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677 results for “Replication package”
Replication Package for "Why Do Deep Learning Projects Differ in Compatible Framework Versions? An Exploratory Study"
<p>This dataset contains scripts and data used to generate relevant results for this paper. Detailed information are described in README.md. </p> <p>code</p> <p>This folder contains all the scripts used for the experiment. The upgrade.py and downgrade.py are used to perform upgrade and downgrade runs. The pairing.py is used to generate the DFVC pairs. The main.py is used to identify root causes of DFVC pairs.</p> <p>result</p> <p>This folder contains all the results of the experiments, including the runtime output (e.g., a_1.0.0.txt), the runtime environment (e.g., condalist_1.0.0.txt), and the project's runtime commands (e.g., pytorch-cifar.xlsx) of all tested 90 PyTorch and 50 TensorFlow projects.</p> <p><br> Distribution of dfvc pairs.xlsx</p> <p>This file includes 6,926 DFVC pairs and their root causes.</p> <p>Tested framework versions.xlsx</p> <p>This file includes the framework versions tested and the Python versions that the framework versions are compatible with.</p> <p>Tested projects.xlsx</p> <p>This file includes the tested 90 PyTorch projects and 50 TensorFlow projects. We provide the following main information: (a) project name, (b) stars, (c) link, (d) the starting version, (e) python version, (f) incompatible upgrade/downgrade version, and (g) compatible versions.</p>
Replication Package for "Function-as-a-Service Performance Evaluation: A Multivocal Literature Review"
<p><strong>Paper</strong></p> <p>J. Scheuner and P. Leitner, “Function-as-a-Service Performance Evaluation: A Multivocal Literature Review,” Accepted at the <a href="https://www.journals.elsevier.com/journal-of-systems-and-software">Journal of Systems and Software (JSS)</a>, Preprint: <a href="https://arxiv.org/abs/2004.03276">arXiv:2004.03276</a>.</p> <blockquote> <p>Function-as-a-Service (FaaS) is one form of the serverless cloud computing paradigm and is defined through FaaS platforms (e.g., AWS Lambda) executing event-triggered code snippets (i.e., functions). Many studies that empirically evaluate the performance of such FaaS platforms have started to appear but we are currently lacking a comprehensive understanding of the overall domain. To address this gap, we conducted a multivocal literature review (MLR) covering 112 studies from academic (51) and grey (61) literature. We find that existing work mainly studies the AWS Lambda platform and focuses on micro-benchmarks using simple functions to measure CPU speed and FaaS platform overhead (i.e., container cold starts). Further, we discover a mismatch between academic and industrial sources on tested platform configurations, find that function triggers remain insufficiently studied, and identify HTTP API gateways and cloud storages as the most used external service integrations. Following existing guidelines on experimentation in cloud systems, we discover many flaws threatening the reproducibility of experiments presented in the surveyed studies. We conclude with a discussion of gaps in literature and highlight methodological suggestions that may serve to improve future FaaS performance evaluation studies.</p> </blockquote> <pre>@article{scheuner:20-jss, author = {Joel Scheuner and Philipp Leitner}, title = {Function-as-a-Service Performance Evaluation: A Multivocal Literature Review}, journal = {J. Syst. Softw.}, volume = {170}, pages = {110708}, year = {2020}, doi = {10.1016/j.jss.2020.110708} }</pre> <p><strong>Dataset</strong></p> <p>All extracted data originating from academic and grey literature studies are available as machine-readable CSV (<a href="https://github.com/joe4dev/faas-performance-mlr/blob/master/data/faas_mlr_raw.csv">./data/faas_mlr_raw.csv</a>) and human-readable XLSX (<a href="https://github.com/joe4dev/faas-performance-mlr/blob/master/data/faas_mlr_raw.xlsx">./data/faas_mlr_raw.xlsx</a>). The Excel file also contains all 700+ comments with guidance, decision rationales, and extra information. It is configured with a filtered view to display only <em>relevant</em> sources but contains the complete data (i.e., including discussion for sources considered to be <em>not relevant</em> in our context).</p> <p>Find further documentation in the README.md or on Github: <a href="https://github.com/joe4dev/faas-performance-mlr/">https://github.com/joe4dev/faas-performance-mlr/</a></p> <p>The latest version is also available online as an interactive Google spreadsheet (GSheet): <a href="https://docs.google.com/spreadsheets/d/1EK9yg9fMZIDybnbi7thsnBx1NdqDmkW86sMygH9r8q8">https://docs.google.com/spreadsheets/d/1EK9yg9fMZIDybnbi7thsnBx1NdqDmkW86sMygH9r8q8</a></p> <p> </p>
