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324 results for “test cases”
Prioritizing test cases with Markov Chains: a Preliminary Investigation
<p>This replication package includes the inputs to replicate the results shown in a paper under revision. Data is structured in four folders, one for each considered case study (there is an initial example besides the three case studies reported in Section 5). Within each folder, it is possible to inspect the input files as well as the results, for both proposed heuristics, i.e., H1 and H2. The input formats can be consulted to understanding and replicating the study. The dataset also contains the algorithm written in Python.</p>
On the importance of additional behavioral observation in behavioral psychopharmacology research: a case study on agomelatine's effects on feedback sensitivity in probabilistic reversal learning test in rats (data&code)
<p>Dataset to the manuscript entitled: On the importance of additional behavioral observation in behavioral psychopharmacology research: a case study on agomelatine's effects on feedback sensitivity in probabilistic reversal learning test in rats. Code in R used for analysis data from probabilistic reversal learning (PRL) test performed in operant conditioning boxes. Script analyzes data files from MED-PC IV software.</p>
FD Simulator test parameters and results from NYC case study
<p>This repository gives the dataset for Fleet-Demand platform (FD platform). The full FD platform code description can be found <a href="https://github.com/BUILTNYU/Fleet-Demand-Platform/tree/main">here</a>.</p> <p>The files are under two categories:</p> <p>1. MATSim input file:</p> <p> "config-with-mode-vehicles-drt-tele_2_income.xml": configuration file defining input file names and parameters for MATSim in the initial cycle of FD platform cycle (cycle 0).</p> <p> "config-with-mode-vehicles-drt-tele_2_income_updated.xml": configuration file defining input file names and parameters for MATSim in FD platform cycles after the initial cycle.</p> <p> "population_large_two_drt_added.xml": initial trip plan containing 35000 agents with initially assigned ride-pooling services ("drt_1" and "drt_2"). The trip plan was extracted from the MATSim-NYC population file, which can be found here.</p> <p> "new_subpopulation_income_onefifth.xml": subpopulation file assigning agent to either "Low_Income" or "Not_Low_Income". The value of time (VOT) of the two groups are defined in the configuration file</p> <p> "separated-network_shrunk_clean.xml": Network file containing road and transit network of Manhatta, Brooklyn, and Queens. This network file is extracted from the MATSim-NYC network file.</p> <p>2. DVRPT input file</p> <p> "nyc_sub_nodes_update.csv": File defining the nodes that constructs the ride-pooling service area.</p> <p> "nyc_sub_network_update_new_1_27_max.csv": File defining the shortest path time cost for all node pairs.</p> <p> "nyc_sub_stops.csv": File defining the stops of the ride-pooling service.</p> <p> "nyc_sub_links.csv": File defining the links of the ride-pooling service area.</p>
Test cases for the Geospatial Probabilistic Estimation Package (GPEP)
<p>The dataset contains test cases for the Geospatial Probabilistic Estimation Package (GPEP, https://github.com/NCAR/GPEP) and relevant publications. The two current test cases are based on prior implementations for GMET version 2.0 (https://github.com/NCAR/GMET and https://zenodo.org/records/5498408) for applications, and include:</p><ul><li>a GMET version 2.0 test case of daily meteorological forcing generation at 1/16th degree resolution for February 2017 for a region of the US Southwest encompassing California, US (Bunn et al, 2022).</li><li>a test case for daily meteorological forcing generation at 0.01 degree resolution from 1970-2022 for a smaller region of the US Southwest encompassing the upper Colorado River basin and Colorado Front Range.</li></ul><p>The associated meteorological station forcings and gridded parameter inputs were developed for water resources research projects at NCAR (PI A. Wood) sponsored by the United States Army Corps of Engineers Climate Preparedness and Resilience Program and the United States Bureau of Reclamation Science and Technology Program. </p><p>References:</p><p>Bunn, PTW, AW Wood, AJ Newman, H Chang, CL Castro, MP Clark and JR Arnold, 2022, Improving station-based ensemble surface meteorological analyses using numerical weather prediction: A case study of the Oroville Dam crisis precipitation event. <i>J. Hydromet.</i> 23(7), 1155-1169. https://doi-org.cuucar.idm.oclc.org/10.1175/JHM-D-21-0193.1<br> </p>
