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25 results for “technical replicates”
Replication package for "An Empirical Assessment of Best-Answer Prediction Models in Technical Q&A Sites" (EMSE 2018)
<p>Replication package for the paper:</p> <blockquote> <p>F. Calefato, F. Lanubile, and N. Novielli (2018) “<a href="http://collab.di.uniba.it/fabio/wp-content/uploads/sites/5/2018/07/EMSE-D-17-00159_R3.compressed.pdf">An Empirical Assessment of Best-Answer Prediction Models in Technical Q&A Sites</a>.” Empirical Software Engineering Journal, DOI: <a href="http://dx.doi.org/10.1007/s10664-018-9642-5">10.1007/s10664-018-9642-5</a>.</p> </blockquote>
Replication package for technical lag analysis for the JSEP journal.
<p>This is the replication package for our article "A Formal Framework for Measuring Technical Lag in Component Repositories --- and its Application to npm" submitted for the JSEP journal in 2018.</p> <p>This replication package requires Python 3.5+ to be installed, and all the dependencies listed in ``requirements.txt``.<br> They can be automatically installed using ``pip install -r requirements.txt``. <br> These experiment were executed on a Linux Ubuntu OS.</p> <p>To obtain the analysis used in the paper, one should execute ``jupyter notebook`` at the root of this replication package, and open the notebook contained in ``notebooks``.</p> <p>This replication package contains three folders (i.e scripts, notebooks and data), each folder has a README with a description of what it contains.</p> <p>The list of all npm package releases and Github repositories with their dependencies was download from the last available dataset of libraries.io: https://zenodo.org/record/1196312</p> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license.<br> The source code is under the GNU General Public License.</p> <p><br> For any more information about the details of these experiments, please contact: <strong><a href="mailto:ahmed.zerouali73@gmail.com">ahmed.zerouali73@gmail.com</a></strong></p>
Dataset and replication information for Temporal Discounting in Technical Debt: How do Software Practitioners Discount the Future?
<p>Dataset and replication information for the paper Temporal Discounting in Technical Debt: How do Software Practitioners Discount the Future? (Becker, C., Fagerholm, F., Mohanani, R., Chatzigeorgiou, A., 2009). The dataset consists of answers to a questionnaire on temporal discounting in a technical debt context. The respondents are from two companies. The raw data from the two companies is given separately. Information showing how to replicate the calculations required for analysis is given for each company. The dataset and replication information are given in both Excel (xlsx) and Open Document (ods) formats.</p>
Replication package for the paper: "Technical Debt's State of Practice on Stack Overflow: a Preliminary Study"
<p>This is the replication package for the paper "Technical Debt’s State of Practice on Stack Overflow: a Preliminary Study", published (in Portuguese) in the preliminary results track of SBQS, the Brazilian Symposium on Software Quality.</p> <p> </p> <p>We provide the data for all steps of our methodology and final analysis. Each file is numbered, indicating the order in which they were produced in our study. </p>
Replication package for the paper: "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions"
<p>This is the replication package for the article "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions". The article was published in the Research Track of the third Brazilian Workshop on Intelligent Software Engineering (ISE'23).</p> <p>The replication package consists of 8 files:<br> 1) dataset.csv, 2) code_anayses.ipynb and 3) example_test_balanced.csv and the others are results of word cloud generation.</p> <p>In dataset.csv, we provide the data for future replications.</p> <p>In code_anayses.ipynb, we provide the code we use to arrive at the results.</p> <p>In example_test_balanced.csv, we provide an example input dataset for training the models.</p> <p>For future references in this article, please contact lead author Eliakim Gama, or one of the co-authors.</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>
