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36 results for “bug dataset”
Studying Bug-Fixing Commits in the WoC Dataset: Replication Package
<p>A replication package for the MSR 2023 Challenge submission titled "Studying Bug-Fixing Commits in the WoC Dataset".</p>
Unified Bug Dataset
<p><strong>A Public Unified Bug Dataset for Java and its Assessment Regarding Metrics and Bug Prediction.</strong></p> <p>Online appendix for the paper submitted to the <strong><em>Software Quality Journal</em></strong>.</p> <p>Bug datasets have been created and used by many researchers to build and validate novel bug prediction models. In this work, our aim is to collect existing public source code metrics based bug datasets and unify their contents. Furthermore, we wish to assess the plethora of collected metrics and the capabilities of the unified bug dataset in bug prediction.<br> We considered 5 public datasets and we downloaded the corresponding source code for each system in the datasets and performed source code analysis to obtain a common set of source code metrics. This way, we produced a unified bug dataset at class and file level as well. We investigated the diversion of metric definitions and values of the different bug datasets. Finally, we used a decision tree algorithm to show the capabilities of the dataset in bug prediction.<br> We found that there are statistically significant differences in the values of the original and the newly calculated metrics, furthermore, notations and definitions can severely differ. We compared the bug prediction capabilities of the original and the extended metric suites (within-project learning). Afterwards, we merged all classes (and files) into one large dataset which consists of 47,618 elements (43,744 for files) and we evaluated the bug prediction model build on this large dataset as well. Finally, we also investigated cross-project capabilities of the bug prediction models and datasets. We made the unified dataset publicly available for everyone. By using a public unified dataset as an input for different bug prediction related investigations, researchers can make their studies reproducible, thus able to be validated and verified.</p>
Enhanced Bug Prediction in JavaScript Programs with Hybrid Call-Graph Based Invocation Metrics (Training Dataset)
<p>This dataset consists of multiple files which contain bug prediction training data.</p> <p>The entries in the dataset are JavaScript functions either being buggy or non-buggy. Bug related information was obtained from the project EsLint contained in BugsJS (https://github.com/BugsJS/eslint). The buggy instances were collected throughout the lifetime of the project, however we added non-buggy entries from the latest version which is tagged as fix (entries which were previously included as buggy were not included as non-buggy later on).</p> <p>The dataset is based on hybrid call graphs which are constructed by https://github.com/sed-szeged/hcg-js-framework. The result of this tool is a call graph where the edges are associated with a confidence level which shows how likely the given edge is a valid call edge.</p> <p>We used different threshold values from which we considered the edges to be valid. The following threshold values were used:</p> <ul> <li>0.00</li> <li>0.05</li> <li>0.20</li> <li>0.30</li> </ul> <p>The prefix in the dataset file names are coming from the used threshold. The the datasets include coupling metrics NII (Nubmer of Incoming Invocations) and NOI (Number of Outgoing Invocations) which were calculated by a static source code analyzer called SourceMeter. Hybrid counterparts of these metrics (HNII and HNOI) are based on the given threshold values.</p> <p>There are four variants for all of these datasets:</p> <ul> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics and information about the entries (file without any postfix). Column contained only in this dataset are: <ul> <li>ID</li> <li>Name</li> <li>Longname</li> <li>Parent ID</li> <li>Component ID</li> <li>Path</li> <li>Line</li> <li>Column</li> <li>EndLine</li> <li>EndColumn</li> </ul> </li> <li>Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h+s' postfix)</li> <li>Only static (NII, NOI) coupling metrics are included with additional static source code metrics (file with '_s' postfix)</li> <li>Only hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h' postfix)</li> </ul> <p>Static source code metrics which are contained in all dataset are the following:</p> <ul> <li>McCC - McCabe