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13 results for “Flaky Tests”
Automatically Reproducing Timing-Dependent Flaky-Test Failures
<p>This artifact contains the source code for FlakeRake, a tool for automatically reproducing timing-dependent flaky-test failures. It also includes raw and processed results produced in the evaluation of FlakeRake</p> <p> </p> <p>Contents:</p> <p> </p> <p>Timing-related APIs that FlakeRake considers adding sleeps at: timing-related-apis</p> <p>Anonymized code for FlakeRake (not runnable in its anonymized state, but included for reference; we will publicly release the non-anonymized code under an open source license pending double-blind review): flakerake.tgz</p> <p>Failure messages extracted from the FlakeFlagger dataset: 10k_reruns_failures_by_test.csv.gz </p> <p>Output from running isolated reruns on each flaky test in the FlakeFlager dataset: 10k_isolated_reruns_all_results.csv.gz (all test results summarized into a CSV), 10k_isolated_reruns_failures_by_test.csv.gz (CSV including just test failures, including failure messages), 10k_isolated_reruns_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven)</p> <p>Output from running the FlakeFlagger replication study (non-isolated 10k reruns):flakeFlaggerReplResults.csv.gz (all test results summarized into a CSV), 10k_reruns_failures_by_test.csv.gz (CSV including just failures, including failure messages), flakeFlaggerRepl_raw_results.tgz (includes all raw results from reruns, including the XML files output by maven - this file is markedly larger than the 10k isolated reruns results because we ran *all* tests in this experiment, whereas the 10k isolated rerun experiment only re-ran the tests that were known to be flaky from the FlakeFlagger dataset).</p> <p>Output from running FlakeRake on each flaky test in the FlakeFlagger dataset:</p> <p>For bisection mode: results-bis.tgz</p> <p>For one-by-one mode: results-obo.tgz</p> <p>Scripts used to execute FlakeRake using an HPC cluster: execution-scripts.tgz<br> Scripts used to execute rerun experiments using an HPC cluster: flakeFlaggerReplScripts.tgz<br> Scripts used to parse the "raw" maven test result XML files in this artifact into the CSV files contained in this artifact: parseSurefireXMLs.tgz </p> <p>Output from running FlakeRake in “reproduction” mode, attempting to reproduce each of the failures that matched the FlakeFlagger dataset (collected for bisection mode only): results-repro-bis.tgz</p> <p>Analysis of timing-dependent API calls in the failure inducing configurations that matched FlakeFlagger failures: bis-sleepyline.cause-to-matched-fail-configs-found.csv</p>
An Empirical Study of Flaky Tests in Python
<p><a href="https://arxiv.org/pdf/2101.09077.pdf">Paper</a></p> <p><a href="https://github.com/se2p/FlaPy">Flapy (our test execution tool)</a></p> <p>TestsOverview.csv<br> - Project Columns: Project_Name, Project_URL, Project_Hash<br> - Test Columns: Test_filename, Test_classname, Test_funcname, Test_parametrization<br> - Flaky_sameOrder_withinIteration: the test is non-order-dependent flaky<br> - Order-dependent: the test is order-dependent flaky<br> - Flaky_Infrastructure: the test shows infrastructure flakiness</p>
Flaky Test Dataset to Accompany "FlakeFlagger: Predicting Flakiness Without Rerunning Tests"
<p>When developers make changes to their code, they typically run regression tests to detect if their recent changes (re)introduce any bugs. However, many tests are flaky, and their outcomes can change non-deterministically, failing without apparent cause. Flaky tests are a significant nuisance in the development process, since they make it more difficult for developers to trust the outcome of their tests. The traditional approach to identify flaky tests is to rerun them multiple times: if a test is observed both passing and failing on the same code, it is definitely flaky. We conducted a very large empirical study looking for flaky tests by rerunning the test suites of 24 projects 10,000 times each, and found that even with this many reruns, some flaky tests were still not detected. We propose FlakeFlagger, a novel approach that collects a set of features describing the behavior of each test, and then predicts tests that are likely to be flaky based on similar behavioral features. We found that FlakeFlagger correctly labeled at least as many tests as flaky as a state-of-the-art flaky test classifier, but that FlakeFlagger reported far fewer false positives (an increase in precision from just 11% to 60%). This lower false positive rate translates directly to saved time for researchers and developers who use the classification result to guide more expensive flaky test detection processes. By investigating the information gain of each feature, we conclude that test execution time, overall test coverage, coverage of recently changed lines and usage of third party libraries are effective predictors of test flakiness. We did not find any keywords or tokens in the source code of tests that were effective in predicting test flakiness, and did not find the presence of test smells to be effective in predicting test flakiness.