Replication package for: A Model of Relative Thinking
<p>Data replication package for Bushong, Benjamin, Matthew Rabin, and Joshua Schwartzstein, "A Model of Relative Thinking", forthcoming, <em>The </em><em>Review of Economic Studies.</em></p> <p> </p> <p>Contents: Stata data from two experiments and Stata .do files to replicate analyses. </p>
Replication Package for the paper: Evaluating the Agreement among Technical Debt Measurement Tools: Building an Empirical Benchmark of Technical Debt Liabilities
<p>This is the replication package for the Archetypal Analysis conducted in the paper "Evaluating the Agreement among Technical Debt Measurement Tools: Building an Empirical Benchmark of Technical Debt Liabilities" accepted at Springer's EMSE Journal.</p> <p>It contains:</p> <ul> <li>The dataset with TD measurements from three TD tools for 25 Java projects</li> <li>The dataset with TD measurements from three TD tools for 25 JS projects</li> <li>The script to run the Archetypal Analysis on the two datasets</li> </ul>
On the Relationship between Refactoring Actions and Bugs: A Differentiated Replication -- Replication Package
<p>This dataset contains working data sets and analysis scripts for the paper "On the Relationship between Refactorings and Bugs: A Differentiated Replication" by Massimiliano Di Penta, Gabriele Bavota, and Fiorella Zampetti, published at the ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2020).</p>
Code-Level Model Checking in the Software Development Workflow -- Replication Package
<p>This experience report describes a style of applying symbolic model checking developed over the course of four years at Amazon Web Services (AWS). Lessons learned are drawn from proving properties of numerous C-based systems, e.g., custom hypervisors, encryption code, boot loaders, and an IoT operating system. Using our methodology, we find that we can prove the correctness of industrial low-level C-based systems with reasonable effort and predictability. Furthermore, AWS developers are increasingly writing their own formal specifications. All proofs discussed in this paper are publicly available on GitHub. All proofs and specifications described in the paper are available, under the Apache 2.0 license, on the GitHub repository located at <a href="https://github.com/awslabs/aws-c-common/">https://github.com/awslabs/aws-c-common/</a> This is the master repository for AWS C Common library, and is in active use by the AWS C Common development team. The description of the contents of this repository are based off commit <code>b0ea9f35df8934f9e03fc3bab3919d55efd69b88</code>, although they are not expected to change significantly in the future.</p>
Replication Package for the Paper: "An Empirical Analysis of the Manual Detection of Code Smells via Code Review"
<p>This repository contains the data and results from the paper "An Empirical Analysis of the Manual Detection of Code Smells via Code Review" submitted to SANER 2021.</p> <p> </p> <p><strong>1. "data.zip" file contains the following three folders: </strong></p> <p> </p> <p><strong>1). data folder</strong></p> <p>The data folder contains the retrieved 1,174 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2). scripts folder</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>keywords.txt</em> contains the keywords keywords asociated with code smells, such as "smell, duplication, and dead".</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>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</li> <li>keywords_improve.py is used for improving the keyword-based mining approach.</li> <li>tools.py is used for supporting the process of keywords improving.</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> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords asociated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Replication Package for the Paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions"
