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324 results for “test cases”
SBML Test Suite Stochastic Test Cases 3.2.0
<p>The SBML Test Suite is a conformance testing system. It allows developers and users to test the degree and correctness of the SBML support provided in a software package. A core part of the SBML Test Suite is the collection of test cases. There are 3 sets of tests: <strong>semantic</strong> (for deterministic simulation behavior), <strong>stochastic</strong>(for stochastic simulation behavior), and <strong>syntactic</strong> (for basic parsing).</p> <p>This is the version 3.2.0 release of the <strong>stochastic</strong> test cases archive.</p> <p>For more information about SBML and the SBML Test Suite, please visit http://sbml.org.</p>
Example configurations and test cases for the Python HDF5Translator framework.
<p>This is a set of use examples for the <a href="https://github.com/BAMresearch/HDF5Translator">HDF5Translator framework</a>. This framework lets you translate measurement files into a different (e.g. NeXus-compatible) structure, with some optional checks and conversions on the way. For an in-depth look at what it does<a href="https://lookingatnothing.com/?p=4087">, there is a blog post here. </a></p> <p>The use examples provided herein are each accompanied by the measurement data necessary to test and replicate the conversion. The README.md files in each example show the steps necessary to do the conversion for each. </p> <p>We encourage those who have used or adapted one or more of these exampes to create their own conversion, to get in touch with us so we may add your example to the set. </p>
The Role of Sacks Sentence Completion Test in a Couple with Borderline Personality Disorder and Narcissistic Personality Disorder: A Case Study
<p> </p> <p><span>Abstract</span></p> <p><span>Background: A voluminous amount of material has been written on individual diagnostic indicators of borderline disorder and narcissistic personality disorder from a psychopathological perspective. However, very few have explored when a narcissist and borderline personality forms a bond or a marriage link. This research aims to show that personality psychopathology is primarily interpersonal at its foundation, and it can be well reflected using Sacks sentence completion test.</span></p> <p><span>Methodology: A Case study of a couple with borderline personality disorder and narcissistic personality disorder from a psychiatric hospital OPD at Jaipur. <span> </span>International personality disorder examination (IPDE) DSM IV, diagnostic interview schedule and Sacks sentence completion test have been administered. The couple clinical report was qualitatively interpreted using content analysis and </span><span>phenomenological approach.</span><span> </span></p> <p><span>Results indicated that couple with borderline personality disorder and narcissistic personality disorder leads to dysfunctional adult relationship. </span></p> <p><span>Conclusion: Lack of secure base from parental figures in childhood is reflected in terms connecting with the partner. </span></p>
mCRL2 models, requirements and test logs for the EULYNX Point interface case study
<p>mCRL2 models and mu-calculus formulas for the EULYNX Point Interface. Models and requirements are made in the context of the FormaSig project. Data is made available for replication purposes.</p> <p>REQ_P_001, REQ_P_001_1 and REQ_P_002 are requirements for the point specific mCRL2 model point_spec.mcrl2</p> <p>Remaining .mcf files are requirements for the generic PDI interface pdi_spec.mcrl2</p> <p>Artifacts relating to testing are:</p> <ul> <li>An mCRL2 model, mbt.mcrl2</li> <li>A rename file to rename internal actions to tau, rename_file.re</li> <li>Partial state space associated to mbt.mcrl2, partial_state_space.aut</li> <li>The weak-trace bisim reduced version of the state space, partial_state_space_reduced.aut</li> <li>Testing logs, test-logs.zip</li> <li>The source code of the simulator and the testing tools, Simulator code.zip</li> </ul>
CView: test case dataset