Data and Code Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"
<p>Code and Data Supplement for "A Mountain-Induced Moist Baroclinic Wave Test Case for the Dynamical Cores of Atmospheric General Circulation Models"<br> ===========================================================</p> <p>This directory contains the data and scripts used to create the plots from our publication as well as the source<br> code modifications necessary to run this test case within the CESM and MPAS models.</p> <p>Generating Plots<br> ---------------</p> <p>The `netcdf` directory contains the nominal half-degree runs necessary to generate nearly all of the plots from the paper. The one plot which is not reproducible from these data is the volume-integrated Eddy Kinetic Energy in the Spectral Element model. Storing high-resolution 4D wind fields requires a prohibitive amount of space. These data can be provided by the corresponding author, O.K. Hughes (owhughes@umich.edu). However, because this is several hundred GB of data I would strongly recommend generating these high-resolution runs yourself on your local system if you need them. Using 288 Intel Skylake cores (that is, 8 nodes each with two 18C processors) ran on the order of an hour.</p> <p><em>In order to generate the plots from the paper, you need only install NCL and then run</em> run.bash. Instructions for installing NCL<br> can be found in the `run.bash` script.</p> <p>Source Code Modifications<br> ----------------</p> <p><strong>CESM</strong><br> The `src` subdirectory contains the files `user_nl_cam` and `ic_baroclinic.F90`. Create a case using `--compset=FKESSLER` and `--run-unsupported` options when running `create_newcase`. If your case is located at `${CASE_DIR}`, then from within the directory containing this README, run `cp user_nl_cam ${CASE_DIR}/user_nl_cam`, and then run `cp ic_baroclinic.F90 ${CASE_DIR}/SourceMods/src.cam/`. Then build and run the model using the usual workflow.</p> <p><strong>MPAS</strong></p> <p>The MPAS code was run using a branch of the MPAS model provided by the model developers to the authors. While the source code modifications are provided in the `src` directory, I would strongly recommend contacting the corresponding author if you wish to run this test case in the MPAS codebase.</p>
Data from: Encoding laboratory testing data: case studies of the national implementation of HHS requirements and related standards in five laboratories
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Using inbreeding to test the contribution of non-additive genetic effects to additive genetic variance: A case study in Drosophila serrata
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Data from: DCMIP2016: The tropical cyclone test case
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Morphometry of the pedipalp patella provides new characters for species-level taxonomy in whip spiders (Arachnida, Amblypygi): A test case with description of a new species of Phrynus
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Data from: The influence of locus number and information content on species delimitation: an empirical test case in an endangered Mexican salamander
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Metabarcoding for biodiversity inventory blind spots: A test case using the beetle fauna of an insular cloud forest
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Identifying diagnostic genetic markers for a cryptic invasive agricultural pest: a test case using the apple maggot fly, Rhagoletis pomonella (Diptera: Tephritidae)
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Does Unit-Tested Code Crash? A Case Study of Eclipse: Replication Package
