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15 results for “Issue Reports”
Eclipse issue report dataset
<div> <div>This dataset contains bug reports for key Eclipse projects. It has been updated to reflect the latest available data and is suitable for: software engineering research, applying mining software repositories techniques, bug prediction, classification, triage, and related studies on highly configurable systems.</div> <div> </div> <div> <div> <h3>Projects Covered</h3> </div> </div> </div> <table> <tbody> <tr> <th>Project</th> <th>Number of issues</th> </tr> </tbody> <tbody> <tr> <td>Platform</td> <td>122.497</td> </tr> <tr> <td>JDT</td> <td>63.266</td> </tr> <tr> <td>CDT</td> <td>23.371</td> </tr> <tr> <td>BIRT</td> <td>23.308</td> </tr> <tr> <td>PDE</td> <td>17.639</td> </tr> <tr> <td>Equinox</td> <td>14.559</td> </tr> <tr> <td>Mylyn</td> <td>13.906</td> </tr> <tr> <td>TPTP</td> <td>10.579</td> </tr> <tr> <td>Papyrus</td> <td>13.253</td> </tr> </tbody> </table> <p><br>Contains the next attributes: Issue URL,ID,Alias,Classification,Component,Product,Version,Platform,Op sys,Status,Resolution,Depends on,Dupe of,Blocks,Groups,Flags,Severity,Priority,Deadline,Target Milestone,Creator,Creator Detail,Creation time,Assigned to,Assigned to detail,CC,CC detail,Is CC accessible,Is confirmed,Is open,Is creator accessible,Summary,Description,URL,Whiteboard,Keywords,See also,Last change time,QA contact,History/Activity Log,Comments,Attachments.</p> <div> <div>All the information about these attributes can be found at <a href="https://bugzilla.readthedocs.io/en/5.2/api/" target="_blank" rel="noopener">Bugzilla's API documentation</a></div> <div> </div> <div>This dataset was obtained using the Issuex tool, available at Github:<a href="https://github.com/diverso-lab/Issuex"> https://github.com/diverso-lab/Issuex</a></div> </div>
Mozilla CORE issue report dataset
<div>This dataset contains 522,355 bug reports from the CORE project of Mozilla. It has been updated to reflect the latest available data and is suitable for: software engineering research, applying mining software repositories techniques, bug prediction, classification, triage, and related studies on highly configurable systems.</div> <div> </div> <div> <div> <h2>Data Schema</h2> Each bug report in the dataset include the following attributes:</div> <div><br> <div>-<strong> Identificative Information</strong>: Issue URL, ID, Alias, Summary, Description<br>- <strong>Classification</strong>: Classification, Component, Product, Version, Platform, Op sys<br>- <strong>Status</strong>: Status, Resolution, Severity, Priority, Target Milestone, Deadline, Is open, Is confirmed, Creation time, Last change time<br>- <strong>People Involved:</strong> Creator, Creator Detail, Assigned to, Assigned to detail, CC, CC detail, Is CC accessible, QA contact<br>- <strong>Relationships:</strong> Depends on, Dupe of, Blocks, See also<br>- <strong>Activity log</strong>: History/Activity Log<br>- <strong>Comments</strong>: Comments<br>- <strong>Other</strong>: Groups, Flags, Whiteboard, Is creator accessible, Keywords, URL, Attachments</div> <br><br> <div>All the information about these attributes can be found at <a href="https://bugzilla.readthedocs.io/en/5.2/api/" target="_blank" rel="noopener">Bugzilla's API documentation</a></div> <div>This dataset was obtained using the Issuex tool, available at Github:<a href="https://github.com/diverso-lab/Issuex"> https://github.com/diverso-lab/Issuex</a></div> </div> </div>
GIRT-Data: Sampling GitHub Issue Report Templates
<p><strong>GIRT-Data</strong> is the first and largest dataset of <strong>issue report templates (IRTs)</strong> in both YAML and Markdown format. This dataset and its corresponding open-source crawler tool are intended to support research in this area and to encourage more developers to use IRTs in their repositories. The stable version of the dataset, containing <code>1_084_300</code> repositories, that <code>50_032</code> of them support IRTs.</p> <p>For more details see the GitHub page of the dataset: <a href="https://github.com/kargaranamir/girt-data">https://github.com/kargaranamir/girt-data</a></p> <p><br> The dataset is accepted for <a href="https://conf.researchr.org/track/msr-2023/msr-2023-data-showcase">MSR 2023</a> conference, under the title of "GIRT-Data: Sampling GitHub Issue Report Templates" <a href="https://scholar.google.com/scholar?q=GIRT-Data:+Sampling+GitHub+Issue+Report+Templates">Search in Google Scholar</a>.</p>