</p> <p>This archive contains the dataset that we collected of flaky tests, along with the features that we collected from each test.</p> <p>Contents:<br> Project_Info.csv: List of projects and their revisions studied<br> build-logs-<project-slug>.tgz: An archive of all of the maven build logs from each of the 10,000 runs of that project's test suite. <br> failing-test-reports-<project-slug>.tgz An archive of all of the surefire XML reports for each failing test of each build of each project.<br> test_results.csv: Summary of the number of passing and failing runs for each test in each project. <br> "Run ID" is a key into the <project-slug>.tgz archive also in this artifact, which refers to the run that we observed the test fail on.<br> test_features.csv: Summary of the features that each test had, as per our feature detectors described in the paper<br> flakeflagger-code.zip: All scripts used to generate and process these results. These scripts are also located at https://github.com/AlshammariA/FlakeFlagger</p>
Supplemental Material: What we talk about when we talk about software test flakiness
<p>This supplemental material details the definitions of the concepts that have been found by conducting the scoping review of both the white and grey literature introduced in Section 2 of the manuscript titled: <strong>"What we talk about when we talk about software test flakiness</strong>".</p> <p> </p>
Regression-Test History Data for Flaky Test-Research, Dataset
<p>The dataset comprises developer test results of Maven projects with flaky tests across a range of consecutive commits from the projects' git commit histories. The Maven projects are a subset of those investigated in an <a href="https://doi.org/10.1145/3428270">OOPSLA 2020 paper</a>. The commit range for this dataset has been chosen as the flakiness-introducing commit (FIC) and iDFlakies-commit (see the OOPSLA paper for details). The commit hashes have been obtained from the <a href="https://github.com/TestingResearchIllinois/idoft/blob/main/tic-fic-data.csv">IDoFT dataset</a>.</p> <p>The dataset will be presented at the <a href="https://conf.researchr.org/home/icse-2024/ftw-2024">1st International Flaky Tests Workshop 2024 (FTW 2024)</a>. Please refer to <a href="https://doi.org/10.1145/3643656.3643901">our extended abstract</a> for more details about the motivation for and context of this dataset.</p> <p>The following table provides a summary of the data.</p> <table> <tbody> <tr> <td><strong>Slug (Module)</strong></td> <td><strong>FIC Hash</strong></td> <td><strong>Tests</strong></td> <td><strong>Commits</strong></td> <td><strong>Av. Commits/Test</strong></td> <td><strong>Flaky Tests</strong></td> <td><strong>Tests w/ Consistent Failures</strong></td> <td><strong>Total Distinct Histories</strong></td> </tr> <tr> <td>TooTallNate/Java-WebSocket</td> <td> 822d40</td> <td>146</td> <td> 75</td> <td> 75</td> <td>24</td> <td> 1</td> <td>2.6x10^9</td> </tr> <tr> <td>apereo/java-cas-client (cas-client-core)</td> <td> 5e3655</td> <td>157</td> <td> 65</td> <td>61.7</td> <td> 3</td> <td> 2</td> <td>1.0x10^7</td> </tr> <tr> <td>eclipse-ee4j/tyrus (tests/e2e/standard-config)</td> <td> ce3b8c</td> <td>185</td> <td> 16</td> <td> 16</td> <td>12</td> <td> 0</td> <td> 261</td> </tr> <tr> <td>feroult/yawp (yawp-testing/yawp-testing-appengine)</td> <td> abae17</td> <td> 1</td> <td>191</td> <td>191</td> <td> 1</td> <td> 1</td> <td> 8</td> </tr> <tr> <td>fluent/fluent-logger-java</td> <td> 5fd463</td> <td> 19</td> <td>131</td> <td>105.6</td> <td>11</td> <td> 2</td> <td>8.0x10^32</td> </tr> <tr> <td>fluent/fluent-logger-java</td> <td> 87e957</td> <td> 19</td> <td>160</td> <td>122.4</td> <td>11</td> <td> 3</td> <td>2.1x10^31</td> </tr> <tr> <td>javadelight/delight-nashorn-sandbox</td> <td> d0d651</td> <td> 81</td> <td>113</td> <td>100.6</td> <td> 