<p>This is the replication package for the paper: "Will Data Influence the Experiment Results?: A Replication Study of Automatic Identification of Decisions". 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. main_code folder</strong></p> <ul> <li><em>automatic_approach.py </em>contains the main source code of the automatic approach for identifying decisions in our experiment, which is conducted on MacOs and Python 3.7.9. <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>EASE2020 - 650 decisions.xlsx </em>contains 650 decision sentences from our previous work (EASE2020)</li> <li><em>EASE2020 - 650 non-decisions.xlsx </em>contains 650 non-decision sentences from our previous work (EASE2020)</li> <li><em>Our 844 relabeled decisions.xlsx</em> contains 844 relabeled decisions in this work.</li> <li><em>Our 750 assumptions.xlsx</em> contains 750 assumptions from our previous work (APSEC2019)</li> </ul> <p><strong>3. RQ1 folder</strong></p> <ul> <li><em>experiment_RQ1.py</em> contains the main source code of the experiment for answering RQ1, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>4. RQ2 folder</strong></p> <ul> <li><em>experiment_RQ2.py</em> contains the main source code of the experiment for answering RQ2, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul> <p><strong>5. RQ3 folder</strong></p> <ul> <li><em>experiment_RQ3.py</em> contains the main source code of the experiment for answering RQ3, which is conducted on the same environment configuration as the <em>automatic_approach.py.</em></li> </ul>
Replication package for: Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia
<p>This package contains the data and code needed to reproduce tables and figures in Iacovone, Maloney and McKenzie (forthcoming) "</p> <p><strong>Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia"., <em>Review of Economic Studies</em></strong>. </p> <p>Anonymized firm data are provided for analyzing impacts on management and employment. Authorized log files and output files are provided for analysis that uses the confidential EAM data, along with instructions for applying to access these data if required.</p>
Replication package: Does the duration of rapid release cycles affect the bug handling activity?
<p>Replication package for the paper: "Does the duration of rapid release cycles affect the bug handling activity?"</p>
Replication Package for the Paper: "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community"
<p>This repository contains the data and results from the paper "Understanding Code Smell Detection via Code Review: A Study of the OpenStack Community" submitted to ICPC 2021.</p> <p> </p> <p><strong>1. "data.zip" contains the following three folders:</strong></p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the retrieved 1,190 reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</p> <p> </p> <p><strong>2) scripts folder</strong></p> <p>The scripts folder contains the Python scripts that were used to search for code smell terms and the list of code smell terms.</p> <ul> <li> <p><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</p> </li> <li> <p><em>get_changes.py</em> is used for getting code changes from OpenStack.</p> </li> <li> <p><em>get_comments.py</em> is used for getting review comments for each code change.</p> </li> <li> <p><em>keywords_search.py</em> is used for searching review comments that contain at least one keyword.</p> </li> <li> <p><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</p> </li> <li> <p><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</p> </li> <li> <p><em>tools.py</em> is used for supporting the process of keywords improving.</p> </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 <a href="https://www.maxqda.com/">https://www.maxqda.com/</a> for download. You may also use the free 14-day trial version of MAXQDA 2018, which is available at <a href="https://www.maxqda.com/trial">https://www.maxqda.com/trial</a> for download.</p> <ul> <li> <p><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.</p> </li> <li> <p><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.</p> </li> </ul> <p> </p> <p><strong>2. Keywords associated with code smells.pdf</strong></p> <p>This file contains the final set of keywords associated with code smells that we identified by following the systematic approach proposed by Bosu and his colleagues in their paper: Identifying the Characteristics of Vulnerable Code Changes: An Empirical Study, FSE 2014.</p>
