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16 results for “OpenStack”
Administrative OpenStack Traffic
<p>This is a simple dataset which groups the administrative network traffic volume from OpenStack clouds by Virtual Machine (VM) operations. This dataset considers ten different images of OS for the VMs.</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>
Archival bundle of the data used for "Predictive Auto-scaling with OpenStack Monasca" (UCC 2021)
<p>This archive contains the data used for the paper</p> <p><strong>Predictive Auto-scaling with OpenStack Monasca</strong><br> <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella<br> 2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing (UCC)<br> <a href="https://doi.org/10.1145/3468737.3494104">10.1145/3468737.3494104</a></p> <p>Follow the instructions provided in the <a href="https://github.com/giacomolanciano/UCC2021-predictive-auto-scaling-openstack">companion repo</a> to automatically download and decompress the archive. The following files are included:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> <p>amphora-x64-haproxy.qcow2</p> </td> <td> <p>Image used to create Octavia amphorae</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>.log</p> </td> <td> <p>distwalk run log</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-pred.json</p> </td> <td> <p>Predictive metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-real.json</p> </td> <td> <p>Actual metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-times.csv</p> </td> <td> <p>Client-side response time for each request sent during a run</p> </td> </tr> <tr> <td> <p>model_dumps/*</p> </td> <td> <p>Dumps of the models and data scalers used for the validation</p> </td> </tr> <tr> <td> <p>predictor.log</p> </td> <td> <p>monasca-predictor log</p> </td> </tr> <tr> <td> <p>predictor-times.log</p> </td> <td> <p>monasca-predictor` log (timing info only)</p> </td> </tr> <tr> <td> <p>predictor-times-{lin,mlp,rnn}.{csv,log}</p> </td> <td> <p>monasca-predictor log (timing info only, group by predictor)</p> </td> </tr> <tr> <td> <p>super_steep_behavior.csv</p> </td> <td> <p>Dataset used to train MLP and RNN models</p> </td> </tr> <tr> <td> <p>test_behavior_02_distwalk-6t_last100.dat</p> </td> <td> <p>distwalk load trace</p> </td> </tr> <tr> <td> <p>ubuntu-20.04-min-distwalk.img</p> </td> <td> <p>Image used to create Nova instances for the scaling group</p> </td> </tr> </tbody> </table> <p>* <em>contact author</em></p>
Replication Package for the Paper: "Understanding Code Snippets in Code Reviews: A Preliminary Study of the OpenStack Community"
<p>This is the replication package for the paper: "Understanding Code Snippets in Code Reviews: A Preliminary Study of the OpenStack Community", including dataset and so on (see the description below) : </p> <ul> <li> <p><strong>Data of Code Snippets in Code Review.xlsx</strong> is the dataset of our paper, which contains 10,790 review comments collected from the Nova project and Neutron project of OpenStack community. Among all the review comments, 626 review comments contain code snippets. For the rows of review comments with code snippets, we filled them with blue color as an indicator.</p> </li> <li> <p><strong>Examples for Each Purpose.xlsx</strong> contains six review comment examples for the six detailed purposes mentioned in our paper (see Section 4.2).</p> </li> <li> <p><strong>README.md</strong></p> </li> </ul>
Replication Package for the Paper: "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities"
<p>This repository contains the data and results from the paper "Code Smells Detection via Modern Code Review: A Study of the OpenStack and Qt Communities" submitted to the ICPC 2021 special issue of the Empirical Software Engineering Journal, 2021.</p> <p> </p> <p>The replication package contains the following two folders:</p> <p> </p> <p><strong>1) data folder</strong></p> <p>The data folder contains the following four folders, which is organized by research questions (RQs).</p> <ul> <li>RQ1: The RQ1 folder contains the retrieved 1,539 code reviews that discuss code smells. Each review includes four parts: Code Change URL, Code Smell, Code Smell Discussion, and Source Code URL.</li> <li>RQ2: The RQ2 folder contains the coded data for RQ2, called <em>Data Labeling & Encoding for RQ2.mx18</em>. It is the results of data labeling and encoding for RQ2, which was analyzed by the MAXQDA tool.</li> <li>RQ3 and RQ5: <ul> <li><em>Extracted data for RQ3.1.xlsx</em>: this file contains the extracted data (i.e., specific refactoring actions suggested by reviewers) for RQ3.1.</li> <li><em>Data Labeling & Encoding for RQ3 and RQ5.mx18</em>: this file contains the extracted data for RQ3 (excluding the specific refactoring actions in RQ3.1) and RQ5.