Few-Shot Learning for Issue Report Classification
<p>This dataset is a sample of the NLBSE23 dataset for Issue Report Classification, manually re-labeled.</p>
The Pollution from Obsolete Issue Report: An Empirical Study
<p>The Pollution from Obsolete Issue Report: An Empirical Study</p> <p>Project summary</p> <p>In software development, programmers use issue trackers to manage their maintenance issues and record valuable maintenance details in issue reports. Based on these issue reports, programmers have enhanced code comprehension and researchers have mined knowledge from issue reports to assist various programming tasks. Although issue reports are useful, some of them can be obsolete, in that their corresponding commits are overwritten or rolled back, with the evolution of software. The obsolete issue reports can invalidate their references and descriptions, and can have far-reaching impacts on the approaches built on them.</p> <p>To deepen the understanding of obsolete issue reports, we conducted the first empirical study to analyze obsolete issue reports. We consider that an issue report is obsolete if its revisions are partially or totally removed in later commits. To measure how an issue report becomes obsolete, we define an obsolete ratio of an issue report as its deleted lines over all its modified lines. In this paper, we build a tool, ICLINKER, to inspect the obsolete issue reports and calculate the obsolete ratios. With ICLINKER, we analyze 70,180 commits and 46,257 issue reports that are collected from five Apache projects. Taking them as our inputs, we explore four research questions, which concern the distributions, the references, and the explanations of obsolete issue reports. Our findings on these research questions enrich the knowledge of obsolete issue reports, and some are even counterintuitive. For example, we find that obsolete issue reports are mixed with other issue reports. As another example, we find that only a small portion of issue reports are mentioned in code comments, but about half of them are obsolete. Based on our results, we analyze some directions that are worthy of exploration.</p> <p>Our identified obsolete ratios</p> <p>We identified the obsolete ratios of the issue reports from five projects. Their obsolete ratios are as follows: calcite.txt, cassandra.txt, derby.txt, hbase.txt, and hive.txt.</p> <p>Our Dataset</p> <p>Our dataset is stored in the data folder.</p>
Data Package for "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns"
<p>This Zenodo repository contains the data supporting the findings of the journal paper, titled "A Platform-Agnostic Approach for Automatically Identifying Real-Life Performance Issue Reports with Heuristic Linguistic Patterns", published on IEEE Transactions on Software Engineering, including:</p> <ol> <li><strong>Heuristic Linguistic Pattern Set</strong>: <span>we listed the 80 HLP we derived from </span><span>Apache's JIRA issue tracking system</span><span>. Column "</span><span>Category" </span><span>lists the type of each pattern. Namely, LEX represents lexical pattern, STR represents structural pattern, SEM represents semantic pattern, and PRF represents profiling pattern. Column "Name" is a descriptive name we give to each pattern. Column "Definition" defines the detailed content in each pattern.</span></li> <li><strong>Manual Tagging Results</strong>: manual_tagging.xlsx spreadsheet <span>comprises both sentence-level and issue-level manually tagging results for three datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', '</span><span>Dataset-</span><span>2: Apache Jira's Heterologous Evaluation', and '</span><span>Dataset-</span><span>3: Other Platform's Evaluation'. The tagging results are segmented into sentence-level tabs ("Dataset-1 Sen", "Dataset-2 Sen", "Dataset-3 Sen") and issue-level tabs ("Dataset-1 Issue", "Dataset-2 Issue", "Dataset-3 Issue").</span></li> <li><span><strong>RQ Findings</strong>: </span> <p><span>This section contains detailed data findings from six research questions (RQ1 to RQ6).