Find research datasets worth reusing

opencc-by-4.0Sep 2024View details →

Replication package for "Application Barriers and the Socioeconomic Gap in Child Care Enrollment"

This package contains the readme, data, and dofiles to replicate the results of the paper "Application Barriers and the Socioeconomic Gap in Child Care Enrollment" by Henning Hermes, Philipp Lergetporer, Frauke Peter & Simon Wiederhold forthcoming at JEEA.

AugmenTest: Enhancing Tests with LLM-driven Oracles - Replication package

Replication package for the paper "AugmenTest: Enhancing Tests with LLM-driven Oracles"

Replication Package for "Using Reinforcement Learning to Sustain the Performance of Version Control Repositories"

The replication package is organized into two containers, each of which is responsible for reproducing figures and analyses for each RQ. ## Decompress Package ``` $ tar -xvJf rl4monorepos.tar.xz ``` ## RQ1 1. Import the container ``` $ docker import rq1.tar rq1 ``` 2. Regenerate figures ``` $ docker container run -v <outputdir>:/out -e R_SCRIPT=figures.R rq1  ``` 3. Re-execute statistical tests ``` $ docker container run -e R_SCRIPT=stats-test.R rq1 ``` ## RQ2 1. Import the container ``` $ docker import rq2.tar rq2 ``` 2. Regenerate figures and print AUC values ``` $ docker container run -v <outputdir>:/out -e R_SCRIPT=figures.R rq2  ```

Replication Package for ISAR and Change Impact Rules

All projects P1 ../FudanSELab/train-ticket P2 ../apssouza22/java-microservice P3 ../shabbirdwd53/Springboot-Microservice  P4 ../mdeket/spring-cloud-movie-recommendation  P5 ../piomin/sample-spring-microservices-new  P6 ../koushikkothagal/spring-boot-microservices-workshop  P7 ../anilallewar/microservices-basics-spring-boot  P8 ../AthirsonSilva/spring-microservices  are separated by a folder with their name and include the following <ul> <li>A list of Intermediate Representation files for each commit (JSON)</li> <li>A list of Delta files for each commit (JSON)</li> <li>An output file for rule violations (found)</li> <li>An output file for rule violation validation (validated by 2 anonymized researchers)</li> </ul> Train ticket also contains validation of 25 manually found changes that need focus review used to validate these were identified.

Replication Package of the Paper "Towards Better Understanding of Breaking Changes in the NPM Ecosystem"

Replication Package of the paper "Towards Better Comprehension of Breaking Changes in the NPM Ecosystem"   We describe the files in this replication package as follows.   collected_data folder: This folder contains the our collected breaking changes from 381 sampled NPM projects. The files include: 1) sampled_projects.txt, the 381 projects we sampled. 2) original_breaking_commits.csv, the breaking commits obtained from the 381 projects. 3) breaking_commits_after_removal.csv, it contains the breaking commits after removing (1) non-JavaScript source code change, (2) contain very long commit messages over 10 lines.   RQ1 folder: This folder contains the breaking changes used in RQ1. 1) documented_bc_can_be_detected.csv, it contains the breaking changes after removal, indicating whether a documented breaking change can be detected by test cases. 2) detected_bc_are_documented.csv, it contains the breaking changes sampled from all commits from 381 projects, indicating whether a detected breaking commit is documented by developers.   RQ2-3-4 folder: This folder contains the breaking changes used in analysis process in RQ2, RQ3 and RQ4. 1) used_projects.csv, the projects that contain breaking changes. 2) analyzed_breaking_changes.csv, the analyzed breaking changes in RQ2 to 4. The breaking changes are annotated. For example, column “category” indicates the type of the breaking change, column “change_signature_type” is related to RQ2, column “change_behavior_type” is related to RQ3 and column “reason” is related to RQ4.

Replication Package for "Identity Politics"

This replication package contains the codes and data needed to reproduce all Figures and Tables in the text and in the online appendix of the paper “Identity Politics”, by Nicola Gennaioli and Guido Tabellini

Replication package for "Gravity with History: On Incumbency Effects in International Trade"

This package contains the data, programs and instructions to replicate manuscript "Gravity with History: On Incumbency Effects in International Trade" by Egger, Foellmi, Schetter, and Torun forthcoming at JEEA.

Stack Overflow's Hidden Nuances: How Does Zip Code Define User Contribution? – Replication Package

Collective intelligence constitutes a foundational element within online community question-and-answering (CQA) platforms, such as Stack Overflow, being the source of most programming-related issues. Despite this relevance, concerns remain regarding issues surrounding user participation. Precedent research tends to focus on simple numerical measurements to analyse participation, which may sideline the inherent, subtler aspects. The proposed study aims to bridge this gap by operationalising 11 distinct metrics to represent user participation, behaviour, and community value across different regions of the USA. The study also conducts inductive content analysis to understand the impact of regional contextual factors on users' knowledge sharing patterns. This replication package is provided for those interested in further examining our research methodology.

A Cross-Continental Analysis of How Regional Cues Shape Developers' Stack Overflow Contributions – Replication Package

Stack Overflow provides a wide range of knowledge for the software development community. Despite the importance of these platforms, several studies have shown that digital information tends to cluster geographically, which limits knowledge access that is otherwise necessary for innovation. The proposed study highlights the dynamics of users from different geographical backgrounds within Stack Overflow, which entails intra-country interactions, predominant topics of discourse, as well as their communication patterns. Finally, the study highlights that regional behavioural variations stem beyond cultural factors, encompassing technological advancement, entrepreneurial ventures, and workforce composition.  This replication package is provided for those interested in further examining our research methodology.