2</td> <td> 5</td> <td>4.2x10^10</td> </tr> <tr> <td>javadelight/delight-nashorn-sandbox</td> <td> d19eee</td> <td> 81</td> <td> 93</td> <td>83.5</td> <td> 1</td> <td> 5</td> <td>2.6x10^9</td> </tr> <tr> <td>sonatype-nexus-community/nexus-repository-helm</td> <td> 5517c8</td> <td> 18</td> <td> 32</td> <td> 32</td> <td> 0</td> <td> 0</td> <td> 18</td> </tr> <tr> <td>spotify/helios (helios-services)</td> <td> 23260</td> <td>190</td> <td>448</td> <td>448</td> <td> 0</td> <td> 37</td> <td> 190</td> </tr> <tr> <td>spotify/helios (helios-testing)</td> <td> 78a864</td> <td> 43</td> <td>474</td> <td>474</td> <td> 0</td> <td> 7</td> <td> 43</td> </tr> </tbody> </table> <p> </p> <p>The columns are composed of the following variables:</p> <ul> <li><strong>Slug (Module):</strong> The project's GitHub slug (i.e., the project's URL is https://github.com/{Slug}) and, if specified, the module for which tests have been executed.</li> <li><strong>FIC Hash:</strong> The flakiness-introducing commit hash for a known flaky test as described in this OOPSLA 2020 paper. As different flaky tests have different FIC hashes, there may be multiple rows for the same slug/module with different FIC hashes. </li> <li><strong>Tests:</strong> The number of distinct test class and method combinations over the entire considered commit range.</li> <li><strong>Commits:</strong> The number of commits in the considered commit range</li> <li><strong>Av. Commits/Test:</strong> The average number of commits per test class and method combination in the considered commit range. The number of commits may vary for each test class, as some tests may be added or removed within the considered commit range.</li> <li><strong>Flaky Tests:</strong> The number of distinct test class and method combinations that have more than one test result (passed/skipped/error/failure + exception type, if any + assertion message, if any) across 30 repeated test suite executions on at least one commit in the considered commit range.</li> <li><strong>Tests w/ Consistent Failures:</strong> The number of distinct test class and method combinations that have the same error or failure result (error/failure + exception type, if any + assertion message, if any) across all 30 repeated test suite executions on at least one commit in the considered commit range.</li> <li><strong>Total Distinct Histories:</strong> The number of distinct test results (passed/skipped/error/failure + exception type, if any + assertion message, if any) for all test class and method combinations along all commits for that test in the considered commit range.</li> </ul>
Dataset of UI-based Flaky Tests
<p><strong>This artifact contains the dataset used in the paper accepted in the ICSE 2021 Technical Track #565, “An Empirical Analysis of UI-based Flaky Tests”. The dataset contains 235 samples of flaky UI tests composed of 152 samples from web projects and 83 samples from mobile projects. </strong></p>
Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests – Replication Package
<p>This is the replication package associated with the paper: <em>Flakify: A Black-Box, Language Model-based Predictor for Flaky Tests.</em> We explain how to use it to reproduce the results reported in the paper. A maintainable version of this replication package is available on GitHub (<a href="https://github.com/uOttawa-Nanda-Lab/Flakify">https://github.com/uOttawa-Nanda-Lab/Flakify</a>).</p> <p><strong>Flakify Test Smell Detector</strong></p> <p>This is a step-by-step guideline to detect test smells in the source code of test cases and retain statements that match them.</p> <p><em><strong>Requirements:</strong></em></p> <ul> <li>Eclipse IDE (the version we used was 2021-12)</li> <li>The libraries (the <strong><em>.jar</em></strong> files in the <strong><code>lib\</code></strong> directory)</li> </ul> <p><em><strong>Input Files:</strong></em></p> <p>This is a list of input files that are required to accomplish this step:</p> <ul> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</em></p> </li> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_class_files/</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_filtered_dataset.csv</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_class_files/</em></p> </li> </ul> <p>The <strong><code>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</code></strong> and <strong><code>dataset/IDoFT/IDoFT_filtered_dataset.csv</code></strong> are used to obtain the label (<em>flaky</em>=1 or <em>non-flaky</em>=0) and project name for each test case parsed from <strong><code>dataset/FlakeFlagger/FlakeFlagger_class_files/</code></strong> and <strong><code>dataset/IDoFT/IDoFT_class_files/</code></strong>, respectively.