</span></p> <ul> <li> <p><span>The RQ1 tab provides an evaluation of our HLP-based approach, showing the precision, recall, and F1-Score of eight classifiers. These results are juxtaposed with the corresponding values from baseline methods, at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ2 tab illustrates the precision, recall, and F1-Score of eight classifiers under two training conditions: a balanced training dataset (BT+HLP) and an imbalanced training dataset (UBT+HLP). These outcomes are contrasted with the equivalent values from baseline methods, also trained under balanced (BT+BLM) and imbalanced (UBT+BLM) conditions. The results are shown at both sentence and issue levels for automatic tagging.</span></p> </li> <li> <p><span>The RQ3 tab evaluates the dataset transferability of our HLP-based approach in comparison to baseline methods. It achieves this by analyzing the precision, recall, and F1-Score metrics for eight classifiers under two different "training/testing" dataset conditions, i.e., 'D1/D1' and 'D1/D3'. These conditions allow for a direct comparison of performance when applied to the same dataset ('D1/D1') versus when transferred to a different dataset ('D1/D3'). Additionally, the tab includes an 'Avg Change' and 'p-value' section, summarizing the statistical change in performance metrics between the two dataset conditions. </span></p> </li> <li> <p><span>The RQ4 tab presents a direct comparison between strict and fuzzy HLP matching approaches, assessed through precision, recall, and F1-Score metrics across eight issue classifiers.</span></p> </li> <li> <p><span>The RQ5 tab examines the influence of sentence order on the accuracy of eight classifiers within our approach. It shows the change in precision, recall, and F1-Score when the sentence order feature is taken into consideration versus when it is not.</span></p> </li> <li> <p><span>The RQ6 tab explores the impact of feature selection algorithms on both issue and sentence-level tagging accuracy. This tab presents the average precision, recall, and F1-Score for three experiments: Boruta, Recursive Feature Elimination (RFE), and the usage of all 80 features. </span></p> </li> </ul> </li> <li><strong>Qualitative Analysis</strong>: <p><span>This spreadsheet offers a comprehensive examination of the data supporting Section 6.1, which focuses on Qualitative Analysis. It is organized into several tabs, each dedicated to specific research questions (RQs) as outlined below:</span></p> <ul> <li> <p><span>Tab "RQ-1" showcases performance issue reports accurately detected by our High-Level Performance (HLP) approach's top model, XGBoost, which were not identified by the benchmark method's leading model, BERT. This highlights the comparative advantage of our approach in identifying nuanced performance issues.</span></p> </li> <li> <p><span>Tab "RQ-2" continues the exploration of performance issue reports, presenting cases with specific details (to be added).</span></p> </li> <li> <p><span>Tab "RQ-3" delves into the unique capabilities of XGBoost, the leading model in our HLP approach, showcasing its ability to detect performance issues missed by the baseline's top model, BERT. This comparison is drawn under distinct conditions: with pre-training (Dataset 1) and without pre-training (Dataset 3), illustrating the robustness and adaptability of our model.</span></p> </li> <li> <p><span>Tab "RQ-4" focuses on performance issue reports uniquely identified through the implementation of Fuzzy HLP Matching within our HLP approach. This method underscores the innovative matching techniques that enhance issue detection.</span></p> </li> <li> <p><span>Tab "RQ-5" presents performance issue reports pinpointed exclusively by applying the Issue HLP Matrix within our approach. This tab demonstrates the effectiveness of our matrix-based analysis in isolating and identifying specific performance concerns.</span></p> </li> <li> <p><span>Tab "RQ-6" is dedicated to performance issue reports uniquely detected by incorporating feature selection techniques into our HLP approach. This illustrates the value of advanced feature selection in improving the precision of performance issue identification.</span></p> </li> </ul> </li> <li><strong>LLM Experiment Data</strong>: presents the tagging outcomes of Large Language Models (LLMs), specifically ChatGPT-3.5 and ChatGPT-4, across three distinct datasets: 'Dataset-1: Apache Jira's Homologous Evaluation', 'Dataset-2: Apache Jira's Heterologous Evaluation', and 'Dataset-3: Evaluation on Other Platforms'. The results are organized into three separate tabs: 'Dataset-1 Issue', 'Dataset-2 Issue', and 'Dataset-3 Issue'.</li> <li><strong>ChatGPT Operation Python Script</strong>: crafted for automating the evaluation and tagging of issue reports in Excel using Large Language Models (LLMs) like ChatGPT-3.5 and ChatGPT-4. It underscores the importance of administrative rights for file modifications and outlines procedures for reading from and writing responses to Excel files. Key functions include querying LLMs with issue descriptions, processing their responses, and updating the spreadsheet with 'Yes' or 'No' labels and explanatory reasons, thereby facilitating an organized review of LLM performance across different datasets.</li> </ol>
