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184 results for “Replication Study”
Study replicability dataset 1
<p>This dataset contains all data collected to conduct the studies on Chapter 2 and 5 of Ph.D. thesis "Augmented fine-grained defect prediction for code review"</p> <p>Bug prediction is aimed at identifying software artifacts that are more likely to be defective in the future. Most approaches defined so far target the prediction of bugs at class/file level. Nevertheless, past research has provided evidence that this granularity is too coarse-grained for its use in practice. As a consequence, researchers have started proposing defect prediction models targeting a finer granularity (particularly method-level granularity), providing promising evidence that it is possible to operate at this level. Particularly, models mixing product and process metrics provided the best results. We present a study in which we first replicate previous research on method-level bug-prediction, by using different systems and timespans. Afterwards, based on the limitations of existing research, we (1) re-evaluate method-level bug prediction models more realistically and (2) analyze whether alternative features based on textual aspects, code smells, and developer-related factors can be exploited to improve method-level bug prediction abilities. Key results of our study include that (1) the performance of the previously proposed models, tested using the same strategy but on different systems/timespans, is confirmed; but, (2) when evaluated with a more practical strategy, all the models show a dramatic drop in performance, with results close to that of a random classifier. Finally, we find that (3) the contribution of alternative features within such models is limited and unable to improve the prediction capabilities significantly. As a consequence, our replication and negative results indicate that method-level bug prediction is still an open challenge.</p>
Study replicability dataset 2
<p>This dataset contains all data collected to conduct the studies in Chapter 3 "Fine-Grained Just-In-Time Defect Prediction" of the Ph.D. thesis "Augmented fine-grained defect prediction for code review".</p>
Replication Package: A Study on the Accuracy of OCR Engines for Source Code Transcription from Programming Screencasts
<p>The replication package of the paper "A Study on the Accuracy of OCR Engines for Source Code Transcription from Programming Screencasts" including the dataset, results and tools</p>
Using White-Box Test Generation During Development: A Replicated Study
<p>This package contains the artifacts for our external, differentiated replication of an empirical study on using white-box test generation.</p> <p>The original experiment is described in the following paper.</p> <blockquote> <p>José Miguel Rojas, Gordon Fraser, and Andrea Arcuri. 2015. Automated unit test generation during software development: a controlled experiment and think-aloud observations. In Proceedings of the 2015 International Symposium on Software Testing and Analysis (ISSTA 2015), 338–349. DOI: 10.1145/2771783.2771801</p> </blockquote> <p>30 developers and students participated in the experiment in January 2016.</p> <p>The main difference in the replication was using the IntelliTest test generator tool instead of EvoSuite.</p> <p>The contents of the package are the followings.</p> <ul> <li>/assignments <ul> <li>session_starts_endings.csv - Timestamps when each session was started</li> <li>assignments_with_projects.csv - Assigments of participants to projects, classes and sessions</li> </ul> </li> <li>/projects <ul> <li>/golden - Contains the golden implementation and test suites of all 4 classes</li> <li>/user - Contains the user implementation and test method stubs of all 4 classs</li> </ul> </li> <li>/results <ul> <li>time_merged.csv - File of timing analysis containing each test run of subjects</li> <li>results.csv - Contains the coverage and other required data of each participant tasks</li> <li>exit.csv - Answers of the exit survey</li> <li>background.csv - Answers of the background questionnaire</li> </ul> </li> <li>/scripts <ul> <li>/ps <ul> <li>Get-Results.ps1 - Gets the results of both tasks for one specified participant (invokes MSBuild, MSTest, VisualMutator, OpenCover)</li> <li>Get-Failures-Errors.ps1 - Gets failures and errors of both tasks for one specified participant (invokes MSBuild, MSTest)</li> <li>Get-TimeData.ps1 - Iterates over all test runs for each participant</li> </ul> </li> <li>/r <ul> <li>likert.R - Generates the Likert chart</li> <li>si_st.R - Generates table of subject implementation executed with subject test suites</li> <li>gi_st.R - Generates table of results where subject test suites was run on golden implementation</li> <li>time_spent_tests.R - Generates a table of time and activity of testing</li> <li>errors_failures.R - Generates table of errors and failures obtained using the golden test suite</li> <li>correlation_analysis.R - Generates table containing correlation between lack of correctness and IntelliTest usage</li> </ul> </li> </ul> </li> </ul>
Replicability and Reproducibility of a Schema Evolution Study in Embedded Databases
<p>Archives containing datasets, scripts, and instructions for reproducing a study.</p>