</p> <p><strong><em>Output Files:</em></strong></p> <ul> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_dataset.csv</em></p> </li> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_test_cases_full_code/</em></p> </li> <li> <p><em>dataset/FlakeFlagger/FlakeFlagger_test_cases_preprocessed_code/</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_dataset.csv</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_test_cases_full_code/</em></p> </li> <li> <p><em>dataset/IDoFT/IDoFT_test_cases_preprocessed_code/</em></p> </li> </ul> <p> </p> <p><strong>Replicating the experiment</strong></p> <p>To detect test smells and retain only code statements related to them, the <strong><code>src/FlakifySmellsDetector.java</code></strong> file should be compiled and run using the Eclipse IDE by having all the <em>.jar</em> files in the classpath.</p> <p>The pre-generated executable Jar file <strong><code>src/FlakifySmellsDetector.jar</code></strong> can be executed using the shell script <strong><code>src/FlakifySmellsDetector.sh</code></strong> after changing paths for each dataset as needed, using the following commands:</p> <pre><code class="language-bash">bash FlakifySmellsDetector.sh FlakeFlagger bash FlakifySmellsDetector.sh IDoFT</code></pre> <p>It will generate the dataset required to run Flakify's flaky test prediction model for the datasets given as input. The class file containing each of the test cases is then parsed to produce the corresponding full code and pre-processed code of the test case. The full and pre-processed source code of all test cases are also combined and saved in a CSV file, along with test smells found, project names, and labels.</p> <p> </p> <p><strong>Flakify Replication</strong></p> <p>This is the guideline for replicating the experiments we used to evaluate Flakify for classifying test cases as <em>flaky</em> and <em>non-flaky</em> using both cross-validation and per-project validation.</p> <p><em><strong>Requirements:</strong></em></p> <p>This is a list of all required python packages:</p> <ul> <li><em>python =3.8.5</em></li> <li><em>imbalanced_learn= 0.8.1</em></li> <li><em>numpy= 1.19.5</em></li> <li><em>pandas= 1.3.3</em></li> <li><em>transformer= 4.10.2</em></li> <li><em>torch=1.5.0</em></li> <li><em>scikit_learn= 0.22.1</em></li> </ul> <p><em><strong>Input Files:</strong></em></p> <p>This is a list of input files that are required to accomplish this step:</p> <ul> <li><em>dataset/FlakeFlagger/Flakify_FlakeFlagger_dataset.csv</em></li> <li><em>dataset/IDoFT/Flakify_IDoFT_dataset.csv</em></li> </ul> <p>This file contains the full code and pre-processed code of the test cases in both FlakeFlagger and IDOFT datasets, along with their ground truth labels (<em>flaky</em> and <em>non-flaky</em>).</p> <p><em><strong>Output File:</strong></em></p> <ul> <li> <p><em>results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_model_trained_on_FlakeFlagger_dataset.pt</em></p> </li> <li> <p><em>results/Flakify_cross_validation_results_on_IDoFT_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_per_project_results_on_IDoFT_dataset.csv</em></p> </li> <li> <p><em>results/Flakify_model_trained_on_IDoFT_dataset.pt</em></p> </li> </ul> <p> </p> <p><strong>Replicating Flakify experiments</strong></p> <p><strong>Cross-Validation</strong></p> <p>To run the Flakify experiment using cross-validation on the two datasets, navigate to <code>src\</code> folder and run the following commands:</p> <pre><code class="language-bash">bash Flakify_predictor_cross_validation.sh FlakeFlagger bash Flakify_predictor_cross_validation.sh IDoFT</code></pre> <p>This will generate the classification results into <strong><code>results/Flakify_cross_validation_results_on_FlakeFlagger_dataset.csv</code></strong> and <strong><code>results/Flakify_cross_validation_results_on_IDoFT_dataset.csv</code></strong> for the cross-validation experiments on both datasets. It will also save the weights of the two models trained on the FlakeFlagger and IDoFT datasets into <strong><code>results/Flakify_model_trained_on_FlakeFlagger_dataset.pt</code></strong> and <code><strong>results/Flakify_model_trained_on_IDoFT_dataset.pt</strong></code>, respectively.