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14 results for “Self-Admitted Technical Debt”
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 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>
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>
An Exploratory Study on the Occurrence of Self-Admitted Technical Debt in Android Apps
<p>This is the code and dataset that accompanies the study: "<strong>An Exploratory Study on the Occurrence of Self-Admitted Technical Debt in Android Apps</strong>." This study has been accepted for publication at the 2023 International Conference on Technical Debt.</p> <p><strong><em>Following is the abstract of the study:</em></strong></p> <p>Technical debt describes situations where developers write less-than-optimal code to meet project milestones. However, this debt accumulation often results in future developer effort to live with or fix these quality issues. To better manage this debt, developers may document their sub-optimal code as comments in the code (i.e., self-admitted technical debt or SATD). While prior research has investigated the occurrence and characteristics of SATD, this research has primarily focused on non-mobile systems. With millions of mobile applications (apps) in multiple genres available for end-users, there is a lack of research on sub-optimal code developers intentionally implement in mobile apps. </p> <p>In this study, we examine the occurrence and characteristics of SATD in 15,614 open-source Android apps. Our findings show that even though such apps contain occurrences of SATD, the volume per app (a median of 4) is lower than in non-mobile systems, with most debt categorized as Code Debt. Additionally, we identify typical elements in an app that are prone to intentional sub-optimal implementations. We envision our findings supporting researchers and tool vendors with building tools and techniques to support app developers with app maintenance. </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>
Dataset: An empirical study on self-admitted technical debt in modern code review
<pre>This data was used in the IST paper "An Empirical Study on Self-Admitted Technical Debt in Modern Code Review". The program to use this data is published in GitHub (https://github.com/Yutaro-Kashiwa/ReviewSATD_RP) When you use this data in your research, please cite the following papers: ``` @article{Kashiwa:IST:2022:SATD_Review, author = {Yutaro Kashiwa and Ryoma Nishikawa and Yasutaka Kamei and Masanari Kondo and Emad Shihab and Ryosuke Sato and Naoyasu Ubayashi}, title = {An empirical study on self-admitted technical debt in modern code review}, journal = {Information and Software Technology}, volume = {146}, pages = {106855}, year = {2022}, url = {https://doi.org/10.1016/j.infsof.2022.106855}, doi = {10.1016/j.infsof.2022.106855} } ``` </pre>
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>
Exploring Community Smells in Machine Learning Applications: Analytical Insights and Their Association with Self-Admitted Technical Debt
Open the record for dataset details and reuse information.
Exploring Community Smells in Machine Learning Applications: Analytical Insights and Their Association with Self-Admitted Technical Debt
Open the record for dataset details and reuse information.
Beyond the Code: Mining Self-Admitted Technical Debt in Issue Tracker Systems
<p>Self-admitted technical debt (SATD) is a particular case of Technical Debt (TD) where developers explicitly acknowledge their sub-optimal implementation decisions. Previous studies mine SATD by searching for specific TD-related terms in source code comments.By contrast, in this paper we argue that developers can admit technical debt by other means, e.g., by creating issues in tracking systems and labelling them as referring to TD. We refer to this type of SATD as issue-based SATD or just SATD-I. We study a sample of 286 SATD-I instances collected from five open source projects, including Microsoft Visual Studio and GitLab Community Edition. We show that only 29% of the studied SATD-I instances can be tracked to source code comments. We also show that SATD-I issues take more time to be closed, compared to other issues, although they are not more complex in terms of code churn. Besides, in 45% of the studied issues TD was introduced to ship earlier, and in almost 60%it refers to Design flaws. Finally, we report that most developers pay SATD-I to reduce its costs or interests (66%). Our findings suggest that there is space for designing novel tools to support technical debt management, particularly tools that encourage developers to create and label issues containing TD concerns.</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>
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>
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