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113 results for “debt”
Data for Investigating the Technical Debt in Procedural Model Transformation Languages
<p>The content presents the data for Investigating the Technical Debt in Procedural Model Transformation Languages </p>
Replication package for the paper: "Using Stack Overflow to Assess Technical Debt Identification on Software Projects".
<p>This is the replication package for the paper "Using Stack Overflow to Assess Technical Debt Identification on Software Projects". The paper was published on the Technical Research Track of the Bralizian Symposium on Software Engineering. </p> <p> </p> <p>The replication package is composed of 3 files: 1) data.zip, 2) tables.zip, and 3) clarifications.zip</p> <p> </p> <p>In data.zip, we provide the outputs of our methodological procedure to select the discussions from StackOverflow to include in our empirical analysis. </p> <p>In tables.zip, we provide additional and detailed data regarding all tables included in the paper/</p> <p>In clarifications.zip, we provide additional clarification regarding concepts we did not fully discuss in the manuscript. </p> <p> </p> <p>For future references in this paper, please contact the main author Eliakim Gama, or one of the co-authors.</p>
Anticipating Identification of Technical Debt Items in Model-Driven Software Projects
<p>Model-driven development (MDD) and Technical Debt (TD) are software engineering approaches that look for promoting quality on systems under development. MDD uses high-level abstraction models that can be transformed into application code, potentially improving system understanding and maintainability. TD, on the other hand, promotes quality through the use of strategies for detecting, quantifying, monitoring, and correcting software development problems that may hinder its maintenance and evolution. Most research on TD focuses on the application code as primary TD sources. In an MDD project, however, dealing with technical debt only on the source code may not be an adequate strategy because MDD projects should focus their software building efforts on models instead of source code. Besides, in MDD projects, code generation is often done at a later stage than creating models, then dealing with TD only in source code can lead to unnecessary interest payments due to unmanaged debts, such as model and source codes artifacts desynchronization. The use of TD concept in an MDD context is also known as Model-Driven Technical Debt (MDTD). Recent works concluded that MDD project codes are not technical debt free, making it necessary to investigate the possibility and benefits of applying TD identification techniques in earlier stages of the development process, such as in modeling phases. This paper intends to analyze whether it is possible to use source code technical debt detection strategies to identify TD on code-generating models in the context of model-driven development projects. A catalog of nine different model technical debt items for platform-independent code-generating models was specified. Each catalog item provides a detection strategy to automatically find elements suspected to incur the corresponding TD type in the models. An evaluation was performed in order to observe the effectiveness of the proposed catalog compared to existing source code identification techniques found in the literature. Through three different open source software projects, more than 78 thousand lines of code were investigated. Results revealed that, although the catalog items present different precision rates, it is possible to identify and deal with these model-driven technical debts before source code is generated. We hope that sharing this initial version provides future contributions and improvements for this catalog.</p>
Public debt and economic growth in Spain, 1851–2013
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Testing explosive bubbles with time-varying volatility: The case of Spanish public debt
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The Euro Area Sovereign Debt Crisis: Can Contagion Spread from the Periphery to the Core?
<p><strong>Data and codes:</strong></p> <p>"The Euro Area Sovereign Debt Crisis: Can Contagion Spread from the Periphery to the Core?" by Denis Gorea and Deyan Radev</p> <p> </p> <p>The Stata codes that generate the regression results are stored in the archive <em>'stata_for_website.zip'</em> (below, including the final dataset). Most of the initial data used in regressions is proprietary (IMF, BIS). Hence, we cannot republish it. However, we attach our final dataset that includes only variables on the country-pair level and has no identifying information on each of the countries in a particular pair. For details regarding the ways these variables were constructed, please see the appropriate section in our paper.</p> <p>The Matlab codes generating the Joint Probability of Default (JPoD) and the figures in our paper are also available below. Since CDS data series are not publicly available, we can't post our original data here. Please refer to the paper for a detailed description of sources for the CDS and bond yield data.</p> <p>Also, please check a simple Python code computing the JPoD for some random parameters.</p> <p>Please email the authors if you spot any errors.</p>
Self-Admitted Technical Debt in Scientific Software