</p> <p> </p> <p><strong>Per-project Validation</strong></p> <p>To run the Flakify experiment using per-project validation on the two datasets, navigate to <code>src\</code> folder and run the following commands:</p> <pre><code class="language-bash">bash Flakify_predictor_per_project.sh FlakeFlagger bash Flakify_predictor_per_project.sh IDoFT</code></pre> <p>This will generate the classification results into <strong><code>results/Flakify_per_project_results_on_FlakeFlagger_dataset.csv</code></strong> and <strong><code>results/Flakify_per_project_results_on_IDoFT_dataset.csv</code></strong> for the whole per-project validation experiments on both datasets.</p> <p> </p> <p><strong>FlakeFlagger Replication</strong></p> <p>This is the guideline for replicating the experiments we used to evaluate the two versions of FlakeFlagger, white-box and black-box, for classifying test cases as <em>flaky</em> and <em>non-flaky</em> using cross-validation on the FlakeFlagger dataset.</p> <p><em><strong>Requirements:</strong></em></p> <p>This is a list of all required python packages:</p> <ul> <li><em>python =3.8.5</em></li> <li><em>imbalanced_learn= 0.8.1</em></li> <li><em>pandas= 1.3.3</em></li> <li><em>scikit_learn= 0.22.1</em></li> </ul> <p><em><strong>Input File:</strong></em></p> <p>This is a list of input files that are required to accomplish this step:</p> <ul> <li><em>dataset/FlakeFlagger/FlakeFlagger_filtered_dataset.csv</em></li> <li><em>dataset/FlakeFlagger/FlakeFlaggerFeaturesTypes.csv</em></li> <li><em>dataset/FlakeFlagger/Information_gain_per_feature.csv</em></li> </ul> <p><em><strong>Output File:</strong></em></p> <ul> <li><em>results/FlakeFlagger_black-box_results.csv</em></li> <li><em>results/FlakeFlagger_white-box_results.csv</em></li> </ul> <p> </p> <p><strong>Replicating FlakeFlagger experiments</strong></p> <p>To run the FlakeFlagger experiments, navigate to <code>src\</code> folder and run the following command:</p> <pre><code class="language-bash">bash FlakeFlagger_predictor.sh white-box bash FlakeFlagger_predictor.sh black-box</code></pre> <p>This will generate the classification results into <strong><code>results/FlakeFlagger_white-box_results.csv</code> </strong>and <strong><code>results/FlakeFlagger_black-box_results.csv</code> </strong>for both white-box and black-box experiments, respectively.</p>
The Effects of Computational Resources on Flaky Tests (Artifact)
<p>This artifact contains resources for reproducing and extending the work "The Effects of Computational Resources on Flaky Tests"</p><p>Contents:</p><ul><li> Analysis and Processed Test Results.tgz: An archive that contains information about the projects analyzed, summarized test results per-throttling configuration per-run, and a Jupyter notebook that detects RAFT (generating all tables and figures in the article). A README in this archive provides further guidance on its contents</li><li>js-results.tar, java-results.tar, python-results.tar: The raw results produced by the test runner when executing each JavaScript, Java and Python project 300 times in each of the throttling configurations.</li></ul><p>See also:</p><p>We have published docker containers that include each project that we studied, along with all of the dependenices for running the tests. These containers can be used to reproduce our results, or to extend our work by running additional tests. The containers are available at <a href="https://hub.docker.com/r/jonbell/raft/tags">https://hub.docker.com/r/jonbell/raft/tags</a></p>
Flaky Test Prediction using GraphCode2Vec
<p><a href="https://zenodo.org/api/files/28bc0577-f62a-4ecc-9bd4-86e6036d7c72/Flakify_baseline.tar.gz">Flakify_baseline.tar.gz</a> is the baseline code from https://github.com/uOttawa-Nanda-Lab/Flakify , Please cite their work and it is not an official tar file. We put it here because we have to make sure they use the same dataset. They finetune CodeBERT 200 epochs in total</p> <p><a href="https://zenodo.org/api/files/28bc0577-f62a-4ecc-9bd4-86e6036d7c72/data.tar.gz">data.tar.gz</a> is the dataset for GraphCode2Vec and please decompress it in the root folder of the project.</p> <p><a href="https://zenodo.org/api/files/28bc0577-f62a-4ecc-9bd4-86e6036d7c72/results_grid_search.tar.gz">results_grid_search.tar.gz</a> is the grid search results.</p> <p>GraphCode2Vec for Flaky, <a href="https://github.com/Marvinmw/Flaky">https://github.com/Marvinmw/Flaky</a> </p>
The dataset of DOM events interactions flakiness issues in web tests
Open the record for dataset details and reuse information.