<p><strong>Summary</strong></p> <p>All North American subtype B gp120 sequences, verified to be CCR5-using sequences (n = 636), were downloaded from the<a href="https://www.hiv.lanl.gov/content/sequence/HIV/mainpage.html"> Los Alamos HIV sequence database</a> in aligned fasta format [1]. These were loaded into <a href="https://sourceforge.net/projects/cview/">CView</a>, using the “Load (fasta)” option of the “All Sequences” menu, following which they were saved in unaligned format using the “Save (unaligned)” option of the “All Sequences” menu. Additionally, the titles of these sequences were saved to a separate file using the “Save (titles)” option. This was repeated for CXCR4-using sequences (n = 76). In order to make sites directly comparable between the two sets of unaligned sequences, they were combined into a single file and aligned using MUSCLE [2].</p> <p>This alignment was used in the test case example of our CView paper [3] titled “<em>CView: A network based tool for enhanced alignment visualization</em>” <a href="https://doi.org/10.1371/JOURNAL.PONE.0259726">(view)</a>.</p> <p>The CView project page is located at: <a href="https://sourceforge.net/projects/cview/">https://sourceforge.net/projects/cview/</a></p> <p>A poster sumarization presented at <a href="http://www.iscb.org/ismb2022">ISMB 2022</a> is available <a href="https://doi.org/10.5281/zenodo.6798044">here</a>.</p> <p> </p> <p><strong>Tutorials</strong></p> <p>1. A demonstration of CView usage in the form of an mp4 movie is available <a href="https://zenodo.org/record/6514787">here</a>.</p> <p>2. A demonstration of how variants can be identified using CView is available <a href="https://zenodo.org/record/6514972">here</a>.</p> <p> </p> <p>Related software to this project are:<br> 1. <a href="http://sourceforge.net/projects/cstone/">CStone</a> <br> 2. <a href="http://sourceforge.net/projects/csreadgen/">CSReadGen</a><br> 3. <a href="https://sourceforge.net/projects/cview/">CView</a> <<br> 4. <a href="https://sourceforge.net/projects/chimsim/">ChimSim</a><br> 5. <a href="https://sourceforge.net/projects/tvscript/">TVScript</a></p> <p> </p> <p>General details of the project are available <a href="https://cibio.up.pt/en/projects/de-novo-based-sequence-assembly-of-next-generation-sequence-data-without-chimeras-improved-annotation-gene-expression-profiles-and-haplotype-br-reconstruction/">here</a>.</p> <p> </p> <p><strong>References</strong></p> <p>1. Kuiken C, Korber B, Shafer RW. HIV Sequence Databases. AIDS Rev. 2003;5: 52. Available: /pmc/articles/PMC2613779/</p> <p>2. Edgar R. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32: 1792–1797. doi:10.1093/NAR/GKH340</p> <p>3. Linheiro, R., Sabatino, S., LoboID, D., & ArcherID, J. (2022). CView: A network based tool for enhanced alignment visualization. <em>PLOS ONE</em>, <em>17</em>(6), e0259726. https://doi.org/10.1371/JOURNAL.PONE.0259726</p> <p> </p> <p><strong>| linkedIn . web . orcid |</strong><br> | <a href="https://www.linkedin.com/in/raquel-linheiro-50883873/">linheiro</a> . web . <a href="https://orcid.org/0000-0003-2659-0910">orcid</a> |---| sabatino . <a href="https://cibio.up.pt/en/people/details/stephen-joseph-sabatino/">web</a> . <a href="https://orcid.org/0000-0003-2815-2375">orcid</a> |---| <a href="https://www.linkedin.com/in/diana-lobo-6aa63328/">lobo</a> . <a href="https://cibio.up.pt/en/people/details/diana-lobo/">web</a> . <a href="https://orcid.org/0000-0001-6988-9993">orcid</a> |---| <a href="https://www.linkedin.com/in/john-archer-7b872872">archer</a> . <a href="https://cibio.up.pt/en/people/details/john-archer/">web</a> . <a href="https://orcid.org/0000-0001-6212-0962">orcid</a> |</p> <p> </p>
Data from: Encoding laboratory testing data: case studies of the national implementation of HHS requirements and related standards in five laboratories
<p><strong>Objective</strong>: Assess the effectiveness of providing Logical Observation Identifiers Names and Codes (LOINC®)-to-In Vitro Diagnostic (LIVD) coding specification, required by the United States Department of Health and Human Services for SARS-CoV-2 reporting, in medical center laboratories and utilize findings to inform future United States Food and Drug Administration policy on the use of real-world evidence in regulatory decisions.</p> <p><strong>Materials and Methods</strong>: We compared gaps and similarities between diagnostic test manufacturers' recommended LOINC® codes and the LOINC® codes used in medical center laboratories for the same tests.