Replication package for: Credit Shocks and Equilibrium Dynamics in Consumer Durable Goods Markets
<p>Alessandro Gavazza and Andrea Lanteri, Credit Shocks and Equilibrium Dynamics in Consumer Durable Goods Markets, Review of Economic Studies</p> <p>The package includes four folders: data, stata_code, matlab_code, and figures. Please see the readme.pdf file for details.</p>
Replication Package for: Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures
<p>This repository contains a replication package and experimental results for our study <em>Theodolite: Scalability Benchmarking of Distributed Stream Processing Engines in Microservice Architectures</em>.</p> <p>The following description can also be found in the README.md file.</p> <p><strong>Repeating Benchmark Execution</strong></p> <p><em><strong>The following introduction describes how to repeat our scalability experiments. If you plan to conduct your own studies, we suggest to use the latest version of <a href="https://github.com/cau-se/theodolite">Theodolite</a> with significantly enhanced usability.</strong></em></p> <p>The Apache Kafka Streams scalability experiments of our study were executed with <a href="https://github.com/cau-se/theodolite/tree/v0.1.2">Theodolite v0.1.2</a>. To repeat our Kafka Streams experiments:</p> <ol> <li>Clone and install <a href="https://github.com/cau-se/theodolite/tree/v0.1.2">Theodolite v0.1.2</a> according to the official documentation located in <code>execution</code>.</li> <li>Copy the file <code>repeat-kstream.sh</code> into Theodolite's <code>execution</code> directory.</li> <li>Run the repetition file with <code>./repeat-kstream.sh</code> from within the <code>execution</code> directory.</li> </ol> <p>Our Apache Flink benchmark implementations are currently migrated to the latest version of Theodolite. <a href="https://github.com/cau-se/theodolite/tree/apache-flink">Theodolite's <code>apache-flink</code> Branch</a> provides the basis for our Flink scalability experiments. To repeat them:</p> <ol> <li>Clone <a href="https://github.com/cau-se/theodolite/tree/apache-flink">Theodolite's <code>apache-flink</code> Branch</a> and install Theodolite according to the official documentation located in <code>execution</code> (should be identical to the installation for Kafka Streams (see above)).</li> <li>Copy the files <code>repeat-flink-without-checkpointing.sh</code> and <code>repeat-flink-with-checkpointing.sh</code> into Theodolite's <code>execution</code> directory.</li> <li>Switch to the <code>execution</code> directory.</li> <li>Run the first repetition file with <code>./repeat-flink-with-checkpointing.sh</code>.</li> <li>Disable checkpointing by reconfiguring the Kubernetes resources <code>jobmanager-job.yaml</code> and <code>taskmanager-job-deployment.yaml</code> for each benchmark (<code>uc{1,2,3,4}-application</code>) by setting the environment variable <code>CHECKPOINTING</code> to <code>"false"</code>.</li> <li>Run the second repetition file with <code>./repeat-flink-without-checkpointing.sh</code>.</li> </ol> <p><em>Please note that the naming of our benchmarks recently changed. While our publication already uses the new naming, the corresponding Theodolite versions are is still using the old one. Specifically, this means that UC1 in the publication is UC1 in Theodolite, UC2 in the publication is UC3 in Theodolite, UC3 in the publication is UC4 in Theodolite, and UC4 in the publication is UC2 in Theodolite.</em></p> <p><strong>Raw Measurements</strong></p> <p>The results of above benchmark execution can be found in the <code>measurements</code> directory. These are CSV files, containing the measured lag trend over time for a certain subexperiment. Theodolite creates a bunch of additional files, which serve for debugging and preliminary interpretation. As these files are not required for replication, we do not included them in this package.