Find research datasets worth reusing

Replication package for "Method Chaining Redux: An Empirical Study of Method Chaining in Java, Kotlin, and Python"

This dataset includes scripts and data files used to generate all analysis and results from the paper.  A README.md file is included for details on using the scripts. The dataset is quite large.  It is broken down into three archives.  All scripts are in replication-pkg.zip and the other 2 files only contain data.  So if you want to just inspect the analysis, you only need that single zip. If you grab the data-cached.zip file and extract it, it will need around 3GB of space.  This is the processed dataset stored in Parquet files.  Use this if you want to just recreate the tables/figures from the paper. If you want to make changes to the analyses, you will need the raw data in data-raw.zip.  This will need around 29GB of space once extracted.  If you then generate the CSV files from those TXT files (which you will need to do for any custom analysis), you will need an additional 22GB of space.

[Replication package] Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning

Anonymized replication package of submission #1917: "Explainable Human-Machine Teaming using Model Checking and Interpretable Machine Learning". See README.md for further instructions.

opencc-by-4.0Sep 2022View details →

Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions (Replication Package)

Online appendix for "Code Review of Build System Specifications: Prevalence, Purposes, Patterns, and Perceptions", in the Proceedings of the International Conference on Software Engineering (ICSE), 2023.

Reaction Data - Replication Package

Reaction Data - Replication Package: Contains four .py files to collect for each of the six studied projects : <ul> <li>The reactions on the pull requests</li> <li>The reactions on the pull requests' comments</li> <li>The reactions on the comments of the pull requests' reviews</li> <li>The reactions on the comments of the pull requests' commits Additionally, this package contains all the data used in the quantitative and qualitative analyses.</li> </ul>

Qualitative Analyses - Replication Package

Qualitative Analyses - Replication Package: Contains 4 csv files for: <ul> <li>The manual classification of pull requests in RQ1</li> <li>The qualitative analysis of negative reactions in RQ1</li> <li>The qualitative analysis of positive reactions in RQ1</li> <li>The qualitative analysis on the five categories of reactors in RQ2</li> </ul>

Pull Request Classification - Replication Package

Pull Request Classification - Replication Package: Contains two .java files (Heuristics.java and a helper class, namely Formatter.java) for the classification of pull request into the eight categories (i.e., bug, feature, test, resources, refactoring, merge, deprecate and others). Additionally, the results of the classification are presented in .csv files.

opencc-by-4.0Mar 2024View details →

Replication package for the paper: "Towards Effective Gamification of Existing Systems: Method and Experience Report"

This is the replication package for the paper: "Towards Effective Gamification of Existing Systems: Method and Experience Report", submitted to Software Quality Journal. It contains all the artifacts used to conduct our study.

Replication package for "Bilateral Trade Imbalances"

Replication package for Cuñat, A., and R. Zymek. "Bilateral Trade Imbalances", Review of Economic Studies (forthcoming). The package contains raw data and STATA .do-files that permit the replication of all steps in the production of the key outputs in the accepted version of the paper, as well as those outputs.

opencc-by-4.0Mar 2023View details →

Replication package for: "The Intergenerational Transmission of World War I on Female Labour"

Replication package for: Gay, Victor. 2023. "The Intergenerational Transmission of World War I on Female Labor." The Economic Journal.

opencc-by-4.0Mar 2023View details →

# Blocks? Graphs? Why Not Both? Designing and Evaluating a Hybrid Programming Environment for End-users: Replication Package