D6.1 - Report on non-technical barrier and legal issues - Literature-mapping database
<p>This data set presents a literature map for D6.1 "Report on non-technical barrier and legal issues" considering relevant non-technical and legal challenges for consideration during the deployment of a pay-per-wash business model in European.</p>
Ethical issues in patient-reported outcomes (PROs) among various populations - a special perspective from dental practice
<p>Video resumen de un artículo presentado en el VI Congreso Latinoamericano de Marketing Social en Brasil</p>
An Empirical Analysis of Rebalancing Methods for Security Issue Report Identification - Supplementary Material
<p>Bug report data set for the Ubuntu issue tracker on Launchpad and accompanying test python scripts.</p>
The Pollution from Obsolete Issue Reports: An Empirical Study
<h1>The Pollution from Obsolete Issue Report: An Empirical Study</h1> <h2>Project summary</h2> <p>In software development, programmers use issue trackers to manage their maintenance issues and record valuable maintenance details in issue reports. Based on these issue reports, programmers have enhanced code comprehension and researchers have mined knowledge from issue reports to assist various programming tasks. Although issue reports are useful, some of them can be obsolete, in that their corresponding commits are overwritten or rolled back, with the evolution of software. The obsolete issue reports can invalidate their references and descriptions, and can have far-reaching impacts on the approaches built on them.</p> <p>To deepen the understanding of obsolete issue reports, we conducted the first empirical study to analyze obsolete issue reports. We consider that an issue report is obsolete if its revisions are partially or totally removed in later commits. To measure how an issue report becomes obsolete, we define an obsolete ratio of an issue report as its deleted lines over all its modified lines. In this paper, we build a tool, ICLinker, to inspect the obsolete issue reports and calculate the obsolete ratios. With ICLinker, we analyze 116,106 commits and 72,136 issue reports that are collected from 9 Apache projects. Taking them as our inputs, we explore four research questions, which concern the distributions, the references, the explanations and the impact of obsolete issue reports. Our findings on these research questions enrich the knowledge of obsolete issue reports, and some are even counterintuitive. For example, we find that obsolete issue reports are mixed with other issue reports. As another example, we find that only a small portion of issue reports are mentioned in code comments, but about half of them are obsolete. We confirm that 48.18% of issue reported recommended by a recent approach are obsolete. As many approaches take issue reports as inputs, obsolete issue reports can affect more research topics than recommending issue reports.</p>
ISSUES OF HARMONIZING THE NATIONAL ACCOUNTING SYSTEM WITH THE INTERNATIONAL FINANCIAL REPORTING SYSTEM
Open the record for dataset details and reuse information.
Radicle Revive™ CSP: A Trial Evaluating Plant-Based Health and Wellness Products on Perimenopausal Women's Self-Reported Health Issues and Outcomes
ClinicalTrials.gov study NCT07148011. IPD Sharing: NO. Countries: 1. Publications: 0.
Aviation Safety Reporting System: Fuel Management Issues
A sampling of reports referencing incidents of fuel mismanagement, and operational concerns for fuel planning.
Aviation Safety Reporting System: CRM Issues
Crew Resource Management (CRM) inflight situations (conflicts, NMACs, and emergencies).
Mining and Classification Privacy Requirements in Issue Reports
<p>A replication package for a paper entitled "Mining and Classification Privacy Requirements in Issue Reports" submitted to the 43rd International Conference on Software Engineering (ICSE 2021)</p>
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