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677 results for “Replication package”
Web Image Formats: Assessment of Their Real-World-Usage and Performance across Popular Web Browsers - Replication Package
<p>Replication package for the paper: Web Image Formats: Assessment of Their Real-World-Usage and Performance across Popular Web Browsers. </p>
Replication package for "Macroevolutionary Origins of Comparative Development"
<p> </p> <p><strong> Replication files and instruction for "Macroevolutionary Origins of Comparative Development," by Riahi, Ideen A. 2023 </strong></p>
Replication package for "An Exploratory Eye Tracking Study on How Developers Classify and Debug Python Code in Different Paradigms"
<p>See the README.md file for more details.</p>
Replication package for "How Consumption Carbon Emission Intensity Varies Across Spanish Households"
<p>This package replicated the empirical analysis of the paper "How Consumption Carbon Emission Intensity Varies Across Spanish Households".</p>
Replication Package of the Paper: An Empirical Study of Untangling Patterns of Two-Class Dependency Cycles
<p>This is a replication package of the paper: "An Empirical Study of Untangling Patterns of Two-Class Dependency Cycles".</p><p>This replication package contains:</p><ul><li><strong>all-projects.xlsx:</strong> list 38 projects' demographic information.</li><li><strong>rq1-recurring-patterns:</strong> contains successful untangling cases into each pattern we manually inspected. In one case, it contains:<ul><li><strong>src/:</strong> src code before and after a commit</li><li><strong>cycle-dependency-change/: </strong>cyclic files presented in dependency graph before and after a commit</li><li><strong>all-modified-files-dependency-change/:</strong> all modified files presented in dependency graph before and after a commit</li><li><strong>diff.txt:</strong> code diff changes</li><li><strong>refactor.json:</strong> mined refactoring operations from RefactoringMiner 2.0.0.</li></ul></li><li><strong>rq3-counterintuitive-solutions: </strong>contain counterintuitive cases dependency cycles are not resolved. The file structure of each case is the same as above.</li><li><strong>rq2-design-relations</strong> contains:<ul><li><strong>dependency-relations.csv:</strong> dependency relations inside and outside dependency cycle. Column 2-25 contains dependency information inside dependency cycle. Column 26-77 contain dependency information outside dependency cycle.</li><li><strong>dependency-type-inside-cycle.csv </strong>lists <strong>24</strong> unique dependency types inside dependency cycle.</li><li><strong>dependency-type-outside-cycle.csv</strong> lists <strong>52</strong> unique dependency types inside dependency cycle.</li></ul></li><li><strong>SrcCode_CycleUntangleEmpirical:</strong> source code to generate data and replicate the results. It contains a README file to explain the purpose of each module.</li></ul><p> </p><p> </p>
Replication package for: "Revisiting Event Study Designs: Robust and Efficient Estimation"
<p>This replication package contains the code and instructions necessary to replicate Borusyak, Kirill, Xavier Jaravel, and Jann Spiess. "Revisiting event study designs: Robust and efficient estimation." Forthcoming Review of Economic Studies (2023).</p>
Replication Package for the Paper: Towards Automated Identification of Violation Symptoms of Architecture Erosion
<p><strong>Abstract of this Study</strong></p> <div> <div>Architecture erosion has a detrimental effect on maintenance and evolution, as the implementation deviates from the intended architecture. To prevent this, development teams need to understand early enough the symptoms of erosion, and particularly violations of the intended architecture. One feasible way is through the automated identification of architecture violations from textual artifacts, and particularly code reviews. In this paper, we developed 15 machine learning-based and 4 deep learning-based classifiers with three pre-trained word embeddings to identify violation symptoms of architecture erosion from developer discussions in code reviews. Specifically, we looked at code review comments from four large open-source projects from the OpenStack (Nova and Neutron) and Qt (Qt Base and Qt Creator) communities. We then conducted a survey and semi-structured interviews to acquire feedback from the involved participants who discussed architecture violations in code reviews, to validate the usefulness of our trained classifiers. Moreover, we conducted