Replication package for "An Industrial Study on the Challenges and Effects of Diversity-based Testing in Continuous Integration"
<p>This is the replication package for the analysis done in the paper "An Industrial Study on the Challenges and Effects of Diversity-based Testing in Continuous Integration".</p> <p>The package includes: (i) CSV files with data on test case and corresponding feature coverage, as well as test execution data; (ii) CSV files including failure coverage for the executed techniques; (iii) R scripts to re-run our visual and statistical analysis when comparing techniques results; and (iv) An R Markdown file (rendered into HTML) detailing the steps of our analysis with the coresponding code.</p>
Replication Package for the Paper: "How Do Users Revise Architectural Related Questions on Stack Overflow: An Empirical Study"
<p>This is the replication package for the paper: "How Do Users Revise Architectural Related Questions on Stack Overflow: An Empirical Study". In the following, we provide a brief description of the folders and files:</p> <p><strong>(1) raw data</strong></p> <p>The raw data folder contains the retrieved 36,417 posts and the SQL query used for retrieving ARPs from Stack Overflow through the query interface provided by Stack Exchange.</p> <p><strong>(2) filtered ARPs</strong></p> <p>The filtered ARPs folder contains 13,205 filtered candidates ARPs from the retrieved 36,417 posts and the results of data analysis for the first RQ (i.e., RQ1).</p> <p><strong>(3) randomly selected posts and labeling results</strong></p> <p>The randomly selected posts and labeling results folder contains 1,068 randomly selected posts and their labeling results (i.e., 21 ARPs, wherein 14.3%, 3 out of 21 ARPs, do not contain “architect*” terms and 85.7%, 18 out of 21 APRs, contain “architect*” terms).</p> <p><strong>(4) relevant ARPs for answering RQs</strong></p> <p>The relevant ARPs for answering RQs folder contains 4,114 ARPs with revision information for answering the last three RQs (i.e., RQ2, RQ3, and RQ4).</p> <p><strong>(5) interview responses</strong></p> <p>The interview responses folder contains 11 collected interview responses from software practitioners. These responses were gathered to evaluate the identified categories related to ARQ revisions.</p> <p><strong>(6) data extraction and analysis</strong></p> <p>The data extraction and analysis folder contains the MAXQDA file. Data Labeling & Encoding for RQs.mx20 is the results of data labeling and encoding for RQ2, RQ3, and RQ4, which were analyzed by the MAXQDA tool. This file can be opened by MAXQDA 2020 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14 days trial version of MAXQDA 2020, which is available at https://www.maxqda.com/trial for download.</p>
Replication package for: "Economic Distress and Children's Mental Health: Evidence from the Brazilian High Risk Cohort Study for Mental Conditions"
<p>This package replicates the results of "Economic distress and children's mental health: evidence from the Brazilian High Risk Cohort Study for Mental Conditions" (and its respective online appendix) using mostly Stata. The package does not include the paper's main dataset, which is confidential.</p>
Replication Package for ML-EUP Conversational Agent Study
<p>This is the replication package of the paper <a href="https://conf.researchr.org/details/icse-2024/icse-2024-research-track/5/How-to-Support-ML-End-User-Programmers-through-a-Conversational-Agent">How to Support ML End-User Programmers through a Conversational Agent</a>, published at ICSE 2024.</p> <p><strong>Replication Package Files</strong></p> <ul> <li><strong>Readme.pdf:</strong> document that describes the replication package and indicates how to use it. </li> <li> <p><strong>1. Forms.zip: </strong>contains the forms used to collect data for the experiment.</p> </li> <li> <p><strong>2. Experiments.zip: </strong>contains the participants’ and sandboxers’ experimental task workflow with Newton.</p> </li> <li> <p><strong>3. Responses.zip: </strong>contains the responses collected from participants during the experiments.</p> </li> <li> <p><strong>4. Analysis.zip:</strong> contains the data analysis scripts and results of the experiments.</p> </li> <li> <p><strong>5. newton.zip: </strong>contains the tool we used for the WoZ experiment.</p> </li> <li> <p><strong>Interactions.pdf: </strong>explains Figure 4 of the paper in detail by depicting the interactions of P4.</p> </li> <li> <p><strong>TutorialStudy.pdf:</strong> script used in the experiment with and without Newton to be consistent with all participants.</p> </li> <li> <p><strong>Woz_Script.pdf:</strong> script wizard used to maintain consistent Newton responses among the participants.</p> </li> <li><strong>Dockerfile</strong>: docker definition of newton-docker.tar.gz.</li> <li> <p><strong>newton-docker.tar.gz:</strong> docker image that contains both the tool and the analysis files.</p> </li> <li><strong>LICENSE: </strong>license file describing the license of data and code files.</li> </ul> <p> </p> <p><strong>1. Forms.zip</strong></p> <p>The forms zip contains the following files:</p> <ul> <li> <p><strong>Demographics.pdf: </strong>a PDF form used to collect demographic information from participants before the experiments</p> </li> <li> <p><strong>Post-Task Control (without the tool).pdf:</strong> a PDF form used to collect data from participants about challenges and interactions when performing the task without Newton </p> </li> <li> <p><strong>Post-Task Newton (with the tool).pdf:</strong> a PDF form used to collect data from participants after the task with Newton.