dataset of time-based repair of async wait flaky tests
<p>This is the dataset of time-based repair of async wait flaky tests in web testing</p>
Flaky tests in quantum programs
<p>This is a spreadsheet that includes 46 flaky tests that we found in existing quantum programs. We include 1) reports of flakiness, 2) pull requests to fix flakiness, 3) categories of flakiness, and 4) categories of fixes.</p> <p>To follow the double-blind review policy, we submit it as anonymous.</p>
The repository of the SANER'21 paper titled "On the Impact of Flaky Tests in Automated Program Repair"
<p>This repository contains the artefact of the paper “On the Impact of Flaky Tests in Automated Program Repair” under review by SANER2021.</p> <ul> <li>results: <ul> <li><strong>RQ1:</strong> This table contains all the statistical data of flaky tests we find out from Defects4J benchmark where each line illustrates the commit date, commit id, java file name, as well as all the flaky methods in this test file. Note that the commit id is empty if there is no update about the java file in that line since last version. <ul> <li>flaky_methods.xlsx</li> </ul> </li> <li><strong>RQ2:</strong> Here are the execution results of 10 times running of flaky tests under 4 different environment. Detailed execution log of each running are classified by project and named in the format <strong><em>test_log_[project_name].txt</em></strong>,the summarized data of each project are named in the format <strong><em>Flaky_test_[project_name].xlsx</em></strong>. The <strong><em>Flaky_test_all.xlsx</em></strong> contain the summarized data for all projects. <ul> <li>jdk1.7 + ubuntu16.04</li> <li>jdk1.7 + ubuntu18.04</li> <li>jdk1.8 + ubuntu16.04</li> <li>jdk1.8 + ubuntu18.04</li> </ul> </li> <li><strong>RQ3:</strong> This folder contains execution results of the fault localization tool <a href="https://github.com/GZoltar/gzoltar">GZoltar-V1.7</a> under two jdk versions. Result of each bug is named in the format <strong><em>[project_name]_[bug_id].csv</em></strong>. <ul> <li>gzoltar_reuslt_flaky_jdk1.7</li> <li>gzoltar_reuslt_flaky_jdk1.8</li> <li>gzoltar_reuslt_noFlaky_jdk1.7</li> <li>gzoltar_reuslt_noFlaky_jdk1.8</li> </ul> </li> <li><strong>RQ4:</strong> This folder contains repair results of different APR tools. For the 10 tools from <a href="https://github.com/program-repair/RepairThemAll">RepairThemAll</a> framework, each result is named in format <strong><em>validation_result_[APR_name].txt</em></strong> which indicates whether the previous patch can be generated this time. For <a href="https://github.com/SerVal-DTF/TBar">TBar</a>, we release all the generated patches including <em>fixed</em> and <em>partially fixed</em>. <ul> <li>RepairThemAll</li> <li>TBar</li> </ul> </li> </ul> </li> </ul>
ScienceDex guides
Understand access before you commit
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.