<p># Title<br>Self-Admitted Technical Debt (SATD) in Scientific Software Projects</p> <p># Description<br>## Abstract<br>This dataset contains annotated code comments from nine open-source scientific software projects: Astropy, Biopython, CESM, Firedrake, MOOSE, GROMACS, Elmer, Athena, and Root. The comments are labeled to identify instances of Self-Admitted Technical Debt (SATD), with a focus on a novel category termed Scientific Debt (SD). The dataset supports research on the nature and management of technical debt in scientific software.</p> <p>## Purpose<br>The dataset was created to explore the prevalence and characteristics of SATD in scientific software, with the aim of improving software maintainability and scientific validity.</p> <p>## Content<br>The dataset includes over 28,680 annotated code comments, with labels indicating various types of technical debt such as Code Debt, Design Debt, and Scientific Debt. Each comment is accompanied by metadata including the project name, file path, comment introduction date, and comment removal date.</p> <p>## Scope<br>The dataset covers nine projects across different scientific domains, including astronomy, molecular biology, and climate modeling. Data was collected from publicly available repositories and spans from the inception of each project to the present.</p> <p>## Methodology<br>Data was extracted using GitPython to access the version control histories of the selected projects. Comments were manually labeled for SATD, with a focus on identifying Scientific Debt indicators such as assumptions, missing edge cases, computational inaccuracies, translation challenges, and new scientific findings.</p> <p>## Usage Notes<br>This dataset can be used for research on technical debt management, software maintenance, and scientific software development. Users should have a basic understanding of programming and version control systems. Recommended tools for analysis include Python and Pandas.</p> <p>## Ethical Considerations<br>All data was collected from publicly available sources. No personal or sensitive information is included.</p> <p># Technical Details<br>## File Formats<br>- CSV: Contains the annotated comments and metadata</p> <p>## Size<br>- Number of records: 28,680<br>- Total file size: 15MB</p> <p>## Version<br>- Version 1.0, July 2024</p> <p># Access and Use<br>## Access<br>The dataset can be downloaded from Zenodo: [Zenodo Link](https://doi.org/10.5281/zenodo.13174322)</p> <p>## License<br>This dataset is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).</p>
Impact of a Sleep Debt in Middle-Aged and Older Adults
ClinicalTrials.gov study NCT00817700. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Using Stack Overflow to Assess Technical Debt Identification on Software Projects SBES2020 - Eliakim Gama
<p>Vídeo para backup da apresentação SBES 2020.</p>
John Lachester's « Debt to Pleasure »
<u>Source</u>: Flickr <br><u>4DCity URL</u>: <a href="https://4dcity.org/imgupload/1677443285.1091.jpg">https://4dcity.org/imgupload/1677443285.1091.jpg</a> <br><u>Original Image URL</u>: <a href="https://live.staticflickr.com/65535/49538999713_60f55d393c_m.jpg">https://live.staticflickr.com/65535/49538999713_60f55d393c_m.jpg</a>
Additional Material for The Technical Debt Gamble: A Case Study on Technical Debt in a Large-Scale Industrial Microservice Architecture
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Self-Admitted Technical Debt in Commit Messages: Comparing Java, Python, and R
<p><strong><span>The folder organization and datasets within each are as follows:</span></strong></p> <p><strong><span>Collection Folder:</span></strong><span> the original dataset that we scraped is placed. We have removed the user names and email addresses to keep the users’ privacy. </span><strong><span>RQ1 Folder</span></strong><span> has three subfolders: </span></p> <p><span><span>❖<span> </span></span></span><strong><span>Manual Training:</span></strong><span> The initial manually labeled data we used to initially train the classifiers is included. Note that columns A-O in this dataset are all extracted from GitHub’s API. Column O (heading “message”) is the commit message itself. The following columns P and Q (heading “author_a” and “author_b”) are the final classification (upon which the Cohen Kappa was calculated). Column R (heading “notes”) contains some commentaries on specific cases that may be meaningful.</span></p> <p><span><span>❖<span> </span></span></span><strong><span>Predicted:</span></strong><span> The results of the automatic classifiers (both 1st and 2nd round) are included. The additional columns are generated by the classifiers.</span></p> <p><span><span>❖<span> </span></span></span><strong><span>Verifications</span></strong><span> contain the manually labeled data that we used as 1st and 2nd verification rounds. This is a simplified dataset with the commit’s sha and the parsed message. The authors classified columns E and F independently and individually. The labels stated here are those that the authors agreed to (without having access to column D). Note that column D was added afterward by sha-matching by another author to calculate the Cohen Kappa. The yellow rows are those with disagreements.</span></p> <p><span> </span><strong><span>RQ2_RQ3 Folder</span></strong><span> contains the manually labeled dataset for RQ2 and RQ3 (SATD Types and Activities). </span></p> <p><span>NOTE: Kindly note that many messages or classifications are <em>multiline</em>. This means that the cells have to be expanded to be capable of reading all text included in a cell.</span></p>
Dataset used for "Recruitment credit cannot compensate for extinction debt in a degraded dry Afromontane forest, northern Ethiopia"
<p>The data is part of a large dataset collected by WeForest Ethiopia, a nonprofit organisation engaged in restoring degraded forests in different parts of Ethiopia. This is particularly data from Desa'a forest, a dry Afromontane forest in Tigray. This data presents the identity and number of mature woody plant species (individuals with >1.5 height), their DBH (diameter at breast height, 1.3 m) or DSH and height (diameter at stump height, 0.3 m) measured at 400 m<sup>2</sup> and, the identity and number of regeneration of woody plants, height < 1.5 m, measured in 9m<sup>2</sup> nested within the 400 m<sup>2</sup> plot.</p> <p>These data were used in a manuscript entitled "Recruitment credit cannot compensate for extinction debt in a degraded dry Afromontane forest, northern Ethiopia", submitted to the Journal of Vegetation Sciences and accepted for publication.</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.