</p> </li> <li> <p><strong>Post-Study Questionnaire.pdf:</strong> a PDF form used to collect data from the participant after the experiment.</p> </li> </ul> <p> </p> <p><strong>2. Experiments.zip</strong></p> <p>The experiments zip contains two types of folders:</p> <ul> <li> <p><strong>exp[participant’s number]-c[number of dataset used for control task]e[number of dataset used for experimental task]</strong>. Example: exp1-c2e1 (experiment participant 1 - control used dataset 2, experimental used dataset 1)</p> </li> <li> <p><strong>sandboxing[sandboxer’s number].</strong> Example: sandboxing1 (experiment with sandboxer 1)</p> </li> </ul> <p> </p> <p>Every experiment subfolder contains:</p> <ul> <li> <p><strong>warmup.json: </strong>a JSON file with the results of Newton-Participant interactions in the chat for the warmup task.</p> </li> <li> <p><strong>warmup.ipynb: </strong>a Jupyter notebook file with the participant’s results from the code provided by Newton in the warmup task.</p> </li> <li> <p><strong>sample1.csv: </strong>Death Event dataset.</p> </li> <li> <p><strong>sample2.csv: </strong>Heart Disease dataset.</p> </li> <li> <p><strong>tool.ipynb: </strong>a Jupyter notebook file with the participant’s results from the code provided by Newton in the experimental task.</p> </li> <li> <p><strong>python.ipynb: </strong>a Jupyter notebook file with the participant’s results from the code they tried during the control task.</p> </li> <li> <p><strong>results.json:</strong> a JSON file with the results of Newton-Participant interactions in the chat for the task with Newton.</p> </li> </ul> <p> </p> <p>To load an experiment chat log into Newton, add the following code to the notebook:</p> <pre><code>import anachat import json with open("result.json", "r") as f: anachat.comm.COMM.history = json.load(f) </code></pre> <p>Then, click on the notebook name inside Newton chat</p> <p>Note 1: the subfolder for P6 is exp6-e2c1-serverdied because the experiment server died before we were able to save the logs. We reconstructed them using the notebook newton_remake.ipynb based on the video recording.</p> <p>Note 2: The sandboxing occurred during the development of Newton. We did not collect all the files, and the format of JSON files is different than the one supported by the attached version of Newton.</p> <p> </p> <p><strong>3. Responses.zip</strong></p> <p>The responses zip contains the following files:</p> <ul> <li> <p><strong>demographics.csv: </strong>a CSV file containing the responses collected from participants using the demographics form</p> </li> <li> <p><strong>task_newton.csv: </strong>a CSV file containing the responses collected from participants using the post-task newton form.</p> </li> <li> <p><strong>task_control.csv:</strong> a CSV file containing the responses collected from participants using the post-task control form.</p> </li> <li> <p><strong>post_study.csv:</strong> a CSV file containing the responses collected from participants using the post-study control form.</p> </li> </ul> <p> </p> <p><strong>4. Analysis.zip</strong></p> <p>The analysis zip contains the following files:</p> <ul> <li> <p><strong>1.Challenge.ipynb:</strong> a Jupyter notebook file that performs the statistical tests and creates the perceptions of challenges figure.</p> </li> <li> <p><strong>2.Interactions.py: </strong>a Python file that creates the participants’ JSON files.</p> </li> <li> <p><strong>3.Interactions.Graph.ipynb: </strong>a Jupyter notebook file that creates the participant’s interaction figure.</p> </li> <li> <p><strong>4.Interactions.Count.ipynb:</strong> a Jupyter notebook file that counts participants’ interaction with each figure.</p> </li> <li> <p><strong>config_interactions.py:</strong> this file contains the definitions of interaction colors and grouping</p> </li> <li> <p><strong>interactions.json: </strong>a JSON file with the interactions during the Newton task of each participant based on the categorization.</p> </li> <li> <p><strong>requirements.txt: </strong>dependencies required to run the code to generate the graphs and json analysis.</p> </li> </ul> <p> </p> <p>To run the analyses, please follow the steps:</p> <p>1- Extract Analysis.zip and cd into the directory</p> <p>2- Install Python 3.10, and then the analysis dependencies with the following command:</p> <pre><code>pip install -r requirements.txt</code></pre> <p>3- Run Jupyter Notebook/Lab and execute all cells of <strong>1.Challenge.ipynb</strong>. It will generate the challenges figure.</p> <p>4- Run <strong>2.Interactions.py</strong> using the following command:</p> <pre><code>python 2.Interactions.py</code></pre> <p>This file was created manually by individually categorizing each interaction of the participants. The execution will generate the file interactions.json with the graph definitions of the interactions.</p> <p>5- Run Jupyter Notebook/Lab and execute all cells of <strong>3.Interactions.Graph.ipynb</strong>. It will create the interactions graph visualization.