</p> <p>The CSV files are named according to the schema <code>exp{id}_{uc}_{load}_{inst}_totallag.csv</code>, where <code>{id}</code> represents the experiment ID, assigned by Theodolite, <code>{uc}</code> the benchmark name, <code>{load}</code> the generated load, and <code>{inst}</code> the number of evaluated instances.</p> <p>The CSV table <code>experiments.csv</code> provides an overview about the configurations used in each experiment.</p> <p><strong>Reproducing Scalability Analysis</strong></p> <p><em><strong>The following introduction describes how to repeat our scalability analysis, either with our measurements or with your own. If you plan to conduct your own studies, we suggest to use the latest version of <a href="https://github.com/cau-se/theodolite">Theodolite</a> with significantly enhanced usability.</strong></em></p> <p>Analyzing the Theodolite's measurements is done using two Jupyter notebooks. In general, these notebooks should be runnable by any Jupyter server. Python 3.7 or 3.8 is required (e.g., in a virtual environment) as well as some Python libraries, which can be installed via: <code>pip install -r requirements.txt</code>. See the <a href="https://github.com/cau-se/theodolite/tree/master/analysis">Theodolite documentation</a> for additional installation guidance.</p> <p><strong>Obtaining a Scalability Graph as a CSV File</strong></p> <p>The <code>scalability-graph.ipynb</code> notebook combines the measurements (i.e., the <code>totallag.csv</code> files) of one experiment. It produces a CSV file, which provides a mapping of load intensities to minimum required resources for that load (i.e., the scalability graph). The CSV files are named according to the schema <code>exp{id}_min-suitable-instances.csv</code>, where <code>{id}</code> represents the experiment ID. Additional guidance is provided in the notebook.</p> <p><strong>Resulting Scalability Graph CSV Files</strong></p> <p>The <code>results</code> directory provides the scalability graphs for all our executed experiments.</p> <p><strong>Visualization of the Scalability Graph</strong></p> <p>The <code>scalability-graph-plotter.ipynb</code> notebook creates PDF plots of a scalability graph and allows to combine multiple scalability graphs in one plot. It can be adjusted to match the desired visualization.</p> <p><strong>Acknowledgments</strong></p> <p>This research is funded by the German Federal Ministry of Education and Research (BMBF) under grant no. 01IS17084 and is part of the <a href="https://www.industrial-devops.org">Titan project</a>.</p>
Replication Package for the Paper: Understanding Architecture Erosion: The Practitioners' Perceptive
<p>This is the replication package for the paper: "Understanding Architecture Erosion: The Practitioners' Perceptive". It contains the source code for collecting data from Stack Overflow and our dataset of this study. In the meanwhile, we provide a brief description of the files.</p> <p><strong>1. Count Results & Search Terms.xlsx</strong><br> includes the search results, count results of Table 1, and 28 search terms.</p> <p><strong>2. Dataset from online developer communities.docx</strong><br> comprises of 80 posts and blogs that discuss architecture erosion (178 pages in MS Word organized by their sources), and their URL links that were collected from several popular online developer communities.</p> <p><strong>3. Questionnaire of and dataset from surveys.docx</strong><br> includes an example of the customized emails and 10 formal surveys from the participants.</p> <p><strong>4. Dataset from Interviews.docx</strong><br> comprises of 4 interviewees from the participants.</p> <p><strong>5. Data Labelling & Encoding.mx20</strong><br> is the results of data labelling and encoding that were analyzed by the MAXQDA tool. We extracted data from the posts, labelled them according to RQs, and encoded the extracted data using Constant Comparison. The file can be opened by MAXQDA 20 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 2020, which is available at https://www.maxqda.com/trial for download.</p> <p><strong>6. SO_Spider.zip</strong><br> includes the code for data collection and filtering from Stack Overflow.</p>
Replication package for How the R Community Creates and Curates Knowledge: An Extended Study of Stack Overflow and Mailing Lists
<p>This dataset was used in the paper: "How the R Community Creates and Curates Knowledge: An Extended Study of Stack Overflow and Mailing Lists", Journal of Empirical Software Engineering, to appear.</p>