</li> <li><em>Code change status for RQ5.xlsx</em>: this file contains the information of status of code changes where the developers disagreed with the reviewers and chose to ignore the identified code smells.</li> </ul> </li> <li>RQ4: The RQ4 folder contains the extracted data for RQ4, called <em>Extracted data for RQ4.xlsx</em>.</li> </ul> <p>Note: The mx18 files can be opened by MAXQDA 18 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> <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><em>keyword.txt</em> contains the keywords associated with code smells, such as "smell, duplication, and dead".</li> <li><em>get_changes.py</em> is used for getting code changes from OpenStack and Qt.</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><em>random_select.py</em> is used for randomly selecting review comments that do not contain any keyword.</li> <li><em>keywords_improve.py</em> is used for improving the keyword-based mining approach.</li> <li><em>tools.py</em> is used for supporting the process of keywords improving.</li> </ul>
openstack-24hr
<p>24 hours of logs generated from OpenStack driven by a custom workload generator that used 80 concurrent processes to repeatedly create, boot, suspend/resume, pause/un-pause, and shutdown each VM at a randomized pace. This dataset was first used in the evaluation of "Non-Intrusive Performance Profiling for Entire Software Stacks Based on the Flow Reconstruction Principle."</p>
Code Review for Conformance to Project Coding Style? Revisit using OpenStack Patches
<p>This is a dataset of the paper name "Code Review for Conformance to Project Coding Style? Revisit using OpenStack Patches".</p> <p>It is used for the peer review process in SANER 2021</p>
Dataset of the Paper: Demystifying Code Snippets in Code Reviews: A Study of the OpenStack and Qt Communities and A Practitioner Survey
<p>This dataset, which is composed of two parts: the code review data collected from OpenStack and Qt, and the survey data collected from industrial developers, was used to conduct an empirical study on code snippets in code reviews. A brief description of each part of the dataset is provided below:</p> <p><strong>1. Dataset (OpenStack+Qt).xlsx</strong></p> <p>contains 127,182 review comments mined from four popular projects of the OpenStack community (Nova and Neutron) and the Qt community (Qt Base and Qt Creator) from 2020 to 2021. Among the review comments, 3,197 review comments contain code snippets. In the Excel file, for the rows of review comments with code snippets, we marked the code snippets in red color as an indicator.</p> <p><strong>2. Dataset (Survey).xlsx</strong></p> <p>contains 63 valid responses to our survey questionnaire from industrial developers of OpenStack, Qt, and LinkedIn.</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>
Archival bundle of the data used for "Extending OpenStack Monasca for Predictive Elasticity Control"
<p>This archive contains the data used for the paper</p> <p><strong>Extending OpenStack Monasca for Predictive Elasticity Control</strong><br> <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella</p> <p> </p> <p>Follow the instructions provided in the <a href="https://github.com/giacomolanciano/predictive-elasticity-monasca">companion repo</a> to automatically download and decompress the archive. The following files are included:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td> <p>amphora-x64-haproxy.qcow2</p> </td> <td> <p>Image used to create Octavia amphorae</p> </td> </tr> <tr> <td> <p>distwalk-{lin,aim,mlp,rnn,stc}-<INCREMENTAL-ID>.csv</p> </td> <td> <p>Run traces</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>.log</p> </td> <td> <p>distwalk run log</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-pred.json</p> </td> <td> <p>Predictive metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-real.json</p> </td> <td> <p>Actual metric data exported from Monasca DB</p> </td> </tr> <tr> <td> <p>distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-times.csv</p> </td> <td> <p>Client-side response time for each request sent during a run</p> </td> </tr> <tr> <td> <p>model_dumps/*</p> </td> <td> <p>Dumps of the models and data scalers used for the validation</p> </td> </tr> <tr> <td> <p>predictor.log</p> </td> <td> <p>monasca-predictor log</p> </td> </tr> <tr> <td> <p>predictor-times.log</p> </td> <td> <p>monasca-predictor` log (timing info only)</p> </td> </tr> <tr> <td> <p>predictor-times-{lin,mlp,rnn}.{csv,log}</p> </td> <td> <p>monasca-predictor log (timing info only, group by predictor)</p> </td> </tr> <tr> <td> <p>super_steep_behavior.csv</p> </td> <td> <p>Dataset used to train MLP and RNN models</p> </td> </tr> <tr> <td> <p>test_behavior_02_distwalk-6t_last100.dat</p> </td> <td> <p>distwalk load trace</p> </td> </tr> <tr> <td> <p>ubuntu-20.04-min-distwalk.img</p> </td> <td> <p>Image used to create Nova instances for the scaling group</p> </td> </tr> </tbody> </table> <p>This work extends our <a href="https://doi.org/10.1145/3468737.3494104">previous one</a> appeared at the <em>IEEE/ACM 14th International Conference on Utility and Cloud Computing (UCC'21)</em>. </p> <p>* <em>contact author</em></p>