additional comparative experiments by evaluating Large Language Model (LLM)-based classifiers, including GPT-4o, Qwen-2.5, and DeepSeek-R1. The results show that, for ML/DL-based classifiers, the SVM classifier based on <em>word2vec</em> pre-trained word embedding performs the best with an F1-score of 0.779. In most cases, classifiers with the <em>fastText</em> pre-trained word embedding model can achieve relatively good performance. Moreover, 200-dimensional pre-trained word embedding models outperform classifiers that use 100- and 300-dimensional models. For ML/DL-based classifiers, ensemble classifiers based on the majority voting strategy can enhance the classifier and outperform the individual classifiers. The findings derived from the online survey and interviews conducted with the involved developers reveal that the violation symptoms identified by our approaches have practical value and can provide early warnings for impending architecture erosion. Furthermore, LLM-based classifiers consistently outperform traditional ML/DL models, with GPT-4o yielding the highest F1-score of 0.851, though ensemble strategies offered no further performance gains for ensemble LLM-based classifiers in our case. We investigate the automated identification of violation symptoms from code reviews using both traditional ML/DL and state-of-the-art LLM techniques. Our contributions include the automated approach for identifying violation symptoms in code reviews, a systematic comparison of ML/DL and LLM approaches, and practitioner-centered insights into their practical usefulness, ultimately contributing to better architectural conformance and sustainability in software systems.</div> </div> <p><strong>Structure of the Replication Package</strong></p> <ul> <li><strong>data.zip</strong> includes: (1) extracted features (<strong>extracted_features</strong>) as inputs of classifiers, that is, word vectors of the extracted violation symptoms based on the three pre-trained word embedding models (i.e., word2vec, fastText, GloVe). (2) <strong>word_embedding</strong> includes pre-trained word embedding models. Due to the large size, we listed the download URL Download_url.txt, and we used embedding_dim.py to change the dimensions of the fastText models. (3) <strong>Violation symptoms.xlsx</strong> and <strong>Randomly_selected_comments.xlsx</strong> represent the review comments labeled as violations and non-violations, respectively.</li> <li><strong>scripts.zip </strong>includes the Python scripts used to run the experiments, including data preprocessing and classifier training.</li> <li><strong>survey and interview.zip</strong> include the survey form, interview protocol and questions, and the template of customized emails that we sent to participants.</li> </ul> <p><strong>Experiment Steps</strong></p> <p>1. Preprocessing and feature extraction: </p> <ul> <li>Run feature_extraction.py to conduct preprocessing and feature extraction after adjusting appropriate parameters.</li> <li>It includes five steps: (1) Tokenization (2) Noise Removal (3) Stop words Removal (4) Capitalization Conversion (5) Stemming.</li> <li>Feature selection methods: word2vec, fastText, and Glove.</li> </ul> <p>2. Training classifiers: </p> <ul> <li>Run <em>Classifiers_ML.py</em> to train machine learning-based classifiers.</li> <li>Run <em>Classifiers_DL_classifiers.py</em> to train deep learning-based classifiers.</li> <li> <div> <div>Run <em>LLM.py</em> to generate LLM-based classifiers.</div> <div>Run <em>LLM_performance.py</em> to evaluate the performance of LLM-based classifiers.</div> <div> <div> <div>Run <em>LLM_voting.py</em> to conduct voting strategy for ensemble LLM-based classifiers.</div> </div> </div> </div> </li> <li>Machine learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Bernoulli Naive Bayes (NB), and k-Nearest Neighbor (kNN).</li> <li>Deep learning algorithm: TextCNN.</li> <li> <div> <div>Large language models: GPT-4o, Qwen-2.5, DeepSeek-R1.</div> </div> </li> </ul> <p><strong>Experiment Environment</strong></p> <p>Required packages and their versions:</p> <ul> <li>torch==1.11.0</li> <li>numpy==1.22.3</li> <li>gensim==4.1.2</li> <li>fasttext==0.9.2</li> <li>pandas==1.4.1</li> <li>torchtext==0.12.0</li> <li>sklearn==0.0</li> <li>scikit-learn==1.0.2</li> <li>w2vembeddings==0.1.2</li> <li>matplotlib==3.5.1</li> <li>tqdm==4.62.3</li> <li>nltk==3.7 </li> <li> <div> <div>openai==1.95.1</div> </div> </li> </ul>
[Replication Package] Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation
<p>This repository contains scripts, datasets, and results of the work <em>"Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation"</em></p> <p><strong>Scripts contained in this Zenodo repository can also be visualized at the following link: <a href="https://anonymous.4open.science/r/lowbit-quantization-D070/README.md">https://anonymous.4open.science/r/lowbit-quantization-D070/README.md</a><br></strong></p>
Replication package for: "They Never Had a Chance: Unequal Opportunities and Fair Redistributions"
<p>Replication package for: Dong, L., Huang, L., and Lien, J.W. (2024) "They Never Had a Chance: Unequal Opportunities and Fair Redistributions", The Economic Journal</p>
Replication Package for the paper: "Behind the Intents: An In-depth Empirical Study on Software Refactoring in Modern Code Review"
<p>This is the replication package for the paper: "Behind the Intents: An In-depth Empirical Study on Software Refactoring in Modern Code Review", published at the 17th International Conference on Mining Software Repositories (MSR ’20). </p> <p> </p> <p>It contains all the preliminary and final results of our empirical methodology. We highlight the manual classification of developers' intents behind code changes with refactoring operations. This might be used for further studies on developers' motivations when performing refactoring. </p> <p> </p> <p>Feel free to use any part of this replication package in your study, please cite as:</p> <p>Matheus Paixão, Anderson Uchôa, Ana Carla Bibiano, Daniel Oliveira, Alessandro Garcia, Jens Krinke, and Emilio Arvonio. 2020. Behind the In-tents: An In-depth Empirical Study on Software Refactoring in Modern Code Review. In 17th International Conference on Mining Software Repositories (MSR ’20), October 5–6, 2020, Seoul, Republic of Korea. ACM, New York, NY,USA, 11 pages.</p>
Replication Package for: Scalable and Reliable Multi-Dimensional Sensor Data Aggregation in Data-Streaming Architectures
<p>This repository contains a replication package and experimental results for our study on <em>Scalable and Reliable Multi-Dimensional Sensor Data Aggregation in Data-Streaming Architectures</em>.</p> <p>It features the presented implementations with Kafka Streams, tools for load generation and data collection, scripts for executing the presented evaluations as well as our raw results and script for analysis. A detailed description is given in the top-level README.md file.</p>
Replication Package for A Systematic Literature Review of Model-driven Security Engineering for Cyber-physical Systems
<p>This package contains supplemental material for the paper "A Systematic Literature Review of Model-driven Security Engineering for Cyber-physical Systems".</p> <p>In particular, we provide:</p> <ul> <li>The survey protocol</li> <li>The used search strings</li> <li>The search results for each library</li> <li>The data extraction template</li> <li>The data extraction sheet for each selected approach</li> <li>The list of all publications and their exclusion stage</li> </ul>
Replication Package for the paper: "How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study"
<p>This is the replication package for the paper: "How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study", published at the 36th International Conference on Software Maintenance and Evolution (ICSME' 20).</p> <p> </p> <p>It contains all the preliminary and final results of our empirical methodology. We highlight the manual classification of design-related and design-unrelated reviews, according to the developers’ intent of improving the structural design of the system. This might be used for further studies on the impact of design discussions on the structural quality of design.</p> <p> </p> <p>Feel free to use any part of this replication package in your study, please cite as:</p> <p>Anderson Uchôa, Caio Barbosa, Willian Oizumi, Publio Blenílio, Rafael Lima, Alessandro Garcia, and Carla Bezerra. How Does Modern Code Review Impact Software Design Degradation? An In-depth Empirical Study. Proceedings of the 36th International Conference on Software Maintenance and Evolution (ICSME), Adelaide, Australia, September 2020.</p>
Replication package with data used in the study: "The effect of code smells on the relationship between design patterns and defects. An empirical study"