</p> <p>6- Run Jupyter Notebook/Lab and execute all cells of <strong>4.Interactions.Count.ipynb</strong>. It will create the interactions table.</p> <p> </p> <p><strong>5. newton.zip</strong></p> <p>The newton zip contains the <strong>source code of the Jupyter Lab extension</strong> we used in the experiments. Read the <strong>README.md</strong> file inside it for instructions on how to install and run it.</p> <p> </p> <p><strong>6. newton-docker.tar.gz</strong></p> <p>This file contains the Docker image with the replication package in a configured environment for both the tool and the dat analyses.</p> <p>To import the image, run:</p> <pre><code><span>docker load <</span> <span>newton-docker.tar.gz</span></code></pre> <p>Then, start the container by running:</p> <pre><code><span>docker run -p 8888:8888 -it newton</span></code></pre> <p>Finally, start Jupyter Lab:</p> <pre><code><span>jupyter lab --collaborative --ip="*" --port=8888 --allow-root</span></code></pre> <p> </p>
Replication Package of the Paper: "Using Large Language Models for Commit Message Generation: A Preliminary Study"
<p>This replication package contains the evaluation data and script files used in the paper "Using Large Language Models for Commit Message Generation: A Preliminary Study". We provide below a brief description of each folder:</p><ul><li><strong>experiment_data/human_evaluation</strong>: Human evaluation results from two participants.</li><li><strong>experiment_data/msg</strong>: Generated commit messages of each method (baselines & LLMs) and human-written commit messages.</li><li><strong>script</strong>: Evaluation metric script, parallel inference script (for using OpenAI API).</li></ul>
Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle's 2015 study: "The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception"
<p>Data for the study "Alpha-band oscillations and visual temporal resolution: An expansion and partial replication of Samaha & Postle’s 2015 study: “The Speed of Alpha-Band Oscillations Predicts the Temporal Resolution of Visual Perception”"</p> <p>Contents consist of curated EEG data for statistical analysis, statistical analysis workflow and code and Matlab code for the behavioural flash fusion task.</p>
Replication Package of the Paper "How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow"
<p>This replication package contains datasets and scripts related to the paper: "<em>How do Papers Make into Machine Learning Frameworks: A Preliminary Study on TensorFlow</em>"</p> <ul> <li> <p><code>Contributor_Classification.csv</code>: contains the assignment of each contributor to a specific classification. The file contains the following columns:</p> <ul> <li><em>Date</em> : contains the date of each comment</li> <li><em>Type</em>: describe the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC)</li> <li><em>ID</em> : specific ID of the comment</li> <li><em>Body</em> : contains the body of the comment analyzed</li> <li><em>Url</em> : link at each comments</li> <li><em>NumberPR</em> : number of a PR</li> <li><em>Name_Contributor</em>: contains the name of a contributor for each comment</li> <li><em>Contributor_classification</em>: contains the assignment of a specific classification (academic, bot, ML expert, software engineer, unknown), obtained after manual analysis, for each contributor of a comment</li> </ul> </li> <li> <p><code>Contributors_ManualAnalysis.csv</code>: contains the manual analysis performed by two authors to assign a classification for each contributor. The file contains the following columns:</p> <ul> <li><em>Contributors</em>: contains the name of the contributor for each comment</li> <li><em>Link GitHub</em>: contains the link to the GitHub page for each contributor</li> <li><em># PR</em>: contains the number of PRs in which a specific contributor is involved</li> <li><em>Annotator1</em>: manual classification of the first annotator</li> <li><em>Annotator2</em>: manual classification of the second annotator</li> <li><em>Final Classification</em>: contains the final label (academic, bot, ML expert, software engineer, unknown)assigned for each contributor</li> <li><em>Organization</em>: contains the organization, if any (Google, Hugging Face, Microsoft, OpenAI)</li> </ul> </li> <li> <p><code>ManualAnalysis.csv</code>: contains the manual analysis performed regarding Comment Type, Nature of Comment and Artifact. The .csv contains the following columns:</p> <ul> <li><em>URL</em>: contains the link to each comment</li> <li><em>NumberPR</em> : number of pull request</li> <li><em>CommentType1</em>: contains the classification of the first annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other)</li> <li><em>CommentType2</em>: contains the classification of the second annotator in merit of comment type (Conventional review, Initial implementation, Management, ML review, Other)</li> <li><em>NatureComment1</em>: contains the classification of the first annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment)</li> <li><em>NatureComment2</em>: contains the classification of the second annotator about the nature of the comment (Approval, Bug fix, Buid error, Clarification, Code, Code convention and spacing, Code review, Comment, Enhancement request, Explanation, Feedback, Introducing alternative implementation, Pinging, Plan for merging into TF, Question, References