Blocks? Graphs? Why Not Both? Designing and Evaluating a Hybrid Programming Environment for End-users: Replication Package This repository contains supplementary materials for the paper "Blocks? Graphs? Why Not Both? Designing and Evaluating a Hybrid Programming Environment for End-users". We provide this data for transparency reasons and to support replications of our experiemnts. Note: This package is anonymized for peer review purposes. We will provide contact information for the authors at a later date. We also plan to add interactive versions of our tasks and tutorials in an updated version to allow readers easier exploration/experimentation. Summary of files contained in this package This package contains two parts: <ul> <li> The <code>data-analysis/</code> folder contains the raw dataset we collected for our experiment in CSV format, as well as scripts we used for our analyses. <ul> <li>Column <code>ID</code> contains a unique 4-digit identifier for each participant that they were assigned throughout our study.</li> <li>Column <code>Group</code> contains the group (Blocks/Graph) that participants were randomly assigned to.</li> <li>Columns <code>Task1Time</code> and <code>Task2Time</code> contain the time participants spent to complete the two programming tasks of our study in minutes.</li> <li>Columns <code>Task1Success</code> and <code>Task2Success</code> contain a boolean value indicating whether the participants successfully completed the given task. Note that participants had unlimited attempts until they timed out after a strict time limit of 30 minutes, so if a participant was unsuccessful the corresponding time value is 30.</li> <li>Columns <code>Task1Tests</code> and <code>Task2Tests</code> contain the number of times a participant executed their code throughout a task, including their final submission if they were successful.</li> <li>Columns <code>LearnTask</code>, <code>ReadTask</code> and <code>WriteTask</code> contain the scores that participants gave to the task editor component of their assigned programming environment. There are 3 scores for the categories "learnability", "readability" and "writability". Scores are on a 5-point scale from 1 (worst) to 5 (best).</li> <li>Columns <code>LearnTrig</code>, <code>ReadTrig</code> and <code>WriteTrig</code> contain the scores that participants gave to the trigger editor component of their assigned programming environment. There are 3 scores for the categories "learnability", "readability" and "writability". Scores are on a 5-point scale from 1 (worst) to 5 (best).</li> <li>Columns <code>LearnComp</code>, <code>ReadComp</code> and <code>WriteComp</code> contain the scores that participants gave to their assigned assigned programming environment in direct comparison to the other alternative. There are 3 scores for the categories "learnability", "readability" and "writability". Unlike in the paper, where scores are on a scale from -2 to 2, the raw scores here are on a 5-point scale from 1 (strong preference for other environment) to 5 (strong preference for own environment).</li> <li>The script <code>survival.py</code> was used to perform the survival analysis presented in the paper and generate the related figure.</li> <li>The script <code>batplot.py</code> was used to generate the 3x3 grid of ratings used in a figure in the paper.</li> </ul> </li> <li> The <code>materials/</code> folder contains the tutorials and task descriptions we presented to study participants. It also contains the exact wording of pre-screening and post-experiemental survey questions. <ul> <li>The image <code>pre-screening.png</code> shows the three pre-screening questions we used to determine whether our participants could be included in our study.</li> <li>The images <code>tutorial1_instructions.png</code> and <code>tutorial1_sim.png</code> contain the instructions and initial simulator state we provided to participants for the first programming tutorial. This tutorial did not provide starter code and was identical for both participant groups.</li> <li>The images <code>tutorial2_instructions.png</code> and <code>tutorial2_sim.png</code> contain the instructions and initial simulator state we provided to participants for the second programming tutorial. This tutorial was identical for both participant groups and provided participants with starter code, which is shown in the images: <ul> <li><code>tutorial2_code_main.png</code> for the main program in the left canvas</li> <li><code>tutorial2_code_move.png</code> for the definition of "Move box to the right".</li> </ul> </li> <li>The images <code>tutorial3_instructions_blocks.png</code>/<code>tutorial3_instructions_graph.png</code> and <code>tutorial3_sim.png</code> contain the instructions and initial simulator state we provided to participants for the third programming tutorial. This tutorial also provided participants with starter code, which is shown in the images: <ul> <li><code>tutorial3_code_main.png</code> for the main program in the left canvas</li> <li><code>tutorial3_code_pick.png</code> for the definition of "Pick up box"</li> <li><code>tutorial3_code_place.png</code> for the definition of "Place box"</li> </ul> </li> <li>The images <code>task1_instructions.png</code> and <code>task1_sim.png</code> contain the instructions and initial simulator state we provided to participants for the first programming task. The task did not provide starter code and the instructions were identical for both participant groups.</li> <li>The images <code>task2_instructions.png</code> and <code>task2_sim.png</code> contain the instructions and initial simulator state we provided to participants for the second programming task. The instructions were identical for both groups. This task also provided participants with starter code, which is shown in the images: <ul> <li><code>task2_code_main.png</code> for the main program in the left canvas</li> <li><code>task2_code_pick_prog.png</code> for the definition of "Pick up block"</li> <li><code>task2_code_load_trig_blocks.png</code>/<code>task2_code_load_trig_graph.png</code> for the definition of the trigger "Ready to load machine"</li> <li><code>task2_code_load_prog.png</code> for the definition of "Load and activate machine"</li> <li><code>task2_code_finished_trig_blocks.png</code>/<code>task2_code_finished_trig_graph.png</code> for the definition of the trigger "Machine finished"</li> <li><code>task2_code_finished_prog1.png</code> for the definition of "Get block from machine"</li> <li><code>task2_code_finished_prog2.png</code> for the definition of "Place block in bin"</li> </ul> </li> <li>The image <code>usability.png</code> shows the usability questions we used to determine a participant's rating of their assigned programming environment. The questions were identical for both participant groups.</li> <li>The images <code>comprehension_blocks_1.png</code> and <code>comprehension_blocks_2.png</code> show the program comprehension questions we used to determine whether participants in the Blocks group could understand more complex triggers.</li> <li>The images <code>comprehension_graph_1.png</code> and <code>comprehension_graph_2.png</code> show the program comprehension questions we used to determine whether participants in the Graph group could understand more complex triggers.</li> <li>The images <code>comparison_blocks.png</code> and <code>comparison_graph.png</code> show the images of triggers in the alternative environment that we showed to our participants before choosing their preferred environment. The questions were identical for both participant groups.</li> <li>The image <code>comparison.png</code> shows the questions we used to determine a participant's preference between the two programming environment alternatives.</li> </ul> </li> </ul>