Cyclomatic Complexity</li> <li>NL - Nesting Level</li> <li>NLE - Nesting Level Else If</li> <li>CD - Comment Density</li> <li>CLOC - Comment Lines of Code</li> <li>DLOC - Documentation Lines of Code</li> <li>TCD - Total Comment Density (Comment Lines in an emedded function will be also considered)</li> <li>TCLOC - Total Comment Lines of Code (Comment Lines in an emedded function will be also considered)</li> <li>LLOC - Logical Lines of Code (Comment and empty lines not counted)</li> <li>LOC - Lines of Code (Comment and empty lines are counted)</li> <li>NOS - Number of Statements</li> <li>NUMPAR - Number of Parameters</li> <li>TLLOC - Logical Lines of Code (Lines in embedded functions are also counted)</li> <li>TLOC - Lines of Code (Lines in embedded functions are also counted)</li> <li>TNOS - Total Number of Statements (Statements in embedded functions are also counted)</li> </ul>
A Static-Based Approach to Detect SQL Semantic Bugs Dataset
<p>The dataset used for our study: A Static-Based Approach to Detect SQL Semantic Bugs.</p> <p>This dataset contains more than 172,000 queries extracted from StackOverflow posts. It was built for analysing the prevalence of semantic bugs in SQL queries.</p> <p>For more information about our study and tools see our GitHub repository: <a href="https://github.com/SERG-Delft/sql-bug-finder">https://github.com/SERG-Delft/sql-bug-finder</a></p> <p>Description of included files:</p> <ul> <li>sql_db.png: database ER diagram</li> <li>homedb_queries.sql: contains queries extracted from StackOverflow posts</li> <li>homedb_questions.sql: contains SQL related question posts extracted from StackOverflow</li> <li>homedb_answers.sql: contains the answers to SQL related question posts extracted from StackOverflow</li> <li>homedb_bugs.sql: contains queries with semantic bugs extracted from StackOverflow posts</li> <li>homedb_owners.sql: contains data related to the owners (users) of SQL StackOverflow posts</li> <li>homedb_pages.sql: artifact from book-keeping script, tracking the StackOverflow pages from which SQL queries were extracted (SQL tagged pages, ordered by votes in descending order)</li> </ul>
Dataset of the paper "Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)"
<p>This dataset contains 3792 images of 26 plant bug (Insecta: Heteroptera: Miridae: Mirini) species used to test the performance of a CNN in species recognition. All jpg files are 1920 pixels on the long size and additionally available as an archive file to facilitate download of the entire dataset. </p> <p>Bar code labels (unique specimen identifiers or USIs) were attached to all examined specimens used for this study. Further information such as additional photographs of habitus and genitalic structures, georeferenced coordinates of each locality, specimens dissected, notes, collecting method can be obtained from the Heteroptera Species Pages (http://research.amnh.org/pbi/heteropteraspeciespage/) which assembles available data from a specimen database and are also provided as an Excel spreadsheet (file _Adelphocoris_CNN_label_data.xlsx).</p>
Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"
<p><br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"</p> <p><br> Description of the content:</p> <p><br> 1) "1_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps </p> <p><br> 2) "2_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: </p> <p><br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 3) "3_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.</p>
Dataset: Global X Cybersecurity ETF (BUG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
UAV bugs dataset and taxonomy
<p>This data set contains 569 real world bugs, 168 UAV-specific bugs and their taxonomy. Our replication package consists of two maim folders:bugSet and bugTaxonomy</p> <p>bugSet</p> <p>In this folder we have placed all the bugs that we have indentified from PX4 and Ardupilot on GitHub. Data collection process is explained in detail in Section 3.1 of the paper. Each line in bugSet.xlsx provides project name and the link of 569 real-world bugs.</p> <p>bugTaxonomy</p> <p>Our manual analysis was conducted in 3 iterations(Section 3.2 in the paper). In each file(3 iterations) we provide the following information: (a) Project name;(b)Bug link; (c)bug label or bug description.</p>
Dataset related to the responses of eggplant plants to Bemisia Tabaci whitefly in presence of one of its natural enemies, the Macrolophus pygmaeus plant bug