Replication package for the paper :The Relationship Between Different Python Argument-Passing Mechanisms and Fixes: An Empirical Study
<p><strong>Abstract:</strong></p> <p>Modern programming languages, such as Python, have introduced a variety of constructs and syntactical elements to make software development more efficient and concise. Examples include lambda functions, comprehension collections, or mechanisms to facilitate the passing of arguments to a function. While many of such constructs may, in principle, be beneficial for developers, recent studies have shown that certain programming constructs may affect program understanding and even induce more fixes than other changes. <br>This paper studies the effect of different Python argument-passing mechanisms to investigate their relationship with code proneness to be fixed. Specifically, we study the fix-proneness for what concerns function definitions and invocations. This is done by analyzing the evolutionary history of 200 Python projects, for a total of about 3M functions and 12M call sites. While there are varying effects for what concerns parameter declaration mechanisms, we found evidence that keyword-based argument passing is less defect-prone than positional argument passing, and this is not affected by size-related confounding factors.</p>
Replication Package for "VALIDATE: A Deep Dive into Vulnerability Prediction Datasets"
<p>Replication Package for "VALIDATE: A Deep Dive into Vulnerability Prediction Datasets"</p><ul><li>SDR Results</li><li>SDR Queries</li><li>VALIDATE User Guide</li><li>Original Studies References in BibTeX</li></ul>
Replication Package: Vulnerably (Mis)Configured? Exploring 10 Years of Developers' Q&As on Stack Overflow
<p><strong>Welcome to the public repository for the additional content of the paper "Vulnerably (Mis)Configured? Exploring 10 Years of Developers' Q&As on Stack Overflow", accepted at the International Working Conference on Variability Modelling of Software-Intensive Systems (VAMOS) 2024.</strong></p><p>This repository provides additional information to the conducted exploratory study on configuration-related vulnerabilities, including the following files:</p><ul><li>README.txt</li><li>LICENSE.txt</li><li>DATASET_CONFIG_VULN_SO.csv: sheet containing data of 651 StackOverflow posts, including additional classifications based on manual analyses and automatic topic modeling</li></ul><p><strong>Instructions for using the dataset</strong></p><ol><li>Download and open the dataset (platform-independent CSV file).</li><li>The dataset includes 16 columns (A – P):<br>- Columns A – J: Original data fetched from the BigQuery Stack Overflow dataset (<i>Question_ID, Year_Asked, Question_Title, Question_Body, Question_Tags, View_Count, Question_Rating, Favorite_Count, Status, Answer_Count</i>)<br>- Columns K – N: Manually extracted data from the Stack Overflow posts (<i>System, Configuration Context, Security Context, Topic</i>)<br>- Column O: Data based on the automated topic modeling (<i>Configuration Topic</i>)<br>- Column P: Additional data extracted from the Stack Overflow posts without further classifications (<i>Additional Comments</i>)</li></ol><p><strong>Requirements</strong></p><ul><li>No requirements</li></ul><p><strong>Further information</strong></p><ol><li>The dataset is based on a search string (SQL query; August 1, 2023) applied on the Google BigQuery Stack Overflow dataset:<i> </i><br><i>("secur*") AND ("vulnerabilit*" OR "weakness*" OR "breach*" OR "exposure*" OR "CVE*" OR "CWE*") AND ("config*")</i></li><li>Originally, the dataset included 1,235 post which were limited by the first and second authors to 651 posts (34 deleted posts, 550 posts out of scope) using the following selection criteria: <br>- The post has been created in the last decade (2013-2022).<br>- The post is still available on the Stack Overflow website.<br>- The post is directly connected to a vulnerability-related issue in the context of configuring.</li><li>Topic modeling algorithm used: Latent Dirichlet Allocation (LDA)<br>- Settings: 200 iterations (coherence value = 0.6 for k = 7 to 11), α = k, β = 0.01</li></ol>
The Blockchain Trilemma: an Evaluation Framework (Replication Package)