opencc-by-4.0Apr 2023View details →

Replication Package for Identifying Self-Admitted Technical Debt in Issue Tracking Systems using Machine Learning

This dataset includes pre-trained word embeddings and a weighted file that can be used to identify self-admitted technical debt (SATD) from issue tracking systems.

Replication package for: The Slaughter of the Bison and Reversal of Fortunes on the Great Plains

These files include the code and data to replicate the results for "The slaughter of the bison and reversal of fortunes on the Great Plains" by Donn. L. Feir, Rob Gillezeau, and Maggie EC Jones forthcoming in The Review of Economic Studies.

opencc-by-4.0Dec 2022View details →

Replication package for: DEMAND AND WELFARE ANALYSIS IN DISCRETE CHOICE MODELS WITH SOCIAL INTERACTIONS

This replication package includes the dataset and stata code needed to produce the empirical results in the paper.

Replication package for: "WHEN LESS IS MORE: EXPERIMENTAL EVIDENCE ON INFORMATION DELIVERY DURING INDIA'S DEMONETIZATION"

This package contains all data, codes, documentation required to reproduce figures and tables from Banerjee, Breza, Chandrasekhar, & Golub (forthcoming). "When Less is More: Experimental Evidence on Information Delivery During India's Demonetization" Review of Economic Studies.