and referrals, Request a review, Request documentation improvement, Request test, Request verification, Review, Review assignment)</li> <li><em>Artifact1</em>: contains the classification of the first annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other)</li> <li><em>Artifact2</em>: contains the classification of the second annotator with respect to the artifact (Article, Code, Commit, Issue/bug, Link, Review, Other)</li> <li><em>FINALCommentType</em>: contains the final classification of the comment type after the resolution of the conflicts</li> <li><em>FINALNatureComment</em>: contains the final classification of the nature of the comment after resolution of conflicts</li> <li><em>FINALArtifact</em>: contains the final classification of the artifact after the resolution of conflicts</li> </ul> </li> <li> <p><code>Summary_PR.csv</code>: contains the details about the composition of each PRs. The file contains the following columns:</p> <ul> <li><em>#PullRequest</em>: contains the number of all pull requests analyzed</li> <li><em>#events</em>: contains the number of all the events analyzed for each PR</li> <li><em>#comments</em>: contains the number of all comments for each PR</li> <li><em>#Commit</em>:contains the number of Commit for each PR</li> <li><em>PC</em>: contains the number of PC for each PR</li> <li><em>RC</em>: contains the number of RC for each PR</li> </ul> </li> <li> <p><code>Total_PR_Comments.csv</code>: contains information about all comments analyzed. The columns are:</p> <ul> <li><em>Date</em>: contains the date of each comment</li> <li><em>Type</em>: describes the type of a pull request (if it is Close, Commit, DESCR, Merge, PC, RC)</li> <li><em>ID</em>: SHAn of the comment</li> <li><em>NumberPR</em>: PR number</li> <li><em>Name_Contributor</em>: contains the name of a contributor for each comment</li> <li><em>Body</em>: contains the body of the comment analyzed</li> <li><em>Url</em>: link at each comment</li> </ul> </li> </ul> <p>The replication also contains a directory <code>results</code> in which there are quantitative results. The directory contains:</p> <ul> <li> <p><code>Artifacts.csv</code>: This file contains the results for the artifact. The columns are:</p> <ul> <li><em>#PullRequest</em>: number of the pull request analyzed</li> <li><em>Article</em>: contains the percentage of the occurrences of the article in the specific pull request</li> <li><em>Code</em>: contains the percentage of the occurrences of the code in the specific pull request</li> <li><em>Commit</em>: contains the percentage of the occurrences of the commit in the specific pull request</li> <li><em>Issue reference</em>: contains the percentage of the occurrences of the issue reference in the specific pull request</li> <li><em>External link</em>: contains the percentage of the occurrences of the external link in the specific pull request</li> <li><em>Review</em>: contains the percentage of the occurrences of the review in the specific pull request</li> <li><em>Other</em>: contains the percentage of the occurrences of the other in the specific pull request</li> </ul> <p>ßAlso, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (Article, Code, Commit, Issue reference, External link, Review, Other)</p> </li> <li> <p><code>CommentType.csv</code>: this file contains the results for the comment type. The columns are:</p> <ul> <li><em>#PullRequest</em>:number of the pull request analyzed</li> <li><em>Conventional review</em>: contains the percentage of the occurrences of the conventional review in the specific pull request</li> <li><em>Initial implementation</em>: contains the percentage of the occurrences of the initial implementation in the specific pull request</li> <li><em>Management</em>: contains the percentage of the occurrences of the management in the specific pull request</li> <li><em>ML review</em>: contains the percentage of the occurrences of the ML review in the specific pull request</li> <li><em>Other</em>: contains the percentage of the occurrences of the Other in the specific pull request</li> </ul> <p>Also, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (Conventional review, Initial implementation, Management, ML review, Other)</p> </li> <li> <p><code>Contributor.csv</code>: this file contains the results for the contributors. The columns are:</p> <ul> <li><em>#PullRequest</em>: number of the pull requests analyzed</li> <li><em>academic</em>: contains the percentage of the occurrences of the academic in the specific pull request</li> <li><em>bot</em>: contains the percentage of the occurrences of the bot in the specific pull request</li> <li><em>ML expert</em>: contains the percentage of the occurrences of the ML expert in the specific pull request</li> <li><em>software engineer</em>: contains the percentage of the occurrences of the software engineer in the specific pull request</li> <li><em>unknown</em>: contains the percentage of the occurrences of unknown in the specific pull request</li> </ul> <p>Also, the <code>Mean_Value</code> row contains the mean value of all occurrences for each column (academic, bot, ML expert, software engineer, unknown)</p> </li> </ul>
Replication package of the Paper "On the Relationships between the Initial Ecology Indicators of OSS Projects and Their Long-Term Popularity: An Exploratory Study on GitHub"
