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113 results for “debt”
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>
Dataset of the paper "Self-Admitted Technical Debt Practices: A Comparison Between Industry and Open-Source"
<p>This repository contains the dataset of the manuscript "Self-Admitted Technical Debt Practices: A Comparison Between Industry and Open-Source" accepted in the Empirical Software Engineering Journal, edited by Springer</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>
Data from: Carbon debt of field-scale Conservation Reserve Program grasslands converted to annual and perennial bioenergy crops
Open the record for dataset details and reuse information.
"ATDx: Building an Architectural Technical Debt Index" companion dataset
<p>Companion dataset of the study "ATDx: Building an Architectural Technical Debt Index".</p> <p>The dataset contains the totality of the raw data mined via <a href="https://sonarcloud.io/">SonarCloud</a> for the viability investigation of the study.</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>
Characterizing Technical Debt and Antipatterns in AI-Based Systems: A Systematic Mapping Study
<p>All artifacts related to a systematic mapping study on technical debt and antipatterns in AI-based systems; the data consists of an Excel file (00-all-data.xlsx) which includes a tab for all important constructs plus a separate CSV file per construct:</p> <ul> <li>List of primary studies (01-primary-studies.csv)</li> <li>Established types of technical debt (02-established-td-types.csv)</li> <li>New types of technical debt (03-new-td-types.csv)</li> <li>Affected software quality attributes (04-affected-qas.csv)</li> <li>Identified antipatterns (05-antipatterns.csv)</li> <li>Reported solutions to address technical debt or antipatterns (06-solutions.csv)</li> </ul> <p> </p>
Dataset: A continuous open source data collection platform for architectural technical debt assessment
<p>The dataset and replication package of the study "A continuous open source data collection platform for architectural technical debt assessment".</p> <p> </p> <p>Abstract</p> <p>Architectural decisions are the most important source of technical debt. In recent years, researchers spent an increasing amount of effort investigating this specific category of technical debt, with quantitative methods, and in particular static analysis, being the most common approach to investigate such a topic.</p> <p> </p> <p>However, quantitative studies are susceptible, to varying degrees, to external validity threats, which hinder the generalisation of their findings.</p> <p>In response to this concern, researchers strive to expand the scope of their study by incorporating a larger number of projects into their analyses. This practice is typically executed on a case-by-case basis, necessitating substantial data collection efforts that have to be repeated for each new study.</p> <p> </p> <p>To address this issue, this paper presents our initial attempt at tackling this problem and enabling researchers to study architectural smells at large scale, a well-known indicator of architectural technical debt. Specifically, we introduce a novel approach to data collection pipeline that leverages Apache Airflow to continuously generate up-to-date, large-scale datasets using Arcan, a tool for architectural smells detection (or any other tool).</p> <p>Finally, we present the publicly-available dataset resulting from the first three months of execution of the pipeline, that includes over 30,000 analysed commits and releases from over 10,000 open source GitHub projects written in 5 different programming languages and amounting to over a billion of lines of code analysed.</p>
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>
Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange
<p>Dataset used in the work 'Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange'.</p> <p>Authors not presented due to double blind review.</p>
Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange
<p>Dataset used in the work 'Investigating the Point of View of Project Management Practitioners on Technical Debt - A Preliminary Study on Stack Exchange'.</p>
Al-Ṭabarī's Unacknowledged Debt
<p><strong>Al-Ṭabarī’s Unacknowledged Debt</strong></p> <p>This release contains the below list of data files for “Al-Ṭabarī’s Unacknowledged Debt” and future publications on these and related data. </p> <ol> <li> <p>Alignments.csv: the KITAB project did a <a href="https://github.com/dasmiq/passim">passim</a> evaluation exercise in May-June 2021 that involved team members correcting alignments generated through the software. The alignments between the <em>Kitāb Baghdād</em> by Ibn Abī Ṭāhir and the <em>Taʾrīkh al-rusul wa’l-mulūk</em> by al-Ṭabarī were corrected by Sarah Bowen Savant. The alignments are in CSV format (separated by tab). The alignments file is in CSV format in which the columns are separated by Tab. Fields in the file:</p> <ul> <li> <p>book1: <em>Kitāb Baghdād</em>, OpenITI book id</p> </li> <li> <p>book2:<em>Taʾrīkh</em>, OpenITI book id</p> </li> <li> <p>id: of the alignment</p> </li> <li> <p>seqIAT: Because it is book 1, alignments same as id</p> </li> <li> <p>seqTabari: The sequence of the alignment ids as they occur in the <em>Taʾrīkh</em></p> </li> <li> <p>bw1: word/token position starts, for book1 </p> </li> <li> <p>ew1: word/token position ends, for book1</p> </li> <li> <p>bw2: word/token position starts, for book2</p> </li> <li> <p>ew2: word/token position ends, for book2</p> </li> <li> <p>text1: text aligned from book1</p> </li> <li> <p>text2: text aligned from book2</p> </li> <li> <p>LeidenEd: the pages from Leiden ed.: al-Ṭabarī, <em>Taʾrīkh al-rusul wa’l-mulūk, </em>ed. M. J. de Goeje et al., 15 vols. in 3 series, Leiden: E. J. Brill, 1879–1901. Series 3, vol. 1.