<p>This repository contains essential data files used to generate graphs and statistics related to various blockchain ecosystems. These files are the result of aggregation and cleaning procedures applied to raw data collected from reputable sources within each respective blockchain network.<br> </p> <h2><strong>Transaction Per Second</strong></h2> <ol> <li><strong>Cardano</strong>: <ol> <li>Theoretical: 5.35</li> <li>Maximum: 5.74</li> </ol> </li> <li><strong>Solana</strong>:<br> <ol> <li>Theoretical: 710,000</li> <li>Maximum: 1763</li> </ol> </li> <li><strong>Arbitrum</strong>: <ol> <li>Theoretical: 40000</li> <li>Maximum: 3.09</li> </ol> </li> <li><strong>zkSync</strong>: <ol> <li>Theoretical: 2000</li> <li>Maximum: 0.521</li> </ol> </li> <li><strong>Polygon:</strong> <ol> <li>Theoretical: 7200</li> <li>Maximum: 101.97 </li> </ol> </li> <li><strong>Bitcoin</strong>: <ol> <li>Theoretical: 27</li> <li>Maximum: 4.53</li> </ol> </li> <li><strong>Ethereum</strong>: <ol> <li>Theoretical: 30</li> <li>Maximum: 19.86</li> </ol> </li> </ol> <h2>Nakamoto Coefficients</h2> <ol> <li><strong>arbitrum_aggregators.json</strong> <ol> <li><strong>Description:</strong> presents information on Arbitrum network aggregators' transaction count</li> <li><strong>Data Source: </strong>https://etherscan.io/apis</li> </ol> </li> <li><strong>cardano_stake.json</strong> <ol> <li><strong>Description:</strong> details stake-related data pertinent to the Cardano blockchain. For each exchange address the amount of holded stake</li> <li><strong>Data Source: </strong>https://pooltool.io/</li> </ol> </li> <li><strong>polygon_validators.json</strong> <ol> <li><strong>Description:</strong> provides insights into validators within the Polygon network. For each validator's name, the amount of held stake.</li> <li><strong>Data Source: </strong>https://wallet.polygon.technology</li> </ol> </li> <li><strong>solana_validators.json</strong> <ol> <li><strong>Description: </strong>provides information on validators operating within the Solana network. For each validator, the amount of held stake and details about the validator node.</li> <li><strong>Data Source: </strong>https://www.validators.app/</li> </ol> </li> <li><strong>zksync_validators.json</strong> <ol> <li><strong>Description: </strong>Contains data on validators associated with the zkSync protocol. For each validator's address, the amount of transactions processed.</li> <li><strong>Data Source: </strong>https://etherscan.io/apis</li> </ol> </li> </ol> <h2>Security Quantitative Evaluation</h2> <ol> <li><strong>Cardano</strong><br> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 119</li> <li>33% Stake: 895,706,097.1264836 ADA</li> <li>ADA Value @ 13 Dec-2023 = 0.59</li> <li>Cost of Attack: 895,706,097.1264836 * 0.59 = 528,466,597.3046253 $</li> </ol> </li> <li><strong>Solana</strong> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 18</li> <li>33% Stake: 126,950,233.4364128 SOL</li> <li>SOL Value @ 13 Dec-2023 = 71.78</li> <li>Cost of Attack: 126,950,233.4364128 * 71.78 = 9,112,487,756.06571 $</li> </ol> </li> <li><strong>Polygon</strong><br> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 2</li> <li>33% Stake: 1,016,718,726.6246895 MATIC</li> <li>MATIC Value @ 13 Dec-2023 = 0.8317</li> <li>Cost of Attack: 1,016,718,726.6246895 * 71.78 = 845,604,964.9337542 $</li> </ol> </li> <li><strong>Ethereum</strong> <ol> <li>Cardinality of the smallest amount of nodes adding up to 33% of stake: 1</li> <li>33% Stake: 28907872.7327942/3 = 9,635,957.577598067 ETH</li> <li>ETH Value @ 08 Jan-2024 = 2133,90</li> <li>Cost of Attack: 9,635,957.577598067 * 2133,90 = 20,562,169,874.8365 $</li> </ol> </li> <li><strong>Bitcoin: </strong>$7.9 billion <a href="https://www.investopedia.com/terms/1/51-attack.asp#:~:text=4-,What%20Is%20a%2051%25%20Attack%3F,total%20hashing%20or%20validating%20power.">source</a></li> </ol>
Replication Package for: "Adapting to Climate Risk with Guaranteed Credit: Evidence from Bangladesh"
<p>Contains the code and publicly available datasets to replicate "Adapting to Climate Risk with Guaranteed Credit: Evidence from Bangladesh". Simulated data sets are provided in place of confidential datasets. See Readme file for details on how to obtain confidential data. </p>
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