<p><strong>Does Unit-Tested Code Crash? A Case Study of Eclipse: Replication Package</strong></p> <p>This is a replication package associated with the paper titled “Does Unit-Tested Code Crash? A Case Study of Eclipse”. Below is a description of the package’s contents.</p> <p><strong>Data</strong></p> <p>Data files associated with the paper are provided in the <code>data</code> directory.</p> <p><strong>Text file <code>tested-crashed.txt</code></strong></p> <p>Data specifying whether methods were tested and whether they crashed (according to the criteria adopted in the study). Extracted from <code>matches.xlsx</code>. The data are used as input for Fisher’s test (RQ1).</p> <p><strong>Spreadsheet <code>matches.xlsx</code></strong></p> <p>Test coverage data and calculations associated with failed methods, class coverage, and matching method coverage results are provided in an Excel spreadsheet. Below is the description of the spreadsheet’s contents.</p> <p>Worksheet <em>Test Coverage</em></p> <p>Contains the data regarding the JaCoCo test code coverage analysis.</p> <ul> <li>Class: The name of the class in which a method appears in JVM internal form notation</li> <li>Method: The method’s name</li> <li>Parameters: The method’s arguments in JVM parameter descriptor format; required to handle Java’s {} polymporphism</li> <li>Class Has Unit Test: Whether the corresponding class has associated unit test code</li> <li>Class Unit-Test Line Density: The ratio of lines in class’s test code over those in the class’s implementation code</li> <li>Covered Instructions / Branches / Lines: As reported by JaCoCo</li> <li>Total Instructions / Branches / Lines: As reported by JaCoCo</li> <li>Covered Instructions / Branches / Lines ratio: The ratio between the two preceding values; 1 for methods without any branches</li> <li>Top-1 / Top-6 / Top-10 : In how many stack traces the method appears within; the top-10 / top-6 / the very first stack frame(s)</li> <li>Tested: TRUE if the method is considered tested by having a test code coverage above the median (0.966) and an associated test class</li> <li>Crashed: TRUE if the method has crashed as evidenced by its appearance on the topmost stack frame</li> <li>Stack trace file names: in which the method appeared</li> </ul> <p>Worksheet <em>Test Existence</em></p> <p>Contains the data of the analysis regarding the existence of test code.</p> <ul> <li>Class: Class containing implementation code</li> <li>TestClassNames: Classes that contain tests for the above</li> <li>Number of relevant tests</li> <li>Lines in class test code</li> <li>Lines of class</li> <li>Class Unit-Test Line Density: The ratio between the two above</li> </ul> <p>Worksheet <em>Metrics</em></p> <p>Contains the derivation of metrics reported in the paper. In the cases of tables these are formatted in LaTeX for direct incorporation into the text.</p> <p><strong>Spreadsheet <code>jacoco.xlsx</code></strong></p> <p>Complete test coverage data obtained from JaCoCo are provided in an Excel spreadsheet. Below is the description of the spreadsheet’s contents.</p> <p>Worksheet <em>Data</em></p> <p>Contains the following method code coverage fields as reported by JaCoCo, as well as the calculated percentages.</p> <ul> <li>Class</li> <li>Method</li> <li>Parameters</li> <li>Covered Instructions</li> <li>Total Instructions</li> <li>% Covered Instructions</li> <li>Covered Branches</li> <li>Total Branches</li> <li>% Covered Branches</li> <li>Covered Lines</li> <li>Total Lines</li> <li>% Covered Lines</li> </ul> <p>Worksheet <em>Metrics</em></p> <p>Contains the derivation of numbers reported in the preliminary quantitative analysis and Figure 2.</p> <p>Compressed tar archive <code>eclipse-src.tar.gz</code></p> <p>Contains the Eclipse source code used for running the Eclipse tests with JaCoCo code coverage analysis. It was obtained from the Eclipse source code repositories as follows.</p> <ul> <li>Clone the Eclipse aggreagator repository into a directory named z by running: <code>git clone -b master --recursive git://git.eclipse.org/gitroot/platform/eclipse.platform.releng.aggregator.git z</code></li> <li>In the <code>z</code> directory, checking out the used release by running <code>cd z && git submodule foreach git checkout M20160212-1500</code></li> <li>Checking out the release for the main repository by running: <code>git checkout M20160212-1500</code></li> <li>Applying the patch <code>eclipse-src.diff</code></li> </ul> <p><strong>Patch file <code>eclipse-src.diff</code></strong></p> <p>See above.</p> <p><strong>Zip file <code>incidents.zip</code></strong></p> <p>Contains the 126,026 incidents (crash report stack traces and meta-data) associated with <em>EclipseProduct</em> <code>org.eclipse.epp.package.java.product</code> and <em>BuildID</em> <code>4.5.2.M20160212-1500</code>. This is a subset from the two million incidents available as the <a href="http://software-data.org/datasets/aeri-stacktraces/downloads/incidents_full.tar.bz2">AERI stack traces data set</a>.</p> <p>The subset of incidents was extracted from the full AERI data set with the following command.