</p> <p><strong>Results</strong>: Five medical centers and three test manufacturers extracted data from laboratory information systems (LIS) for prioritized tests of interest. The data submission ranged from 74 to 532 LOINC® codes per site. Three test manufacturers submitted 15 LIVD catalogs representing 26 distinct devices, 6956 tests, and 686 LOINC® codes. We identified mismatches in how medical centers use LOINC® to encode laboratory tests compared to how test manufacturers encode the same laboratory tests. Of 331 tests available in the LIVD files, 136 (41%) were represented by a mismatched LOINC® code by the medical centers (chi-square 45.0, 4 df, P &lt; .0001).</p> <p><strong>Discussion</strong>: The five medical centers and three test manufacturers vary in how they organize, categorize, and store LIS catalog information. This variation impacts data quality and interoperability. </p> <p><strong>Conclusion</strong>: The results of the study indicate that providing the LIVD mappings was not sufficient to support laboratory data interoperability. National implementation of LIVD and further efforts to promote laboratory interoperability will require a more comprehensive effort and continuing evaluation and quality control.</p>
Supplementary materials of the paper entitled: "Metamorphic Testing Meets Regression Testing: A Case Study of Scientific Software Maintenance"
<p>Supplementary materials of the paper entitled:</p> <p>“Metamorphic Testing Meets Regression Testing: A Case Study of Scientific Software Maintenance”</p> <p>file01 - source code for detecting and comparing output relations of a given metamorphic relation<br> file02 - interview transcripts and qualitative coding results<br> file03 - system script for compiling SWMM, creating sSWMM simulation outputs, and logging execution time<br> file04 - source code for SWMM 5.1.014<br> file05 - source code for SWMM 5.1.015<br> file06 - a suite of 40 regression tests (40 .inp files)<br> file07 - result table for 760 cases (19 MRs × 40 .inp files)<br> file08 - 491 automatically generated follow-up .inp files via reuse<br> file09 - result table for mutation analysis<br> file10 - output files for mutation analysis with metamorphic testing:<br> a. Original source output<br> b. Original follow-up output<br> c. Mutant source output<br> d. Mutant follow-up output</p>
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
<p class="MsoNormal"><span>Whip spiders (Amblypygi Thorell, 1883) are easily distinguished from their spider relatives (Araneae Clerck, 1757) by their raptorial pedipalps, the second pair of appendages, which are covered in spines and used to grasp and impale prey. Among other characters, the number, distribution, and size differences among the pedipalp spines continue to be important for whip spider systematics at different levels in the taxonomic hierarchy. However, traditional meristic and qualitative characters used to describe the pedipalp spines often limit their use for species-level taxonomy, as character states are invariant or overlapping. The present study employs landmark-based morphometrics to map the morphospace comprising the retrolateral surface of the pedipalp patella and its dorsal spines in a group of Neotropical whip spiders of the genus <em>Phrynus</em> Lamarck, 1801, all of which share an inconspicuous spine on the prolateral surface of the pedipalp tarsus, i.e., "group B" proposed by Quintero (1983). The analysis presented demonstrates that the relative pedipalp patella lengths and spine to pedipalp patella width ratios are of similar use for species diagnosis as the commonly employed relative length of leg femur I. Importantly, the analysis reveals that, beyond the traditional approach of qualitatively reporting relative pedipalp patella dorsal spine lengths (e.g., P3 > P5 > P2 > P4 > P6 > P1), the ratios between spine lengths are useful morphometric characters for species-level taxonomy in <em>Phrynus</em>. New morphometric ratios are provided for the species of <em>Phrynus</em> group B and used, together with traditional characters, to describe <em>Phrynus guarionexi</em> sp. nov. from the Dominican Republic. Finally, new morphological data are provided concerning the cerotegument, frontal process and prolateral surface of the pedipalp tibia in the species of group B.</span></p>
