Find research datasets worth reusing
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36
datasets available to search
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Dataset results
36 results for “bug dataset”
Dataset 2.2 Bark bug design for realization in organic materials
<p>Dataset 2.2 of Bark bug design for realization in organic materials</p>
Dataset for ISSTA'24 An Empirical Study on Kubernetes Operator Bugs
<p>This is the dataset for the ISSTA'24 submission <strong>An Empirical Study on Kubernetes Operator Bugs</strong>. It contains 210 operator bugs collected from 36 popular Kubernetes operators.</p> <p>It can be used to replicate and reproduce the paper's study, as well as to serve as a bug benchmark for future work on combating operator bugs in Kubernetes.</p>
Dataset of the Paper "Understanding Bugs of Learning Management Systems: An Exploratory Study on Moodle and Sakai"
<p>The datasets collected from JIRA and GitHub are used for an empirical study and data analysis of bug classification issues in learning management systems (Moodle and Sakai). A brief description of each document in the dataset is provided below:</p> <p>1. "LMS_bugs_data.sql" contains bug data items collected from JIRA for LMS (Moodle and Sakai), specifically including: BugID (D1), BugKey (D2), BugSummary (D3), BugStatus (D4), BugResolution (D5), BugDescription (D6), BugCreateAt (D7), BugCloseAt (D8), and BugComments (D9). Additional data items (e.g., fixversions, priority) are provided to support further research.</p> <p>2. "LMS_commits_data.sql" contains the commits corresponding to bug fixes in LMS (Moodle and Sakai) from GitHub, specifically including: CommitSha (D10), CommittedDate (D11), CommitMessage (D12), CommitTotal (D13), Changefiles (D14), Files (D15), and BugKey(D2). Other data items (e.g., commitauthor, additions) are provided for supporting the research.</p> <p>3. OT, LOCM, NOFM, Entropy, NODP, NOC (D16-D21) require the above data for calculation.</p> <p>4. "LMS_Moodle_Bug_Classification_Results_7898.xlsx" contains the bug classification results for Moodle, including: BugID, BugKey, Summary, ClusterId, GPT-4 Label Topic, BugType, and the mapping between BugType and ClusterId.</p> <p>5. "LMS_Sakai_Bug_Classification_Results_6089.xlsx" contains the bug classification results for Sakai, including: BugID, BugKey, Summary, ClusterId, GPT-4 Label Topic, BugType, and the mapping between BugType and ClusterId.</p>
Dataset of "Poster - BugOss: Regression Bug Benchmark for Empirical Study of Regression Fuzzing Techniques"
<p>Dataset of "Poster - BugOss: Regression Bug Benchmark for Empirical Study of Regression Fuzzing Techniques"</p>
Dataset of bugs in automated driving softwares
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161 samples from historical bug fixes dataset
<p>161 bugs and corresponding patches from historical bug fixes dataset that fulfill following criteria:</p> <ul> <li>Unique</li> <li>Satisfy redundancy assumption at file level</li> <li>Fixed by applying replacement mutation</li> <li>Require fixing at expression level</li> <li>Having available project and dependency files</li> </ul>
BuGL - A Cross-Language Dataset for Bug Localization
<p>BuGL - A Cross-Language Dataset for Bug Localization</p>
Dataset on Characterizing Bugs and Inspecting Projects
<p>Temporary anonymous data link for paper review: "A Comprehensive Study on Deep Learning Application Bugs in Python," including bugs after manual labeling and projects after manual inspection.</p>
Dataset for the paper: Understanding Bug-Reproducing Tests
<p>Dataset for the paper "Understanding Bug-Reproducing Tests". This dataset contains information about both "all tests" and "bug-reproducing tests".</p>
[Dataset] Minecraft: Automated Mining of Software Bug Fixes with Precise Code Context
<p>Repository mining of bug fixes from version control systems like GitHub is a challenging problem as far as the precision of the bug context is concerned before and after the fix. Coupled with this, identification of the type of the bug fix goes a long way towards creating high quality datasets that can be used for several downstream tasks. However, existing bug fix datasets suffer from the following limitations that dilute the data quality. Firstly, they do not focus on multilingual projects in their entirety given that most open-source projects are now multilingual. Secondly, the granularity of the bug fixes are considered only at the function/method level without specifying line/statement level information. Thirdly, bug fixes lying within the scope of a source file but outside any of its constituent functions have not been examined. In this paper, we propose a solution to overcome the aforementioned limitations by introducing a novel and extensive dataset named Minecraft. With a size of 28.8GB (considering 416 GitHub projects encompassing programming languages such as C, C++, Java, and Python, 2.2M commits, 3.29M bug-fix pairs), Minecraft surpasses the existing datasets by 4-fold enlargement in terms of data availability. We believe Minecraft would serve as a valuable resource for various stakeholders in the software development and research communities, empowering them to improve software quality, develop innovative bug detection and auto-fix techniques, and<br> advance the field of software engineering.<br> </p>
FireFixDB: A Dataset on Mozilla Firefox Bugs and Fixes
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A Dataset and Service for Automated Identification of Security Bug Reports - Supplementary material
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FireFixDB: A Dataset on Mozilla Firefox Bugs and Fixes
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Dataset for Towards Understanding Performance Bugs in Popular Data Science Libraries
<div> <div>This dataset contains 138 performance bugs in data science popular libraries, and their impacts, root causes, locating and fixing challenge, and fixing strategy.</div> <div>Our replication package consists of three main folders:RQ1&2_Impacts_and_Root_Causes, RQ3_Root_Causes_Locating_Fixing_Effort_Challenge and RQ4_Fixing_Strategy.</div> <br> <div>RQ1&2_Impacts_and_Root_Causes</div> <br> <div>In this folder we first placed the identified impact (Explicit and Implicit). Then we gave the identified symptoms and root cause taxonomy. In each file (corresponding to each iteration), we provided the repo name, issue number, and the label (symptom and root cause).</div> <br> <div>RQ3_Root_Causes_Locating_Fixing_Effort_Challenge</div> <br> <div>We provided the number of comments, lines of changed code and issue duration involved in handling performance bugs. Furthermore, the challenge in resolving these bugs in data science libraries are identified here.</div> <br> <div>RQ4_Fixing_Strategy</div> <br> <div>We provided the identified fixing strategy with small LOC. In the file, we provided the repo name, issue number, and the label (fixing strategy).</div> </div>
Dataset for the paper: Understanding Bug-Reproducing Tests
<div>Dataset for the paper "Understanding Bug-Reproducing Tests". This dataset contains information about both "all tests" and "bug-reproducing tests".</div>
Dataset for the paper "Understanding Bug-Reproducing Tests"
<p>Dataset for the paper "Understanding Bug-Reproducing Tests".</p> <p>This dataset contains information about both "all tests" and "bug-reproducing tests".</p>
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.