Amostra de Dados de Logs do OpenStack
<p>Amostra de dados do openstack</p>
Dataset_of_Commercial_Participation_in_OpenStack
<p>This is the dataset of the research about commercial participation in OpenStack.</p>
Injecting Software Faults in Python Applications: The OpenStack Case Study - Supplemental Material (https://rdcu.be/cAPUh)
<p>Injecting Software Faults in Python Applications: The OpenStack Case Study - Supplemental Material<br> Link to the paper: <a href="https://rdcu.be/cAPUh">https://rdcu.be/cAPUh</a></p>
Dataset for the Paper: "Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities"
<p>This is the dataset for the paper: "Security Defect Detection via Code Review: A Study of the OpenStack and Qt Communities ", including the extracted data and results.</p> <p>The dataset contains the following three folders:</p> <p><strong>1. RQ1</strong>: </p> <ul> <li><strong>Security defect in Nova.xlsx</strong></li> <li><strong>Security defect in Neutron.xlsx</strong></li> <li><strong>Security defect in Qt Base.xlsx</strong></li> <li><strong>Security defect in Qt Creator.xlsx;</strong></li> </ul> <p>The RQ1 folder contains four files corresponding to the four projects (i.e., Nova and Neutron from OpenStack, Qt Base and Qt Creator from Qt), including 539 security-related review comments, in which security defects were identified by the reviewers. These instances were obtained from manual labelling after keyword-based search. The security defect type of these instances are presented to answer RQ1.</p> <p><strong>How to Read the MS Excel files in RQ1:</strong></p> <p>Each of the four MS Excel files in this folder contains 6 sheets for six years from 2017 to 2022. Each sheet has 10 columns for recoding 10 data items, among which the last four data items are used in our study to answer the RQs. We list the data items in the following table.</p> <table> <tbody> <tr> <td><strong>Data Item</strong></td> <td><strong>Description</strong></td> <td><strong>Source</strong></td> </tr> <tr> <td>Keyword</td> <td>The corresponding keyword of the comment.</td> <td>Keyword-based Search</td> </tr> <tr> <td>Code_change_id</td> <td>The code_change_id of the comment.</td> <td>Gerrit</td> </tr> <tr> <td>File</td> <td>The file in which the comment is added.</td> <td>Gerrit</td> </tr> <tr> <td>Patchset</td> <td>The patchset of the comment within the code change.</td> <td>Gerrit</td> </tr> <tr> <td>Line</td> <td>The line number in the file at which the comment is added.</td> <td>Gerrit</td> </tr> <tr> <td>Message</td> <td>The text of the review comment.</td> <td>Gerrit</td> </tr> <tr> <td>Security-related</td> <td>Whether the review comment is security-related (i.e., Yes or No).</td> <td>Labelling</td> </tr> <tr> <td>Security defect type</td> <td>The type of the security defect identified in the comment.</td> <td>Labelling</td> </tr> <tr> <td>Consequence</td> <td>The Consequence of the security defect.</td> <td>Extraction</td> </tr> <tr> <td>Resolution Evidence</td> <td>The information about where the identified security defect was resolved in the code</td> <td>Extraction</td> </tr> </tbody> </table> <p><strong>2. RQ2</strong>: </p> <ul> <li><strong>Extracted data for RQ2.mx22</strong></li> </ul> <p>The RQ2 folder contains the extracted data of 539 security-related review comments in <strong>Extracted data for RQ2.mx22</strong>, which was encoded and analyzed by the MAXQDA tool, investigating the treatment of security defects by developers and reviewers to answer RQ2.</p> <p><strong>3. RQ3</strong>: </p> <ul> <li><strong>Extracted data for RQ3.mx22</strong></li> </ul> <p>The RQ3 folder contains the extracted data of 161 review comments in which identified security defects were not resolved by developers in <strong>Extracted data for RQ3.mx22</strong>. which was also encoded and analyzed by the MAXQDA tool, exploring the causes of not resolving security defects to answer RQ3.</p> <p><strong>Note</strong>: The mx22 can be opened by MAXQDA 22, which are available at <a href="https://www.maxqda.com/">https://www.maxqda.com/</a> for download. You may also use the free trial version of MAXQDA 2022, which is available at <a href="https://www.maxqda.com/trial">https://www.maxqda.com/trial</a> for download.</p>
Commercial_Participation_in_OpenStack
<p>This is the dataset and code of the research of commercial participation in OpenStack</p>
Dataset_CommercialParticipation_OpenStack
<p>The dataset of the research about commercial participation in OpenStack</p>
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OpenNeuro
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