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63 results for “technical debt”
"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>
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>
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>
Investigating the Point of View of Project Management Practitioners on Technical Debt - A Study on Stack Exchange
<p>Investigating the Point of View of Project Management Practitioners on Technical Debt - A Study on Stack Exchange.</p> <p>Journal of Software Engineering Research and Development (JSERD) 2023.</p> <p> </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>
Additional Material for Paper: IT Managers' Perspective on Technical Debt Management
<p>This is the additional material for the Paper "IT Managers’ Perspective on Technical Debt Management" submitted to the Journal of Systems and Software.</p> <p>The folder contains the following files:</p> <p> </p> <ul> <li>Consent form - blinded.pdf / Consent form.pdf:<br> The consent form was used to gather the participants' consent to participate in this study.</li> <li>PreliminarySurvey.pdf:<br> The survey was sent to all participants in advance to gather basic statistical data.</li> <li>ParticipantsOverview.pdf / ParticipantsOverview.xslx<br> The overview of all participants was extracted from the preliminary survey. For anonymity reasons, we can not provide the country/language of the participants.</li> <li>InterviewGuide.pdf<br> The interview guide was followed by the interviewers, including all questions and the rationale for questions added before the 2nd iteration.</li> <li>CodeSystem.xlsx<br> All Codes were gathered from the interviews with a short explanation (in Excel to use the grouping function for a better overview).</li> <li>AnalyticMemos_Themes.pdf<br> All free memos were created during the study, including all themes that led to the main concerns described in the paper.</li> <li>Focusgroup-discussion_Themes_slides.pdf<br> The slides were created for the focus group discussion. The participants were introduced to the "main concerns" and asked to comment on these findings.</li> <li>V4C_model.drawio<br> This is the original drawio file of our V4C model, including all diagrams from the paper. Additionally, we provide the complete version of the visibility diagram, which had to be reduced in the paper due to the page limitation. Also, the file contains one more diagram with another example of the model's usage.</li> <li>V4Cmodel.pdf, V4Cmodel_example.pdf, VisibilityOfCausesConsequences.pdf; VisibilityCycles.pdf<br> All diagrams of the paper are in single PDF files.</li> <li>V4CTDmodel+example.pptx<br> A short presentation of the model and an example of its usage in PowerPoint format to immediately use.</li> </ul>
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>
Technical Debt Awareness: Definitions, Methods, and Metrics
<p>The additional material for the paper "Technical Debt Awareness: Definitions, Methods, and Metrics" comprises the following documents:</p><p> </p><p>* Guideline_to_Use_TD-SAGAT.docx (.pdf)</p><p> A short guideline on how the use the TD-SAGAT method.</p><p>* Semi_SLR_papers.xlsx</p><p> All excluded and included papers of our smei-SLR whit resons for exclusion and extracted data for included papers.</p><p>* Participant_and_Meetings.xslx</p><p> overview of all participants with role and years of experience </p><p> overview of all meetings, we used for analysis</p><p>* Survey_and_Observation_Template_plus_Mappings.xlsx</p><p> survey questions for workshop survey, TD-SAGAT survey and observation protocol</p><p> mapping of all questions and aspects between the surveys and the obersvation aspects</p><p>* SAGAT_for_TD_from_Goals_to_Queries.pdf</p><p> Determination of survey questions by following the SAGAT approach of following the hierarchy of goals, subgoals, decisions, requirements, queries </p><p>* TD-SAGAT_Analysis.xlsx</p><p> analysis data for the TD-SAGAT analysis, incl. chart</p><p>* Observations.xslx</p><p> observation protocols (anonymized) for all observed meetings</p><p> </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>
Technical Debt Dataset from Arcan Pipeline on 2023-11-17T07:39:28.107423+00:00
The dataset of the study "A continuous open source data collection platform for architectural technical debt assessment.