</p> <pre><code class="language-bash">for f in *; do grep -q org.eclipse.epp.package.java.product $f && grep -q 4.5.2.M20160212-1500 $f && mv $f selected-files/ done</code></pre> <p><br> <strong>Compressed file <code>jacoco.xml.gz</code></strong></p> <p>Contains the results of the JaCoCo code coverage analysis over the Eclipse testing.</p> <p><strong>Code</strong></p> <p>The following scripts are provided in the <code>src</code> directory</p> <ul> <li><code>extract.py</code>: script for extracting crash (incidents) and coverage (JaCoCo) data</li> <li><code>unit-tested-classes.py</code>: script for finding the classes with associated unit test code</li> <li><code>merge.py</code>: script for matching crash (incidents) with coverage (JaCoCo) data</li> <li><code>fisher.r</code>: R script for running Fisher’s test</li> </ul>
An Approach for Traceability Recovery between Bug Reports and Test Cases
<p>Scripts and data sets used in research for <em>An Approach for Traceability Recovery between Bug Reports and Test Cases</em>.</p> <p><em><strong>(Context)</strong></em> Automatic traceability recovery between software artifacts may promote early detection of issues.<br> Information Retrieval (IR) techniques have been proposed for the task, but they differ considerably in terms of input parameters and results. It is difficult to assess results when those techniques are applied in isolation, usually in small or medium-sized software projects. Also, an overview would be more comprehensive if a Deep Learning (DL) based technique is applied, in comparison with traditional IR techniques.<br> <em><strong>(Objective)</strong></em> We propose an approach to recover traceability links between bug reports and test cases, which can be instantiated with a set of IR and DL techniques.<br> <em><strong>(Method)</strong></em> For applying and evaluating our solution, we used historical data from the Mozilla Firefox quality assurance (QA) team, on which we assessed the following IR techniques: LSI, LDA, and BM25. We also experimented with a DL architecture called Convolutional Neural Networks (CNNs) through the use of Word Embeddings.<br> <em><strong>(Results)</strong></em> In this context of traceability, we noticed poor performances from three out of the four studied techniques. Only the LSI technique was effective, even standing out over the state-of-the-art BM25 technique.<br> <em><strong>(Conclusions)</strong></em> The obtained results suggest that the semi-automatic application of the LSI technique -- with an appropriate combination of thresholds -- is feasible for real-world software projects.</p>
Supplementary material 6 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S6. Average nucleotide diversity for all four datasets of shared OTUs seperated according to sample sites and EPT (Ephemeroptera, Plecoptera, Trichoptera) and PR ('Pollution Resistant') taxa
Supplementary material 5 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S5. Average haplotype diversity for all four datasets of shared OTUs seperated according to sample sites and EPT (Ephemeroptera, Plecoptera, Trichoptera) and PR ('Pollution Resistant') taxa
Supplementary material 7 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S7 – part 1. Haplotype network of the two most frequent EPT (Ephemeroptera, Plecoptera, Trichoptera) and PR ('Pollution Resistant') taxa
Supplementary material 4 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S4. Average haplotype number per OTU for the four different datasets of shared OTUs. Datasets are split into EPT (Ephemeroptera, Plecoptera, Trichoptera) and PR ('Pollution Resistant') taxa
Supplementary material 3 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S3. Average haplotype number per OTU for the four different datasets of shared OTUs. Values are illustrated for all sample sites including all shared OTUs
Supplementary material 2 from: Zizka VMA, Weiss M, Leese F (2020) Can metabarcoding resolve intraspecific genetic diversity changes to environmental stressors? A test case using river macrozoobenthos. Metabarcoding and Metagenomics 4: e51925. https://doi.org/10.3897/mbmg.4.51925
Figure S2. Four different datasets including shared OTUs between the different river systems (Emscher-Ennepe-Sieg, Emscher-Ennepe, Emscher-Sieg, Sieg-Ennepe). Number of OTUs is illustrated with taxonomic assignment on order level
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.