<p>The dataset is collected from GitHub API and GitHub GHTorrent dataset. A brief description of each folder is provided below:</p> <p><strong>1. "Dataset_and_Code" folder</strong></p> <p>Contains the final dataset and algorithms</p> <p><strong>2. "Test_parameters" folders </strong></p> <p>Includes datasets under different parameters and the corresponding reproduction code, which corresponds to the first experiment of RQ1</p> <p><strong>3. "Compare_baseline"folder</strong></p> <p>Includes the dataset used by our method, the dataset used by the baseline method, and the reproduction code, corresponding to the second experiment of RQ1</p> <p><strong>4. "PLS" folder</strong></p> <p>Includes the dataset used by PLS and the corresponding reproduction code, which corresponds to experiment of RQ2</p> <p><strong>5. " Indicator_Calculation " folder</strong><br>It contains the calculation methods for various metrics in the paper, as well as the corresponding key files. </p> <p><span><strong>6. " Appendix " folder</strong><br></span><span>It includes supplementary materials such as the methodology for metric calculations to address the reviewers' questions.</span> </p> <p><strong>Note</strong></p> <p>We have provided a corresponding README file in each folder to help others reproduce our results</p>
Replication package for "Blended Modeling in Commercial and Open-source Model-Driven Software Engineering Tools: A Systematic Study"
<p>Replication package for the paper <em>Blended Modeling in Commercial and Open-source Model-Driven Software Engineering Tools: A Systematic Study</em>.</p> <p>Protocol</p> <ul> <li><code>/01-protocol/protocol.pdf</code></li> </ul> <p>Data & analysis scripts</p> <p>This replication package is structured as follows:</p> <ul> <li><code>/02-search</code> - Detailed data on the <code>/academic</code> and <code>/grey literature</code> search.</li> <li><code>/03-tools</code> - Identified tools and inclusion/exclusion decisions.</li> <li><code>/04-classification_schema</code> - Classification framework and the corresponding data extraction form.</li> <li><code>/05-data</code> - Clean data in a processable form.</li> <li><code>/06-analysis</code> - Analysis scripts and results.</li> </ul>
Replication Package - How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis
<p>This is the replication package for the paper titled "How Do Requirements Evolve During</p> <p>Elicitation? An Empirical Study Combining Interviews and App Store Analysis", by Alessio Ferrari, Paola Spoletini and Sourav Debnath.</p> <p> </p> <p>The package contains the following folders and files. </p> <p> </p> <p>**<strong>/Experiment Material</strong>**</p> <p>This folder contains the material used for the experiment, and provided to the participants.</p> <p>In particular, it includes the following files:</p> <p> </p> <p>- Happy CampingTM_briefdescription.pdf/docx: brief description of the product for which requirements need to be elicited</p> <p>- Hw_description.pdf/docx: desciption of the tasks to be performed by the participants</p> <p>- Modeling_Intro_Slides.pdf: introductory slides to modelling for requirements engineering</p> <p>- Self-assessment Questionnaire.pdf: first questionnaire to self-assess the mistakes, from the SaPeer method (https://doi.org/10.1007/s00766-020-00334-0) </p> <p>- Self-assessment Questionnaire (Second Interview).pdf: second questionnare to self-assess the mistakes, from the Sapeer method</p> <p> </p> <p>**<strong>/R-analysis</strong>**</p> <p> </p> <p>This is a folder containing all the R implementations of the the statistical tests included in the paper, together with the source .csv file used to produce the results. Each R file has the same title as the associated .csv file. The titles of the files reflect the RQs as they appear in the paper. The association between R files and Tables in the paper is as follows:</p> <p> </p> <p>- RQ1-1-analyse-story-rates.R: Tabe 1, user story rates </p> <p>- RQ1-1-analyse-role-rates.R: Table 1, role rates</p> <p>- RQ1-2-analyse-story-category-phase-1.R: Table 3, user story category rates in phase 1 compared to original rates</p> <p>- RQ1-2-analyse-role-category-phase-1.R: Table 5, role category rates in phase 1 compared to original rates</p> <p>- RQ2.1-analysis-app-store-rates-phase-2.R: Table 8, user story and role rates in phase 2</p> <p>- RQ2.2-analysis-percent-three-CAT-groups-ph1-ph2.R: Table 9, comparison of the categories of user stories in phase 1 and 2</p> <p>- RQ2.2-analysis-percent-two-CAT-roles-ph1-ph2.R: Table 10, comparison of the categories of roles in phase 1 and 2. </p> <p> </p> <p>The .csv files used for statistical tests are also used to produce boxplots. The association betwee boxplot figures and files is as follows. </p> <p> </p> <p>- RQ1-1-story-rates.csv: Figure 4 </p> <p>- RQ1-1-role-rates.csv: Figure 5</p> <p>- RQ1-2-categories-phase-1.csv: Figure 8</p> <p>- RQ1-2-role-category-phase-1.csv: Figure 9</p> <p>- RQ2-1-user-story-and-roles-phase-2.csv: Figure 13</p> <p>- RQ2.2-percent-three-CAT-groups-ph1-ph2.csv: Figure 14</p> <p>- RQ2.2-percent-two-CAT-roles-ph1-ph2.csv: Figure 17</p> <p>- IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv: Figure 15</p> <p>- IMG-only-RQ2.2-frequent-roles.csv: Figure 18</p> <p> </p> <p>NOTE: The last two .csv files do not have an associated statistical tests, but are used solely to produce boxplots.