Metabarcoding for biodiversity inventory blind spots: A test case using the beetle fauna of an insular cloud forest
<p>Soils harbour a rich arthropod fauna, but many species are still not formally described (Linnaean shortfall), and the distribution of those already described is poorly understood (Wallacean shortfall). Metabarcoding holds much promise to fill this gap, however, nuclear copies of mitochondrial genes, and other artefacts lead to taxonomic inflation, which compromises the reliability of biodiversity inventories. Here we explore the potential of a bioinformatic approach to jointly "denoise" and filter non-authentic mitochondrial sequences from metabarcode reads to obtain reliable soil beetle inventories and address open questions in soil biodiversity research, such as the scale of dispersal constraints in different soil layers. We sampled cloud forest arthropod communities from 49 sites in the Anaga peninsula of Tenerife (Canary Islands). We performed whole organism community DNA (wocDNA) metabarcoding, and built a local reference database with COI barcode sequences of 310 species of Coleoptera for filtering reads and the identification of metabarcoded species. This resulted in reliable haplotype data after considerably reducing nuclear mitochondrial copies and other artefacts. Comparing our results with previous beetle inventories, we found: (i) new species records, potentially representing undescribed species; (ii) new distribution records, and; (iii) validated phylogeographic structure when compared with traditional sequencing approaches. Analyses also revealed evidence for higher dispersal constraint within deeper soil beetle communities, compared to those closer to the surface. The combined power of barcoding and metabarcoding contribute to mitigate the important shortfalls associated with soil arthropod diversity data, and thus address unresolved questions for this vast biodiversity fraction.</p>
MPAS-Albany Land Ice v6.0 Test Cases
<p>This archive includes test case for the MPAS-Albany Land Ice v6.0 model.</p> <p>See http://mpas-dev.github.io/ for links to model source code and User's Guide.</p>
Enhanced Synthetic Texas 2,000 bus (ACTIVSg2000) test case w/synthesized dynamic parameters of wind generation and protective devices
<p>Enhanced Synthetic Texas 2,000 bus (ACTIVSg2000) test case w/synthesized dynamic parameters of wind generation and protective devices<br> Compatible with PowerWorld v21, adapted from ACTIVSg2000.pwb (<a href="https://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg2000/">https://electricgrids.engr.tamu.edu/electric-grid-test-cases/activsg2000/</a>)<br> <br> Used as a test case for the paper: `Electrical Grid Resilience Metric with Uncertainty.' Power Systems Computation Conference, under review. </p> <p>Wind Dynamic Parameters:<br> Machine models:<br> -REGC_A: Renewable Energy Generator/Converter.</p> <p> Machine governors:<br> -WTGT_A: Wind Turbine Generator Drive-train Model.</p> <p> Machine excitation system:<br> -REEC_A: Renewable Energy Electrical Control Model.</p> <p>Protective relays:<br> -DLSH: Rate of Frequency Load Shedding Model (used on all loads in network).<br> -LVSH: Undervoltage Load Shedding Model (used on all loads in network).<br> -TIOCR1: Time inverse overcurrent relay (used on all lines in network).<br> -FRQTPAT: Under frequency generator trip relay (used on all machines).<br> -LHVRT: Low/High Voltage Ride through Generator Protection (used on all machines).</p> <p> </p> <p>This research was completed by Idaho National Laboratory with funding<br> from the U.S. Department of Energy Wind Energy Technologies Office. Idaho National Laboratory is operated<br> by Battelle Energy Alliance under contract No. DE AC07-05ID14517.</p>
MITgcm idealized test-case : cyclonic gyre in a mid-latitude closed basin
<p>Model grid data and hydrodynamic output files of the Z-coordinate Massachusetts Institute of Technology general circulation model (MITgcm) for an idealized case-study describing a cyclonic gyre in a mid-latitude closed basin.<br> <br> The domain size is (Ni,Nj,Nk)=(256,256,60).<br> <br> The grid files (Computational_[...]) contain single precision variables (4 bytes), while the hydrodynamic output files contain variables stored in double precision (8 bytes).<br> </p>
Supplementary data for "Enhancing Resource-based Test Case Generation For RESTful APIs with SQL Handling"