Stack Overflow's Hidden Nuances: How Does Zip Code Define User Contribution? – Replication Package

Replication package for: Electoral Turnovers

This package contains replication materials for "Electoral Turnovers" by Benjamin Marx, Vincent Pons, and Vincent Rollet, to be published in the Review of Economic Studies.

Replication package for: State Building in a Diverse Society

Replication package: includes several datasets constructed from various primary and secondary sources, alongside code files that produce the results presented in the paper "State Building in a Diverse Society", by Ruth Carlitz, Ameet Morjaira, Joris Mueller, and Philip Osafo-Kwaako (2024). The replication package is structured into several folders that contain raw data, cleaned datasets, and programs in Stata, R, and Python. The README file also provides instructions on how to obtain access to various datasets.

This repository serves as a replication package for the research paper titled "Typestate-based Fault Localization of API Usage Violations in a Deep Learning Program"

<h2>Overview</h2> This repository serves as a replication package for the research paper titled "An Approach to Detecting Usage Protocol Violations in Deep Learning Programs." <h2>Benchmarks</h2> Two benchmarks were utilized in the research: <ol> <li> Benchmark1 from NeuraLint: <ul> <li>Location: <code>NLBench/SOSamples</code></li> </ul> </li> <li> Benchmark2 from Humbatova et al.: <ul> <li>Location: <code>HumbatovaBench/SOSamples</code></li> </ul> </li> </ol> <h2>Reproducing Results</h2> To replicate the results presented in the paper, follow these steps: <ol> <li>Download the NeuralStateAnalysis Zip file.</li> <li>Extract the file and navigate to the NeuralStateAnlaysis directory.</li> <li>(Optional) Install the necessary requirements by executing <code>pip install requirements.txt</code>. Note: The requirements.txt file is already available in this repository.</li> </ol> <h3>Running NeuralState on <code>NLBench</code>:</h3> <ol> <li>Navigate to the <code>NLBench</code><code>/SOSamples</code> directory.</li> <li>Open any of the programs you wish to execute.</li> <li>Set the path to the NeuralStateAnalysis directory: <code>Path-to-folder/NeuralStateAnalysis/</code></li> <li>Run the program using the command <code>python program_id</code>. Since the 'NeuralStateAnalysis(model).debug()' call is already present in all programs, you will be able to reproduce the results.</li> </ol> <h3>Running NeuralState on <code>HumbatovaBench</code>:</h3> <ol> <li>Navigate to the <code>HumbatovaBench</code><code>/SOSamples</code> directory.</li> <li>Open any of the programs you wish to execute.</li> <li>Set the path to the NeuralStateAnalysis directory: <code>Path-to-folder/NeuralStateAnalysis/</code></li> <li>Run the program using the command <code>python program_id</code>. Since the 'NeuralStateAnalysis(model).debug()' call is already present in all programs, you will be able to reproduce the results.</li> </ol>

[Replication Package] Enhancing Code Generation for Low-Resource Languages: No Silver Bullet

This repository contains scripts and results related to the work "Enhancing Code Generation for Low-Resource Languages: No Silver Bullet". Link to the GitHub repository: <a href="https://github.com/Devy99/low-resource-study">https://github.com/Devy99/low-resource-study</a>

opencc-by-4.0Sep 2024View details →

Replication package for: Fines and Financial Wellbeing

This package contains replication files for "Fines and Financial Wellbeing" by Steven Mello, to be published in the Review of Economic Studies. The results in this paper are based on analyses of public data on traffic citations in Florida merged with confidential credit bureau data. This package includes all public datasets as well as instructions and code for preparing the public datasets to be merged with the credit bureau data. The README file provides instructions for obtaining the credit bureau data, which are not included in this package due to confidentiality restrictions. This package also includes instructions and code for replicating all results using the merged public and credit bureau data.

Replication package for: "Mitigating Consequences of Prestige in Citations of Publications"

This package contains replication files for "Mitigating Consequences of Prestige in Citations of Publications". The package is composed of the data sets concerned with the citations of publication in the field of biomedicine. Furthermore, code written in the programming language R is additionally provided for the reproducibility of all results presented in the paper.  Instructions for the starting points for replication of all results are provided in the README file.

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>

Replication Package for "Classifying Open-Source Pre-Trained Models and Datasets for Software Engineering"

The replication package for the short paper titled 'Classifying Open-Source Pre-Trained Models and Datasets for Software Engineering' is provided. It includes a README file and accompanying scripts with comprehensive instructions to facilitate the replication of the analysis presented in the paper.

Replication Package of "Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?"

This repository contains the datasets and the scripts to replicate our work "Optimizing Datasets for Code Summarization: Is Code-Comment Coherence Enough?".

Replication Package for "A Call for Critically Rethinking and Reforming Data Analysis in Empirical Software Engineer"

Content Overview This replication package contains the following materials: - Data: RAW Focus Group Datasets and Aggregated Results For any issues, questions, or further assistance, please do not hesitate to contact the paper's authors. We are here to help!