<p>This dataset assesses the response of eggplant plants to 3 conditions: noninfested control plants, plants infested by Bemisia tabaci whitefly, and plants infested by Bemisia tabaci where Macrolophus pygmaeus plant bug was also released.</p> <p>The data of the various parameters relating to the responses of the different eggplant plants to the three tested conditions were analyzed using a one-way ANOVA. Where significant differences were detected, the means were separated by Tukey’s HSD test (p < 0.05). Statistical analysis was carried out using the program Statistica (StatSoft, TIBCO Software Inc., Tulsa, OK, USA).</p> <p>The provided dataset was used in the investigation of whether the Macrolophus pygmaeus (Hemiptera: Miridae) plant bug can mitigate the damage caused to plants by the Bemisia tabaci whitefly (Hemiptera: Aleyrodidae). A discussion and interpretation of the data, as well as subsequent results, is provided in the scientific publication <a href="https://zenodo.org/record/7660447#.Y_Soh3aZObg">"Can Macrolophus pygmaeus (Hemiptera: Miridae) Mitigate the Damage Caused to Plants by Bemisia tabaci (Hemiptera: Aleyrodidae)?"</a>, published in the <em><strong><a href="https://www.mdpi.com/2075-4450/14/2/164">Insects</a> </strong></em>journal. </p> <p>This research was conducted within the VIRTIGATION project, which is part of the EU Open Research Data pilot. This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101000570.</p>
Hadoop's Bug-Fixing Tasks Dataset
<p>A dataset with 10,375 bug reports of 10-year bug-tracking information from the Apache Hadoop project modules.</p>
Dataset of real-world WebXR bugs
<p>This is the released dataset of paper <em>An Exploratory Study of Bugs in Extended Reality Applications on the Web </em> to appear in ISSRE 2020.</p> <p>The "WebXR-Bugs-Dataset.zip" includes:</p> <p>(1) A WebXR bug dataset. The dataset contains 368 real bugs from 33 WebXR projects hosted on GitHub, with the final taxonomy with respect to their symptoms and root causes.</p> <p>(2) Materials of the manual analysis process of each round before the final taxonomy was built. The whole process contains 7 rounds.</p> <p>Our dataset is also released at this website: <a href="https://sites.google.com/view/webxr-bug-study/home">https://sites.google.com/view/webxr-bug-study/</a>.</p>
Bug-fix commit script and dataset
<p>This contains the scripts and dataset used in "automatic identification of bug-fixing commits in Python open-source projects" for reproduction purposes. </p>
TSSB-3M: A massive scale dataset of single statement bugs
<p>Datasets created for the paper "TSSB-3M: Mining single statement bugs at massive scale".</p><p>Access to single statement bug fixes at massive scale is not only important for exploring how developers introduce bugs in code and fix them but it is also a valuable ressource for research in data-driven bug detection and automatic repair. Therefore, we are releasing multiple large-scale collections of single statement bug fixes mined from over 500K public Python repositories.</p><p>To facilitate future research, we are releasing three datasets:</p><p><strong>TSSB-3M:</strong> A dataset of over 3 million isolated single statement bug fixes. Each bug fix is related to a commit in a public Python that does not change more than a single statement.</p><p><strong>SSB-9M:</strong> A dataset of over 9 million single statement bug fixes. Each fix modifies at least a single statement to fix a bug. However, the related code changes might incorporate changes to other files.</p><p><strong>SSC-28M:</strong> A dataset of over 28 million general single statement changes. We are releasing this dataset with the intention to faciliate research in software evolution. Therefore, a code change might not necessarily relate to a bug fix.</p><p>In addition, we are releasing two cleaned versions by applying a more aggressive deduplication scheme:</p><p><strong>CTSSB-1M:</strong> A cleaned version of TSSB-3M containing nearly 1 million isolated single statement bug fixes. Each bug fix is related to a commit in a public Python that does not change more than a single statement.</p><p><strong>CSSB-2.6M:</strong> A cleaned version of SSB-9M containing over 2.6 million single statement bug fixes. Each fix modifies at least a single statement to fix a bug. However, the related code changes might incorporate changes to other files.