Replication Package of Deep Learning and Data Augmentation for Detecting Self-Admitted Technical Debt
<p>Self-Admitted Technical Debt (SATD) refers to circumstances where developers use code comments, issues, pull requests, or other textual artifacts to explain why the existing implementation is not optimal. Past research in detecting SATD has focused on either identifying SATD (classifying SATD instances as SATD or not) or categorizing SATD (labeling instances as SATD that pertain to requirements, design, code, test, etc.). However, the performance of such approaches remains suboptimal, particularly when dealing with specific types of SATD, such as test and requirement debt. This is mostly because the used datasets are extremely imbalanced.</p> <p>In this study, we utilize a data augmentation strategy to address the problem of imbalanced data. We also employ a two-step approach to identify and categorize SATD on various datasets derived from different artifacts. Based on earlier research, a deep learning architecture called BiLSTM is utilized for the binary identification of SATD. The BERT architecture is then utilized to categorize different types of SATD. We provide the dataset of balanced classes as a contribution for future SATD researchers, and we also show that the performance of SATD identification and categorization using deep learning and our two-step approach is significantly better than baseline approaches.</p> <p>Therefore, to showcase the effectiveness of our approach, we compared it against several existing approaches:</p> <ol> <li>Natural Language Processing (NLP) and Matches task Annotation Tags (MAT) [<a href="https://github.com/Naplues/MAT" target="_blank" rel="noopener">Github</a>]</li> <li>eXtreme Gradient Boosting+Synthetic Minority Oversampling Technique (XGBoost+SMOTE) [<a href="https://figshare.com/s/87a4b5002c7488822e60" target="_blank" rel="noopener">Figshare</a>]</li> <li>eXtreme Gradient Boosting+Easy Data Augmentation (XGBoost+EDA) [<a href="https://github.com/shenyuanduanzui/xgboost_satd" target="_blank" rel="noopener">Github</a>]</li> <li>MT-Text-CNN [<a href="https://github.com/yikun-li/satd-different-sources-data" target="_blank" rel="noopener">Github</a>]</li> </ol> <p> </p> <div> <div><strong>Structure of the Replication Package:</strong></div> </div> <p>In accordance with the original dataset, the dataset comprises four distinct CSV files delineated by the artifacts under consideration in this study. Each CSV file encompasses a text column and a class, which indicate classifications denoting specific types of SATD, namely code/design debt (C/D), documentation debt (DOC), test debt (TES), and requirement debt (REQ) or Not-SATD.</p> <div> <div><code>├── SATD Keywords</code></div> <div><code>│ ├── Keywords based on Source of Artifacts</code></div> <div><code>│ │ ├── Code comment.txt</code></div> <div><code>│ │ ├── Commit message.txt</code></div> <div><code>│ │ ├── Issue section.txt</code></div> <div><code>│ │ └── Pull section.txt</code></div> <div><code>│ ├── Keywords based on Types of SATD</code></div> <div><code>│ │ ├── code-design debt.txt</code></div> <div><code>│ │ ├── documentation debt.txt</code></div> <div><code>│ │ ├── requirement debt.txt</code></div> <div><code>│ │ └── test debt.txt</code></div> <div><code>├── src</code></div> <div><code>│ ├── bert.py</code></div> <div><code>│ ├── bilstm.py</code></div> <div><code>│ └── preprocessing.py</code></div> <div><code>├── data-augmentation-code_comments.csv</code></div> <div><code>├── data-augmentation-commit_messages.csv</code></div> <div><code>├── data-augmentation-issues.csv</code></div> <div><code>├── data-augmentation-pull_requests.csv</code></div> <div> <div><code>└── Supplementary Material.docx</code></div> <div> </div> </div> <div> </div> </div> <p><strong>Requirements:</strong></p> <div> <div><a href="https://nlp.stanford.edu/projects/glove/" target="_blank" rel="noopener">glove</a></div> <div>nltk</div> <div>transformers</div> <div>torch</div> <div>tensorflow</div> <div>keras</div> <div>langdetect</div> <div>inflect</div> <div>inflection</div> </div> <div> </div> <div> </div> <div> <div><strong>Project