</p> <p> </p> <p>**<strong>/Data-Analysis</strong>**</p> <p> </p> <p>This folder contains all the data used to answer the research questions. </p> <p> </p> <p>**<strong>RQ1.xlsx</strong>**: includes all the data associated to RQ1 subquestions, two tabs for each subquestion (one for user stories and one for roles). The names of the tabs are self-explanatory of their content.</p> <p> </p> <p>**<strong>RQ2.1.xlsx</strong>**: includes all the data for the RQ1.1 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: for each category of user story, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that category for phase 1, and the second one reports the</p> <p>number of user stories in that category for phase 2, considering the specific analyst. </p> <p> </p> <p>* Data Source-role: for each category of role, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that role for phase 1, and the second one reports the</p> <p>number of user stories in that role for phase 2, considering the specific analyst. </p> <p> </p> <p>* RQ2.1 rates: reports the final rates for RQ2.1. </p> <p>NOTE: The other tabs are used to support the computation of the final rates.</p> <p> </p> <p>**<strong>RQ2.2.xlsx</strong>**: includes all the data for the RQ2.2 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.2-category-group: comparison between groups of categories in the different phases, used to produce Figure 14</p> <p> </p> <p>* RQ2.2-role-group: comparison between role groups in the different phases, used to produce Figure 17</p> <p> </p> <p>* RQ2.2-specific-roles-diff: difference between specific roles, used to produce Figure 18</p> <p> </p> <p>**<strong>NOTE:</strong>** the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.2-single-US-category.xlsx</strong>**: includes the data for the RQ2.2 subquestion associated to single categories of user stories.</p> <p>A separate tab is used given the complexity of the computations. </p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Totals: total number of user stories for each analyst in phase 1 and phase 2</p> <p> </p> <p>* Results-Rate-Comparison: difference between rates of user stories in phase 1 and phase 2, used to produce the file</p> <p>"img/IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv", which is in turn used to produce Figure 15</p> <p> </p> <p>* Results-Analysts: number of analysts using each novel category produced in phase 2, used to produce Figure 16.</p> <p>NOTE: the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.3.xlsx</strong>**: includes the data for the RQ2.3 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.3-categories: novel categories produced in phase 2, used to produce Figure 19</p> <p> </p> <p>* RQ2-3-most-frequent-categories: most frequent novel categories</p> <p> </p> <p>**<strong>/Raw-Data-Phase-I</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx, plus the file of the original user stories with annotations (original-us.xlsx). Each file contains two tabs:</p> <p> </p> <p>- Evaluation: includes the annotation of the user stories as existing user story in the original categories (annotated with "E"), novel user story in a certain category (refinement, annotated with "N"), and novel user story in novel category (Name of the category in column "New Feature"). **<strong>NOTE 1:</strong>** It should be noticed that in the paper the case "refinement" is said to be annotated with "R" (instead of "N", as in the files) to make the paper clearer and easy to read. </p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Raw-Data-Phaes-II</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx. Each file contains two tabs:</p> <p> </p> <p>- Analysis: includes the annotation of the user stories as belonging to existing original </p> <p>category (X), or to categories introduced after interviews, or to categories introduced </p> <p>after app store inspired elicitation (name of category in "Cat. Created in PH1"), or to </p> <p>entirely novel categories (name of category in "New Category").</p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Figures</strong>**</p> <p> </p> <p>This folder includes the figures reported in the paper. The boxplots are generated from the </p> <p>data using the tool http://shiny.chemgrid.org/boxplotr/. The histograms and other plots are </p> <p>produced with Excel, and are also reported in the excel files listed above. </p>
Replication Package for the Paper: "Potential Technical Debt and Its Resolution in Code Reviews: An Exploratory Study of the OpenStack and Qt Communities"
<p>This is the replication package for the paper: "Potential Technical Debt and Its Resolution in Code Reviews: An Exploratory Study of the OpenStack and Qt Communities", including the dataset and its description (README.md):</p> <ul> <li><strong>PTD-related Comments in Code Review and Data Extraction Results.xlsx</strong> is the dataset of our paper, which contains 2,030 review comments collected from the Nova project and Neutron project of OpenStack community. Among all the review comments, 163 review comments indicate PTD. For the rows of review comments that are related to PTD, we filled them with blue color as an indicator.</li> <li> <p><strong>README.md</strong></p> </li> </ul>
Replication Package - IoT Roadmap Feasibility Study
