycles are not resolved. The file structure of each case is the same as above.</li><li>rq2-design-relations contains:<ul><li>dependency-relations.csv: dependency relations inside and outside dependency cycle. Column 2-25 contains dependency information inside dependency cycle. Column 26-77 contain dependency information outside dependency cycle.</li><li>dependency-type-inside-cycle.csv lists 24 unique dependency types inside dependency cycle.</li><li>dependency-type-outside-cycle.csv lists 52 unique dependency types inside dependency cycle.</li></ul></li><li>SrcCode_CycleUntangleEmpirical: source code to generate data and replicate the results. It contains a README file to explain the purpose of each module.</li></ul>

opencc-by-4.0Oct 2023View details →

zenodo32/100

Replication package for: "Revisiting Event Study Designs: Robust and Efficient Estimation"

This replication package contains the code and instructions necessary to replicate Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. "Revisiting event study designs: Robust and efficient estimation." Forthcoming Review of Economic Studies (2023).

opencc-by-4.0Aug 2023View details →

ClinicalTrials.gov32/100

A Study of Toca 511, a Retroviral Replicating Vector, Combined With Toca FC in Patients With Solid Tumors or Lymphoma (Toca 6)

ClinicalTrials.gov study NCT02576665. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

An Antiretroviral Treatment Interruption (ATI) Study to Evaluate the Impact of Genetically Modified Autologous Cells (AGT103-T) to Suppress Human Immunodeficiency Virus Replication in the Absence of A

ClinicalTrials.gov study NCT05540964. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.

restrictedIPD-UNDECIDEDFeb 2026View details →

ClinicalTrials.gov32/100

A Study of a Retroviral Replicating Vector Combined With a Prodrug Administered to Patients With Recurrent Malignant Glioma

ClinicalTrials.gov study NCT01156584. IPD Sharing: Not stated. Countries: 1. Publications: 2.

restrictedIPD-UNDECIDEDFeb 2026View details →

ScienceDex guides

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record