sources for each artifact are as follows:</strong></div> </div> <div> </div> <div> <table> <tbody> <tr> <td><strong>Source code comment</strong></td> <td><strong>Issue section</strong></td> <td><strong>Pull section</strong></td> <td><strong>Commit message</strong></td> </tr> <tr> <td>ant<br>argouml<br>columba<br>emf<br>hibernate<br>jedit<br>jfreechart<br>jmeter<br>jruby<br>squirrel</td> <td> <div>camel</div> <div>chromium</div> <div>gerrit</div> <div>hadoop</div> <div>hbase</div> <div>impala</div> <div>thrift</div> </td> <td> <div>accumulo</div> <div>activemq</div> <div>activemq-artemis</div> <div>airflow</div> <div>ambari</div> <div>apisix</div> <div>apisix-dashboard</div> <div>arrow</div> <div>attic-apex-core</div> <div>attic-apex-malhar</div> <div>attic-stratos</div> <div>avro</div> <div>beam</div> <div>bigtop</div> <div>bookkeeper</div> <div>brooklyn-server</div> <div>calcite</div> <div>camel</div> <div>camel-k</div> <div>camel-quarkus</div> <div>camel-website</div> <div>carbondata</div> <div>cassandra</div> <div>cloudstack</div> <div>commons-lang</div> <div>couchdb</div> <div>cxf</div> <div>daffodil</div> <div>drill</div> <div>druid</div> <div>dubbo</div> <div>echarts</div> <div>fineract</div> <div>flink</div> <div>fluo</div> <div>geode</div> <div>geode-native</div> <div>gobblin</div> <div>griffin</div> <div>groovy</div> <div>guacamole-client</div> <div>hadoop</div> <div>hawq</div> <div>hbase</div> <div>helix</div> <div>hive</div> <div>hudi</div> <div>iceberg</div> <div>ignite</div> <div>incubator-brooklyn</div> <div>incubator-dolphinscheduler</div> <div>incubator-doris</div> <div>incubator-heron</div> <div>incubator-hop</div> <div>incubator-mxnet</div> <div>incubator-pagespeed-ngx</div> <div>incubator-pinot</div> <div>incubator-weex</div> <div>infrastructure-puppet</div> <div>jena</div> <div>jmeter</div> <div>kafka</div> <div>karaf</div> <div>kylin</div> <div>lucene-solr</div> <div>madlib</div> <div>myfaces-tobago</div> <div>netbeans</div> <div>netbeans-website</div> <div>nifi</div> <div>nifi-minifi-cpp</div> <div>nutch</div> <div>openwhisk</div> <div>openwhisk-wskdeploy</div> <div>orc</div> <div>ozone</div> <div>parquet-mr</div> <div>phoenix</div> <div>pulsar</div> <div>qpid-dispatch</div> <div>reef</div> <div>rocketmq</div> <div>samza</div> <div>servicecomb-java-chassis</div> <div>shardingsphere</div> <div>shardingsphere-elasticjob</div> <div>skywalking</div> <div>spark</div> <div>storm</div> <div>streams</div> <div>superset</div> <div>systemds</div> <div>tajo</div> <div>thrift</div> <div>tinkerpop</div> <div>tomee</div> <div>trafficcontrol</div> <div>trafficserver</div> <div>trafodion</div> <div>tvm</div> <div>usergrid</div> <div>zeppelin</div> <div>zookeeper</div> </td> <td> <div>accumulo</div> <div>activemq</div> <div>activemq-artemis</div> <div>airflow</div> <div>ambari</div> <div>apisix</div> <div>apisix-dashboard</div> <div>arrow</div> <div>attic-apex-core</div> <div>attic-apex-malhar</div> <div>attic-stratos</div> <div>avro</div> <div>beam</div> <div>bigtop</div> <div>bookkeeper</div> <div>brooklyn-server</div> <div>calcite</div> <div>camel</div> <div>camel-k</div> <div>camel-quarkus</div> <div>camel-website</div> <div>carbondata</div> <div>cassandra</div> <div>cloudstack</div> <div>commons-lang</div> <div>couchdb</div> <div>cxf</div> <div>daffodil</div> <div>drill</div> <div>druid</div> <div>dubbo</div> <div>echarts</div> <div>fineract</div> <div>flink</div> <div>fluo</div> <div>geode</div> <div>geode-native</div> <div>gobblin</div> <div>griffin</div> <div>groovy</div> <div>guacamole-client</div> <div>hadoop</div> <div>hawq</div> <div>hbase</div> <div>helix</div> <div>hive</div> <div>hudi</div> <div>iceberg</div> <div>ignite</div> <div>incubator-brooklyn</div> <div>incubator-dolphinscheduler</div> <div>incubator-doris</div> <div>incubator-heron</div> <div>incubator-hop</div> <div>incubator-mxnet</div> <div>incubator-pagespeed-ngx</div> <div>incubator-pinot</div> <div>incubator-weex</div> <div>infrastructure-puppet</div> <div>jena</div> <div>jmeter</div> <div>kafka</div> <div>karaf</div> <div>kylin</div> <div>lucene-solr</div> <div>madlib</div> <div>myfaces-tobago</div> <div>netbeans</div> <div>netbeans-website</div> <div>nifi</div> <div>nifi-minifi-cpp</div> <div>nutch</div> <div>openwhisk</div> <div>openwhisk-wskdeploy</div> <div>orc</div> <div>ozone</div> <div>parquet-mr</div> <div>phoenix</div> <div>pulsar</div> <div>qpid-dispatch</div> <div>reef</div> <div>rocketmq</div> <div>samza</div> <div>servicecomb-java-chassis</div> <div>shardingsphere</div> <div>shardingsphere-elasticjob</div> <div>skywalking</div> <div>spark</div> <div>storm</div> <div>streams</div> <div>superset</div> <div>systemds</div> <div>tajo</div> <div>thrift</div> <div>tinkerpop</div> <div>tomee</div> <div>trafficcontrol</div> <div>trafficserver</div> <div>trafodion</div> <div>tvm</div> <div>usergrid</div> <div>zeppelin</div> <div>zookeeper</div> </td> </tr> </tbody> </table> </div> <div> <div> </div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <p>This dataset has undergone a data augmentation process using the <a href="https://arxiv.org/abs/2302.13007" target="_blank" rel="noopener">AugGPT</a> technique. Meanwhile, the original dataset can be downloaded via the following link: <a href="https://github.com/yikun-li/satd-different-sources-data">https://github.com/yikun-li/satd-different-sources-data</a></p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div> </div> </div> </div> </div> </div> </div>
Replication Package of the paper: "A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems"
<p># README<br>## _Replication Package for A Taxonomy of Self-Admitted Technical Debt in Deep Learning Systems_</p> <p>This package contains several files and a folder with both code and data used within the context of the study. </p> <p>## output_StaticAnalysis.csv<br>This file contains the results of the manual annotation aimed at verifying whether a static code analysis tool can be used to pinpoint the presence of SATD and, more specifically, DL-SATD. For each SATD instance, you can find a list of warnings impacting the area of the code where the SATD is, as well as the outcome of our manual annotation. </p> <p>## dependentsTensorflow.csv<br>This file contains the list of dependents of the TensorFlow DL framework together with the number of stars and the number of forks. </p> <p>## dependentsTorch.csv<br>This file contains the list of dependents of the PyTorch DL framework, together with the number of stars and the number of forks.</p> <p>## 100Projects.csv<br>This file contains the list of 100 open-source Python projects importing at least one among Tensorflow or PyTorch that we have used as the initial set for our study. </p> <p>## SATDTensorFlow.csv<br>This file contains the list of SATD detected for the 50 Python open-source projects relying on TensorFlow. Each line contains the name of the project, the path to the file in which the SATD has been detected, the line in the file where the SATD comment starts, and the SATD comment. </p> <p>## SATDTorch.csv<br>This file contains the list of SATD detected for the 50 Python open-source projects relying on PyTorch. Each line contains the name of the project, the path to the file in which the SATD has been detected, the line in the file where the SATD comment starts, and the SATD comment.</p> <p>## SampledSATD.csv<br>This file contains the list of the 443 SATD comments used to determine the DL-specific SATD Taxonomy. Each line contains the link to the SATD, followed by the SATD comment body, and two identifiers used to determine the context of the SATD comment (used to properly select the presence of static code analysis tools warnings). </p> <p>## FinalValidation.csv<br>This file contains the outcome of the manual validation of the 443 SATD in our sample. </p> <p>## MappingWithHumbatovaEtAl.xls<br>This file contains the outcome of the mapping between our DL-specific SATD taxonomy and the taxonomy of DL-bugs by Humbatova et al. </p> <p>## extractSATDComments.py<br>This file contains the source code used to analyze the 100 projects in our study. Specifically, for each project and each Python file within it, the script checks whether the file imports one of the two DL frameworks used in the context of the study. If this is the case, it extracts all comments within it and re-implements the KL-SATD to check whether the comment is a SATD candidate. </p> <p>## runAscat.py<br>This file contains the source code used to run Prospector as an aggregator of static code analysis tools for Python. </p> <p>## CompleteStaticAnalysisWarnings<br>This directory contains the outputs of Prospector (one for each studied project, in JSON format)</p>