</p> </li> <li> <p>BosworthTrans: the pages from Bosworth trans.: al-Ṭabarī, <em>The History of al-Ṭabarī, </em>vol. 32: <em>The Reunification of the ʿAbbāsid Caliphate (A.D. 813-833/A.H. 198-218),</em> trans. C.E. Bosworth, Albany: State University of New York Press, 1987. </p> </li> </ul> </li> <li> <p>TransmissiveTerms.csv: a list of the transmissive terms with which we searched the <em>Taʾrīkh</em> to determine how often al-Ṭabarī cites his sources. The file is in CSV format, using Tab as the column separator.</p> </li> <li> <p>Related texts and the corresponding YML files from the <a href="https://zenodo.org/record/4513723">Version 2021.1.4</a> and <a href="https://zenodo.org/record/5550338">Version 2021.2.5</a> releases of the <a href="https://doi.org/10.5281/zenodo.3082463">OpenITI corpus</a>.</p> </li> <li> <p>A sample Python script that generates the graph with the current dataset and can be used to generate similar graphs for other datasets.</p> </li> <li> <p>The citation graph.</p> </li> <li> <p>The release notes.</p> </li> </ol> <p> </p> <p> </p>
Artifact for ESEC/FSE Paper: "23 Shades of Self-Admitted Technical Debt: An Empirical Study on Machine Learning Software"
<p>Artifact for ESEC/FSE paper entitled "23 Shades of Self-Admitted Technical Debt: An Empirical Study on Machine Learning Software"</p>
Agile Social Software Engineer: An Empirical Approach to Mitigate and Manage Social Debts in Agile Environments
<p>Replication package description:<br> "DataCollection.xlsx": Survey responses from Agile teams in weeks 1, 2, 3, 4 and 5.<br> <br> "DataAnalysis.xlsx": Analysis of the answers from the survey to answer the research questions proposed in this work.<br> Team-working issues that could lead to community smells can be found in TeamworkingIssues Tab.<br> The root causes of community smells in agile teams over weeks can be found in Rootcauses Tab.<br> Requests for Agile social guardian intervention can be found in Requestagileteams Tab.<br> Recurring needs and concerns in agile teams can be found in Needs_Concerns Tab.</p>
CHRONOLOGICAL APPROACH TO THE CHARACTERISTICS OF ECUADOR'S DEBT WITH THE IMF AND THE WB.
<p><a name="_Hlk162621365"></a><span>Where did Ecuador's foreign debt originate and grow to alarming levels? </span><span>The purpose of this article is to describe, from an analytical perspective based on the contributions of various authors, the origin of Ecuador's external debt with international institutions such as the World Bank and the International Monetary Fund, as well as the support that these organizations provide for the development of the countries of the South, case Ecuador. To achieve this purpose, a documentary methodology was implemented that included an exhaustive review of studies focused on the specific variables subject to analysis, the PRISMA tool and Atlas.ti were used. The results show that the creation of these financing agencies in their attempt to support the financial stability of developing countries has not been sufficiently effective in improving the welfare conditions of a good percentage of the population and has generated an imbalance in the sustained development of the poorest countries. It is concluded that the capitalist system gives predominant priority to the economic growth of nations, thus relegating in its model the consideration of human development. This orientation does not contribute effectively to the improvement of crucial aspects such: education, health and the standard of living of the population.</span></p>
Water Debt
<p>Gridded data of water debt for year 2000. The water debt expresses the number of years required by the hydrological cycle to replenish the water source (soil moisture, surface water, ground water) that have beet used to accomplish the annual production of nine major crops. The considered crops are: wheat, maize, rice, soybean, sugar cane, sugar beet, cotton, barley, sorghum.</p> <p> </p>
THE EFFECT OF CURRENT RATIO, RETURN ON EQUITY, AND DEBT TO EQUITY RATIO ON STOCK RETURNS ON THE INDONESIA STOCK EXCHANGE
<p>This research examined the effect of the Current Ratio, Return on Equity, and Debt to Equity Ratio on Stock Returns in Kompas 100 Companies Listed on the Indonesia Stock Exchange in the 2015-2019 period. The sampling technique used in this study was purposive sampling. The samples obtained were 30 companies from 100 companies. The type of data used in this research is secondary data, and the method of analysis uses multiple linear regression analysis. Based on the data analysis done, the variable Return on Equity has a positive and significant effect on stock returns. The Current Ratio has a negative and insignificant effect on stock returns, and the Debt to Equity Ratio has a negative and insignificant effect on stock returns. The current ratio, return on equity, and debt to equity ratio simultaneously does not affect stock returns.</p>
On the relation between technical debt indicators and quality criteria in Stack Overflow discussions
<p>This is the replication package for the paper "On the relation between technical debt indicators and quality criteria in Stack Overflow discussions". The paper was published on the Technical Research Track of the Bralizian Symposium on Software Engineering.This is the replication package for the paper "On the relation between technical debt indicators and quality criteria in Stack Overflow discussions". The paper was published on the Technical Research Track of the Bralizian Symposium on Software Engineering.</p>
A Large-scale Benchmark for Technical Debt Assessment
<p>This repository hosts the supplementary material of the paper entitled "A Large-scale Benchmark for Technical Debt Assessment". It comprises a very large-scale study on the Technical Debt of more than 55 million open-source Java files.</p>
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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.