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells on the relationship between design patterns and defects. An empirical study"</p> <p>This dataset contains the following folders:</p> <ul> <li> <p>"Analyzed systems" folder:</p> <ul> <li> <p>For each subject system (Ant-1.7, JEdit-4.2, Lucene-2.4, Camel-1.6, Log4j-1.2, Xalan-2.7, Poi-3.0, Ivy-2.0, Xerces-2.0, Velocity-1.6), we identify the following datasets: SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</p> </li> <li> <p>Those csv files include raw data about every class in every release. Each file includes columns which represent:</p> <ul> <li> <p>System: The analyzed system</p> </li> <li> <p>className: A fully qualified class name</p> </li> <li> <p>Pattern: if the class is part of any pattern the cell contains the name of the pattern, and “null” otherwise</p> </li> <li> <p>Smell: if the class is part of affected by any smell the cell contains the name of the smell, and “null” otherwise</p> </li> <li> <p>Bugs: Number of defects reported inside the class (extracted from the PROMISE dataset)</p> </li> </ul> </li> </ul> </li> <li> <p>A "detailed analysis" folder:</p> <ul> <li> <p>For each pattern, we report all the classes which participate in it in all the analyzed systems. The csv files inside this folder follow the same structure as the other csv files reported above</p> </li> </ul> </li> </ul> <p><br> </p>
It Takes a Village to Build a Robot: An Empirical Study of The ROS Ecosystem - Replication Package
<p>Over the past eleven years, the Robot Operating System (ROS), has grown from a small research project into the most popular framework for robotics development. Composed of packages released on the Rosdistro package manager, ROS aims to simplify development by providing reusable libraries, tools and conventions for building a robot. Still, developing a complete robot is a difficult task that involves bridging many technical disciplines. Experts who create computer vision packages, for instance, may need to rely on software designed by mechanical engineers to implement motor control. As building a robot requires domain expertise in software, mechanical, and electrical engineering, as well as artificial intelligence and robotics, ROS faces knowledge based barriers to collaboration.</p> <p>In this paper, we examine how the necessity of domain specific knowledge impacts the open source collaboration model. We create a comprehensive corpus of package metadata and dependencies over three years in the ROS ecosystem, analyze how collaboration is structured, and study the dependency network evolution. We find that the most widely used ROS packages belong to a small cluster of foundational working groups (FWGs), each organized around a different domain in robotics. We show that the FWGs are growing at a slower rate than the rest of the ecosystem, in terms of their membership and number of packages, yet the number of dependencies on FWGs is increasing at a faster rate. In addition, we mined all ROS packages on GitHub, and showed that 82% rely exclusively on functionality provided by FWGs. Finally, we investigate these highly influential groups and describe the unique model of collaboration they support in ROS.</p>
Replication package for: Understanding Cultural Persistence and Change
<p>The zipped folder contains all the files need to replicate the results reported in the paper “Understanding Cultural Persistence and Change” by Paola Giuliano and Nathan Nunn, published in the Review of Economic Studies. </p>
Replication Package for the Paper: "A Machine Learning Based Ensemble Method for Automatic Multiclass Classification of Decisions: A Study of the Hibernate Developer Mailing List"
<p>This is the replication package for the paper: "A Machine Learning Based Ensemble Method for Automatic Classification of Decisions: A Study of the Hibernate Developer Mailing List". It contains the source code and dataset of our experiment for the replication by other researchers. In the meanwhile, we provide brief description of the files in the replication package below.</p> <p><strong>1. code folder</strong></p> <ul> <li><em>experiment.py </em>contains the source code for our experiment, which is conducted on Windows 10 and Python 3.7.0. <strong>Note that you may get slightly</strong> <strong>different experiment results when conducting the experiments on different environment configurations.</strong></li> <li><em>requirement.txt</em> records all the installation packages and their version numbers needed for the current program to run. You can use "<em>pip install -r requirement.txt</em>" to rebuild the project and install all dependencies. <strong>Note that you may get slightly different experiment results when using different packages or versions. </strong></li> </ul> <p><strong>2. dataset folder</strong></p> <ul> <li><em>decisions.xlsx </em>contains 844 labelled sentence-level decisions from the Hibernate developer mailing list.</li> </ul>