Opportunities and Security Risks of Technical Leverage: A Replication Study on the NPM Ecosystem
<p>Metadata, technical leverage, and vulnerability reports for 14,042 stable releases and scripts used in "Opportunities and Security Risks of Technical Leverage: A Replication Study on the NPM Ecosystem" paper.</p> <p> </p> <p> </p>
Replication Package for Identifying Self-Admitted Technical Debt in Issue Tracking Systems using Machine Learning
<p>This dataset includes pre-trained word embeddings and a weighted file that can be used to identify self-admitted technical debt (SATD) from issue tracking systems.</p>
VulSATDˆ{DS}: a dataset for vulnerabilities and self-admitted technical debt and its replication package
<p>VulSATDˆ{DS} is a dataset of functions in C/C++ extracted from 12 open-source projects and labelled for the existence of self-admitted technical debt (SATD) and vulnerability. It was presented in the paper "VulSATDˆ{DS}: a dataset for vulnerabilities and self-admitted technical debt." This upload contains both the dataset (file: VulSATDDS.zip) and the replication package that allows the recreation of the dataset from its sources (replication.zip).</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] The Dual-Edged Sword of Technical Debt: Benefits and Issues Analyzed Through Developer Discussions
<p>This is the replication package for the submission with the title "The Dual-Edged Sword of Technical Debt: Benefits and Issues Analyzed Through Developer Discussions". This package includes the following artifacts.</p> <ul> <li>Original dataset</li> <li>Preprocessed dataset</li> <li>Source code for obtaining the article results</li> <li>Topic models</li> </ul>
Replication package for: Energy Efficiency and Directed Technical Change: Implications for Climate Change Mitigation
<p>Contains raw data and replication code for the following paper:</p> <p>Casey, Gregory (forthcoming). "Energy Efficiency and Directed Technical Change: Implications for Climate Change Mitigation." Review of Economic Studies.</p>
Replication Package for Automatically Identifying Relations Between Self-Admitted Technical Debt Across Different Sources
<p>This dataset includes a trained weight file for identifying the relations between self-admitted technical debt items.</p>
Replication Package for Automating Technical Debt Management: Insights from Practitioner Discussions in Stack Exchange
Open the record for dataset details and reuse information.
Supplementary material 2 from: Van den Bulcke L, De Backer A, Ampe B, Maes S, Wittoeck J, Waegeman W, Hostens K, Derycke S (2021) Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection. Metabarcoding and Metagenomics 5: e71107. https://doi.org/10.3897/mbmg.5.71107
Tables S1–S8
Supplementary material 1 from: Van den Bulcke L, De Backer A, Ampe B, Maes S, Wittoeck J, Waegeman W, Hostens K, Derycke S (2021) Towards harmonization of DNA metabarcoding for monitoring marine macrobenthos: the effect of technical replicates and pooled DNA extractions on species detection. Metabarcoding and Metagenomics 5: e71107. https://doi.org/10.3897/mbmg.5.71107
Figures S1–S14
DNA methylation profiles of liver and placenta technical replicates
GEO Series GSE52731. Homo sapiens. 126 samples. Type: Methylation profiling by genome tiling array.
Epigenetic clonality confers por prognosis in colorrectal cancer patients (technical replicates)
GEO Series GSE81359. Homo sapiens. 12 samples. Type: Methylation profiling by genome tiling array.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.