Replication package for: Resolving Failed Banks: Uncertainty, Multiple Bidding & Auction Design

"Resolving Failed Banks: Uncertainty, Multiple Bidding, and Auction Design," Jason Allen, Robert Clark, Brent Hickman and Eric Richert, forthcoming, Review of Economic Studies.   DATA AND CODE NEEDED TO REPLICATE ANALYSIS

Replication Package: A Systematic Mapping Study on Security in Configurable Safety-critical Systems Based on Product-Line Concepts

Welcome to the public repository for the additional content of the paper "A Systematic Mapping Study on Security in Configurable Safety-critical Systems Based on Product-Line Concepts", accepted at the ICSOFT 2023. This repository provides additional information to the conducted mapping study, including the following files: <ul> <li>fetched_results_ICSOFT2023.csv: sheet containing all papers fetched from IEEEXplore, Scopus, and the ACM Guide to Computing Literature.</li> <li>excluded_paper.csv: sheet containing all excluded papers related to safety-critical systems but not referring to security.</li> <li>analysis_sheet_ICSOFT2023.csv: sheet containing information regarding the analysis results of 44 included papers based on the extraction criteria.</li> </ul>

opencc-by-4.0Jun 2023View details →

Replication Package for the paper "AI-based Fault-proneness Metrics for Source Code Changes"

This is the replication package for the paper "AI-based Fault-proneness Metrics for Source Code Changes", submitted at the IWSM-Mensura '23 conference. The archive is a Docker image file with a fully setup and working environment to re-execute the experiments involved in the manuscript. We pre-loaded all libraries and codeBERT models to ease the replication process and avoid compatibility issues, as the environment cannot be easily managed using Dockerfiles. To run the image, a Docker installation is needed. Once downloaded, from the command line type: <pre><code>docker load -i </path/to/downloaded/ai-proneness-replication.tar></code></pre> After the loading process, you can run the container by typing: <pre><code>docker run -it mensura/ai-proneness-replication:1.0</code></pre> All the source code and the dataset to re-execute the experiment is located into the /Replication folder. The folder contains the results of our experimentation in CSV and MS Excel format, along with the following subdirectories: <ul> <li>dataset: a replication of the used dataset. The file dataset.csv gives information on all the entries, while the code folder contains a subdirectory for each sample, named by its id. In the folder, the file old.txt and new.txt refers to the older and newer version of the method, respectively; gitdiff.txt stores the raw git-diff command output, while diff.html stores a more human-readable version of the differences.</li> <li>ai-fault-proneness-tk-replication: the Java code used to apply Tree Kernel techniques on the dataset (we used JDK-11, embedded within the container). To build and execute the package, refer to the file README.md in the folder. For convenience, we also provided an executable JAR file ai-fault-proneness-tk-replication-1.0-jar-with-dependencies.jar that can be run directly and saves the output in a CSV file in the results folder of the replication package.</li> <li>code-embeddings-and-analysis: python scripts to execute the codeBERT-based approaches and to extract the diff statistics. To execute all the steps, a convenience shell script execute.sh has been pre-loaded and can be executed to automatize all the process.</li> </ul>

opencc-by-4.0Jun 2023View details →

Towards a Catalog of Refactorings for Elixir [Replication Package]

Replication Package of the paper accepted into ICSME NIER 2023

opencc-by-4.0Oct 2023View details →

Replication package of "How Do Deep Learning Faults Affect AI-Enabled Cyber-Physical Systems in Operation? A Preliminary Study Based on DeepCrime Mutation Operators"

Cyber-Physical Systems (CPSs) combine digital cyber technologies with physical processes. As in any other software system, in the case of CPSs, the use of Artificial Intelligence (AI) techniques in general, and Deep Neural Networks (DNNs) in particular, is contantly increasing. While recent studies have considerably advanced the field of testing AI-enabled systems, it has not yet been investigated how different Deep Learning (DL) bugs affect AI-enabled CPSs in operation. This work-in-progress paper presents a preliminary evaluation on how such bugs can affect CPSs in operation by using a mobile robot as a case study system. For that, we generated DL mutants by using operators proposed by Humbatova et al., which are operators based on real-world DL faults. Our preliminary investigation suggests that such bugs are more difficult to detect when they are deployed in operation rather than when testing their DNN in an off-line setup, which contrast with related studies.   This repository provides the replication data employed in our study.