Additional material for paper: Technical Debt Game - Enabling Discussions about Technical Debt
<div>This is an overview of the additional material for the paper "Technical Debt Game - Enabling Discussions about Technical Debt" submitted to the conference "TechDebt 2025":</div> <div> </div> <div>* ENGameManualDigital.pdf</div> <div> Instructions on how to play the TechDebt Game, including details and explanation on how the game relates to the reality of dealing with technical debt.</div> <div> </div> <div>* ENShortInstruction.pdf</div> <div> Instructions on how to play the TechDebt Game, shortened to the minimum and without details/examples and without explanation on how the game relates to the reality of dealing with technical debt.</div> <div> </div> <div>* ENPrintversion.pdf</div> <div> Gameboard, cards, scoreboard, etc., to print out and play the game offline. The Game must be augmented with two dice, two game stones, and multiple tiles in two different colors. Instead of tiles, one can also use cut-out paper tiles or taller game stones.</div> <div> </div> <div>* Introduction guide for game master.pdf</div> <div> When explaining the game to participants, the game masters, i.e., researchers, followed this guideline.</div> <div> </div> <div>* Survey Printversion - TD Game.pdf</div> <div> Print-version of the online survey each player filled out after the game.</div> <div> </div> <div>* Survey_analysis.xlsx</div> <div> Analysis of the survey results, including tables for:</div> <div>- AllResults: overview of all results</div> <div>- questions: list of all questions</div> <div>- AHA moments: explanations for each AHA-moment</div> <div>- sample 1 and 2: analysis of demographic results</div> <div>- comments game: analysis of the open question regarding feedback on the game</div> <div>- comments attitude + behavior: analysis of the open question regarding attitude and behavior changes</div> <div>- Data AHAmoments: Results of the AHA-moments per player</div> <div>- Data all Players (Vis.): summarized data and visualization for all players</div> <div>- Data per category: AHA-moment's results ordered by stakeholder category</div> <div>- Reality&Insights p. cat. (Vis.): sums for new real-life connections ("had this AHA moment, but is not new") and new insights ("had this AHA moment and is new") per player for each stakeholder category</div> <div>- Data <B|TJ|TS>-Player (Vis.): summarized AHA-moments and visualization for each stakeholder category</div> <p> </p>
Establishing Technical Debt Management - A Five-Step Workshop Approach and an Action Research Study
<div> <div>The additional material for the paper "Establishing Technical Debt Management - A Five-Step Workshop Approach and an Action Research Study" comprises the following documents:</div> <div> </div> <div>* Guideline_to_Use_TD-SAGAT.docx (.pdf)</div> <div> A short guideline on how to use the TD-SAGAT method.</div> <div>* SAGAT_for_TD_from_Goals_to_Queries.pdf</div> <div> Determination of survey questions by following the SAGAT approach of following the hierarchy of goals, subgoals, decisions, requirements, and queries </div> <div>* WIKI page on technical debt.pdf</div> <div> The teams' wiki page on TD management written by the TD manager</div> <div>* AzureTDTickets_Template.pdf</div> <div> Screenshot of the backlog's TD item template as presented in the paper for better readability.</div> <div>* OverviewOfActionCycles_ExtendedTable5.xlsx</div> <div> Table 5 (Overview of actions cycles with workshop topics, TD activities, actions taken, and learning) as an Excel sheet with additional information on actions and learning, which we did not put into the paper</div> <div> </div> <div>* Workshops/workshop_slides/TDMGuide_<x>_Workshop.pptx</div> <div> Slides for each of the six workshops </div> <div>* Workshops/workshop_slides/TDMGuide_0_InitTalk_35Min_EN.pptx</div> <div> The initial one-hour introduction into the TD topic (~40 min. talk + 20 min. for a short discussion) </div> <div> </div> <div>* Workshops/workshop_flipcharts/WS<No.>_<explanation>.jpg</div> <div> Six anonymized flipchart photos to give an impression of the workshops, particularly of the kick-off workshop (other workshops made less use of flipcharts).</div> <div> Translations are directly included in the photos, except for ...</div> <div>* Workshops/workshop_flipcharts/WS1_Topic Cache - Translation.txt</div> <div> Translation of the German topic cache in an extra file, as there were too many topics to translate in the photo</div> <div> </div> <div>* Workshops/Workshop Agendas.pdf</div> <div>Agendas for the kick-off workshop and a general agenda for further workshops, each with a detailed agenda for workshop moderators and a rough agenda for participants.</div> <div> </div> <div>* Workshops/ TD_Energy_Matrix.xlsx</div> <div> Matrix where participants mark their own, the teams, and the companies' (assumed) level of willingness to invest in a TDM versus their (assumed) evaluation of TDM's importance. Used in kickoff workshop</div> <div> </div> <div> </div> <div>* Evaluations/Backlog_Evaluations_and_Figures.xlsx</div> <div>- Backlog data from the 5th workshop (for 6th workshop, we used the PowerBI Visualization instead)</div> <div>- Evaluation of this data comprising </div> <div>- priority calculations based on priority risk and </div> <div>- priority calculations based on priority risk based on ROI</div> <div> </div> <div>* Evaluations/Meetings_Overview_Observations_Evaluation_Figures.xlsx</div> <div>- Overview of all meetings plus information on length, participants, whether a video exists and was used for evaluation</div> <div>- Filled out observation protocols from different meetings</div> <div>- Summation, evaluations, and resulting figures</div> <div> </div> <div>* Evaluations/TD-SAGAT_Survey_and_SurveyResults_Figures.xlsx</div> <div>- Survey questions for TD-SAGAT plus results</div> <div>- Evaluation plus figures</div> <div> </div> <div>* Evaluations/Workshop_Survey_and_SurveyResults_Figures.xlsx</div> <div>- Survey questions for Workshops plus results</div> <div>- Evaluation plus figures</div> <div> </div> <div>* Evaluations/Kickoff_Survey_and_SurveyResults.xlsx</div> <div>- Survey questions for Kickoff Workshops (incl. questions on current TDM process) plus results</div> <div>- Evaluation in Workshop_Survey_and_SurveyResults_Figures.xslx</div> <div> </div> <div> </div> <div>* Visualizations/V<No.>_<adopted|mockup>_<explanation>.png</div> <div>- "adopted" visualizations are similarly presented in the paper in Fig. 2.</div> <div>- "mockup" visualizations were presented during the workshops but not adopted in this form. </div> <div>If a respective "adopted" visualization for this No. exists, the visualisation was replaced by a more suitable one. </div> <div>If no respective "adopted" visualization for this No. exists, the visualisation was discarded.</div> <div> </div> <div> </div> <div>* Visualizations/Dashboard <Manager|Team>.png</div> <div>- shows the dashboard mockups for the manager and the team, respectively. </div> <div>Visualizations presented in the dashboards that were not adopted by the team were removed or replaced by the improved visualizations in the final dashboard versions.</div> <div> </div> <div> </div> <div> </div> <div> </div> </div>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.