<p>Replication package for a Feasibility Study.</p> <p>GOAL: This work aims to report a study with software engineers to characterize an IoT Roadmap's viability, considering the artifacts generated in the context of IoT software systems design already concluded.</p>
Replication Package - IoT Roadmap Observation Study
<p>Replication package for a Observation Study.</p> <p>GOAL: This work aims to analyze the use of the IoT Roadmap with the purpose of understanding in relation to its applicability from the point of view of junior software engineers in the context of the IoT project for COVID-19 developed at the Federal University of Rio de Janeiro.</p>
Linking the gut microbiome to host DNA methylation by a discovery and replication epigenome-wide association study
<p>BACKGROUND: The datafiles deposited here are products of the research project "<strong>Linking the gut microbiome to host DNA methylation by a discovery and replication epigenome-wide association study"</strong></p><p>Authors: Ayşe Demirkan1,2, Jenny van Dongen3,4, Casey T. Finnicum5, Harm-Jan Westra1, Soesma Jankipersadsing1, Gonneke Willemsen3,4, Richard G. Ijzerman6, Dorret I. Boomsma3,4, Erik A. Ehli5, Marc Jan Bonder1, Jingyuan Fu,1,7 Lude Franke1, Cisca Wijmenga1, Eco J.C. de Geus3,4, Alexander Kurilshikov1, Alexandra Zhernakova1</p><p>1 Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</p><p>2 Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine & People-Centered AI institute University of Surrey, Guildford, United Kingdom</p><p>3 Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands</p><p>4 Amsterdam Public Health Research Institute, Amsterdam, the Netherlands</p><p>5 Avera Institute of Human Genetics, Avera McKennan Hospital & University Health Center, Sioux Falls, SD, USA</p><p>6 Department of Endocrinology, Amsterdam University Medical Center, location VUMC, Amsterdam, the Netherlands</p><p>7 Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</p><p><strong>Corresponding Authors: </strong>Alexandra Zhernakova; Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands</p><p>Ayse Demirkan; Section of Statistical Multi-omics, Department of Clinical and Experimental Medicine, School of Biosciences and Medicine & People-Centered AI institute University of Surrey, Guildford, United Kingdom.</p><p>FILES: </p><p>1-merged_lld16s.Rata: Epigenome-wide association of 16s microbial abundances in LifeLines-Deep (LLD, n = 616, 450k methylation array) </p><p>2-lld_mgs.Rdata: Epigenome-wide association ofshotgun metagenomic sequencing derived taxa relative abundances (n = 683, 450k methylation array))</p><p>3-lld_<i>mgs_</i>pathways. Rdata: Epigenome-wide association ofshotgun metagenomic sequencing derived bacterial pathway relative abundances (n = 683, 450k methylation array)</p><p>FUNDING: The Lifelines initiative has been made possible by subsidy from the Dutch Ministry of Health, Welfare and Sport, the Dutch Ministry of Economic Affairs, the University Medical Center Groningen (UMCG), Groningen University and the Provinces in the North of the Netherlands (Drenthe, Friesland, Groningen). The Netherlands Twin Register acknowledges funding from the Netherlands Organization for Scientific Research (NWO): (NWO 911–09–032; NWO 480-04-004; 480-15-001/674, NWO 916-130-82), Biobanking and Biomolecular Research Infrastructure (184.033.111), and the BBRMI-NL-financed BIOS Consortium (NWO 184.021.007), NWO Large Scale infrastructures X-Omics (184.034.019), Genotype/phenotype database for behaviour genetic and genetic epidemiological studies (ZonMw Middelgroot 911-09-032); Netherlands Twin Registry Repository: researching the interplay between genome and environment (NWO-Groot 480-15-001/674); the Avera Institute, Sioux Falls (USA), the European Research Council (Genetics of Mental Illness 230374), the European Research Council (Genetics of Mental Illness 230374), and INRA-Pfizer. Pfizer provided support for data collection, but did not have any additional role in the study design, data analysis, decision to publish, or preparation of the manuscript.</p><p> </p>
Replication Package for "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"
<p># Replication Package for the Paper: "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"</p> <p>This replication package includes the raw data, questionnaire answers, and a Python notebook to reproduce the results detailed in the paper "On The Impact of Adopting AI Libraries in Open Source Projects. A Large Scale Study"</p> <p>## Repository Structure<br>1. **Results:** Contains Excel files with the responses from the 2 human experts and the 5 models and the review of the 3 human reviewers.<br>2. **Tables:** Contains the full Wilcoxon Test Results for H01 and H02 as well as the Anderson Darling test for normality and the Spearmans' Rho.</p> <p>## Replication Process<br>To replicate the results of our study, open the provided Python Notebook in Google Colab and follow the instructions to reproduce the results seamlessly.</p> <p># Instructions for Use<br>To utilize this replicability package, refer to the steps outlined in the notebook file.</p> <p># Remarks<br>If you encounter any issues or have any questions, please get in touch with the paper's authors. We will be glad to assist you!</p>
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