</p><p>Because of concerns regarding the licensing of code, we do not release the original source code related to the single statement code changes. However, our datasets provide enough information to load the original code from the source project. </p><p>All dataset entries are saved in a compressed jsonlines format. Each individual entry provides access to the following information:</p><p>Commit details:</p><ul><li>project: Name of the git project where the commit occurred.</li><li>project_url: URL of project containing the commit</li><li>commit_sha: commit SHA of the code change</li><li>parent_sha: commit SHA of the parent commit</li><li>file_path: File path of the changed source file</li><li>diff: Universal diff describing the change made during the commit</li><li>before: Python statement before commit</li><li>after: Python statement after commit (addresses the same line)</li></ul><p>Commit analysis:</p><ul><li>likely_bug: true if the commit message indicates that the commit is a bug fix. This is heuristically determined.</li><li>comodified: true if the commit modifies more than one statement in a single file (formatting and comments are ignored).</li><li>in_function: true if the changed statement appears inside a Python function</li><li>sstub_pattern: the name of the single statement change pattern the commit can be classified for (if any). Default: SINGLE_STMT</li><li>edit_script: A sequence of AST operation to transform the code before the commit to the code after the commit (includes Insert, Update, Move and Delete operations).</li></ul>
Curated dataset of bug fix commits from "An Empirical Study on Real Bug Fixes"
<p>To cite it:</p> <p><code>@misc{bfdataset,<br> author = {Martin Monperrus},<br> title = {Curated dataset of bug fix commits from "An Empirical Study on Real Bug Fixes"},<br> year = 2017,<br> doi = {10.5281/zenodo.1004734},<br> url = {https://doi.org/10.5281/zenodo.1004734}<br> }</code></p> <pre> </pre> <p> </p>
Dataset for Variability Warning and Bugs Study
<p>This is the dataset for our study of variability warnings and bugs. Our raw data was much too large (461GB) to upload, so we provide our results and the scripts we use to manipulate and process those results.</p>
Datasets for "Learning Realistic Mutations: Bug Creation for Neural Bug Detectors"
<p>This artifact includes the datasets used for Learning Realistic Mutations: Bug Creation for Neural Bug Detectors.</p> <p>Included are preprocessed Java datasets. Using CodeSearchNet as a starting point, the datasets are seeded with bugs of a specific bug type. We distinguish Binary operator bugs, VarMisuse bugs and Function misuses. For each bug type, we employed three level of mutator: weak, strong and contextual.</p> <p>In addition, we also include validation sets, which are used during experiments to validate the bug detection models, but do not relate to experiment results reported in the study.</p> <p>For each bug type, we also included the real world benchmark as test sets.</p> <p>For Python and JavaScript, we include the datasets preprocessed by the contextual mutator.</p>
161 samples from Historical Bug Fixes dataset
<p>The structure of the folders are listed below:</p> <ul> <li>buggy: contains buggy files</li> <li>fault location: contains faulty line numbers</li> <li>fixed: patches written by human</li> </ul>
BugC: C Dataset for Bug Localization
<p>The <strong>BuGC </strong>dataset is <strong>a set of 21 C Projects</strong> extracted from GitHub, assorted for research in <strong>Bug Localization</strong>, in the context of open source development. The dataset consists of 36,617 closed issues. Among these, 2462 issues are closed by pull-requests, which gives us the information of <strong>2462 fixed bug reports</strong>, which can be used for bug localization purposes.</p> <p>For each 21 C projects, there are two file formats, i.e., json and xlsx. These files contain information about the issues and pull-requests for the respective projects. Using these files, bug reports data (issues related to a pull-request) can be extracted, which can be used for the Bug Localization purpose.</p>
Bash in the Wild: Language Usage, Code Smells, and Bugs - Dataset
<p>This is the data set for the paper "Bash in the Wild: Language Usage, Code Smells, and Bugs".</p>
Dataset for ISSTA'22 Understanding Device Integration Bugs in Smart Home System
<p>This is the dataset for the ISSTA`22 submission "Understanding Device Integration Bugs in Smart Home System". It contains 330 device integration bugs collected from the most popular open source SmartHome system, i.e., Home Assistant.</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.