Replication Package for the Paper: "Code Smells Detection via Code Review: An Empirical Study"
<p>This repository contains the data and results from the paper "Code Smells Detection via Code Review: An Empirical Study" submitted to ESEM 2020.</p> <p> </p> <p><strong>1. data folder</strong></p> <p>The data folder contains the retrieved 269 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell Term, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2. scripts floder</strong></p> <p>The scripts folder contains the Python script that was used to search for code smell terms and the list of code smell terms.</p> <ul> <li><em>smell-term/general_smell_terms.txt</em> contains general code smell terms, such as "code smell".</li> <li><em>smell-term/specific_smell_terms.txt</em> contains specific code smell terms, such as "dead code".</li> <li><em>smell-term/misspelling_terms_of_smell.txt</em> contains the misspelling terms of 'smell', such as "ssell".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack.</li> <li><em>get_comments.py</em> is used for getting review comments for each code change.</li> <li><em>smell_search.py</em> is used for searching review comments that contain code smell terms.</li> </ul> <p> </p> <p><strong>3. project folder</strong></p> <p>The project folder contains the MAXQDA project files. The files can be opened by MAXQDA 12 or higher versions, which are available at https://www.maxqda.com/ for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at https://www.maxqda.com/trial for download.</p> <ul> <li><em>Data Labeling & Encoding for RQ2.mx12</em> is the results of data labeling and encoding for RQ2, which were analyzed by the MAXQDA tool.</li> <li><em>Data Labeling & Encoding for RQ3.mx12</em> is the results of data labeling and encoding for RQ3, which were analyzed by the MAXQDA tool.</li> </ul>
On the Importance and Shortcomings of Code Readability Metrics: A Case Study on Reactive Programming - replication package
<p>This is the replication package for the conference paper submission "On the Importance and Shortcomings of Code Readability Metrics: A Case Study on Reactive Programming"</p> <p><strong>Contents:</strong></p> <ul> <li>measurements.zip <ul> <li>DATASET_ORIGINAL.csv</li> <li>DATASET_REACTIVE.csv</li> </ul> </li> <li>source_code.zip <ul> <li> source_code_orig <ul> <li>Client.java</li> <li>Connection.java</li> <li>Server.java</li> <li>TcpConnection.java</li> <li>UdpConnection.java</li> </ul> </li> <li> source_code_rx <ul> <li>Client.java</li> <li>Connection.java</li> <li>Server.java</li> <li>TcpConnection.java</li> <li>UdpConnection.java</li> </ul> </li> </ul> </li> </ul> <p> </p>
Replication package with data used in the study: The effect of code smells and design patterns on two change-related metrics: An exploratory study
<p>This is a replication package with data used in a study by T. Alkhaeir and B. Walter "The effect of code smells and design patterns on two change-related metrics: An exploratory study"</p> <p>This dataset contains the following folders:</p> <ul> <li>Aggregated Results Per System <ul> <li> For each subject system (AOI, Jedit, JHotDraw), we identify the following datasets: DP, nDP, S, nS ,SDP, nSDP, SnDP, and nSnDP. Each dataset is represented by a separate csv file.</li> <li> Those csv files include raw data about every class in every release, the csv files also include columns which represent: <ul> <li>- CHURN (CLPLPR(C)*100): defined as the sum of added and deleted lines in a class in a release, adjusted to the size of the class and to the number of revisions in the release;</li> <li>- and FREQ (MTPR(C)*100): defined as the average number of changes made to a class in a release, adjusted to the number of revisions in the release</li> </ul> </li> </ul> </li> <li>Detailed Results Per Smell Or Pattern <ul> <li> For each specific code smell (S) in each public release (Rel) of all subject systems, we identify SDP and SnDP datasets. Each dataset is in a separate .csv file</li> <li> For each specific design pattern (DP) in each public release (Rel) of all subject systems, we identify SDP and nSDP </li> </ul> </li> <li>Plots<br> We also include QQ plots for CHURN, FREQ values for every dataset in every system, that could serve as a supplementary data for the paper.</li> </ul>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.