<p>Supplement to: <em>Enhancing Resource-based Test Case Generation For RESTful APIs with SQL Handling</em></p> <p>In this repository, we provide <em>tests</em> and their <em>coverage reports</em> (conducted by Intellij coverage reports) that are used in the Discussion section of the paper.</p>
CSV-format data for: Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7
<p>This is a supplementary upload to <a href="https://zenodo.org/record/4579857">https://zenodo.org/record/4579857</a>.</p> <p>This upload provides the same anonymised individual-level SARS-CoV-2 testing data for England as that earlier upload provided, but provides it in CSV format (comma-separated values) instead of in the previous QS format. The QS format requires specialised software (e.g. the <strong>qs</strong> package for R) to read, so I am providing the data in CSV format to facilitate access to and re-use of the data.</p> <p>Please see the original data upload, <a href="https://zenodo.org/record/4579857">https://zenodo.org/record/4579857</a>, the associated journal article, <a href="https://www.nature.com/articles/s41586-021-03426-1">Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7</a>, and the project's Github repository, <a href="https://github.com/nicholasdavies/cfrvoc">https://github.com/nicholasdavies/cfrvoc</a>, for full details.</p>
Fault test cases for series compensated transmission line with capacitor at line terminal
<p>This data set contains time series data of different simulated fault cases in high voltage series compensated transmission lines. The simulation is done in PSCAD using the Python automation library. The capacitor is located at one of the line terminals with CT and VT. The modelled line is 500 kV and 200 km long. The simulation is run for 1 second with 10 kHz sampling rate. </p> <p>Each file contains 7 data in the following order, </p> <ol> <li>Time</li> <li>Voltage phase A</li> <li>Voltage phase B</li> <li>Voltage phase C</li> <li>Current phase A</li> <li>Current phase B</li> <li>Current phase C</li> </ol> <p>The variables used to study different test cases are shown below,</p> <ul> <li>Fault inception angle (phase A): 0 and 90 degree</li> <li>Source impedance ratio: 0.20 and 0.35</li> <li>Capacitor level: 94 µF(50%), 78 µF(60%), 67 µF(70%)</li> <li>Fault resistance: 0.10, 10, 20, 30 ohm</li> <li>Fault location: 10 km reverse, 10 km, 50 km, 100 km, 125 km, 150 km, 155 km, 160 km. 165 km, 170 km, 180 km, 190 km</li> <li>Fault type: No-fault, AG, BG, CG, ABG, CAG, BCG, ABCG, AB, CA, BC, ABC</li> </ul> <p>All these factors lead to 6912 test cases in total. The test cases are organized in four (4) zipped folders each containing 1728 test cases. The PSCAD model file and an example python script that can be used to automate the simulations are also included herewith for anyone interested to replicate the results. </p> <p>The name of the folder contains the information regarding fault inception angle and source impedance ratio for all test cases inside that folder, for example, the folder name <strong>Outputs_0_0.20 </strong>can be decoded as,</p> <p>0: Fault inception angle (phase A)<br> 0.20: Source impedance ration</p> <p>The files names inside the folders include information about the other variables used in generating the test case. For example, the name <strong>test_f_10_0_67_0.01 </strong>can be interpreted as,</p> <p>test_f: f for forward fault, r for reverse fault<br> 10: fault location, 10 km<br> 0: Fault type<br> 67: Compensation level<br> 0.01: Fault resistance</p> <p>Fault types are interpreted as in PSCAD.</p> <p>0 = No-Fault,<br> 1 = Phase A to Ground<br> 2 = Phase B to Ground<br> 3 = Phase C to Ground<br> 4 = Phase AB to Ground<br> 5 = Phase AC to Ground<br> 6 = Phase BC to Ground<br> 7 = Phase ABC to Ground<br> 8 = Phase AB<br> 9 = Phase AC<br> 10 = Phase BC<br> 11 = Phase ABC</p> <p>The different line parameters used in the PSCAD model is as below,</p> <p><strong>Conductor</strong></p> <p>Type: Chukar<br> Geometric mean radius: 20.345 mm<br> DC resistance: 0.0318Ω/km<br> Strands: 84<br> Strand radius: 1.8491 mm<br> Relative permeability: 1.0<br> Sag: 12 m<br> Height: 22 m<br> Sub-conductor: 2<br> Sub-conductor spacing: 0.4572 m<br> Ground wire number: 2<br> Radius: 5.5245 mm<br> DC resistance: 2.8645Ω/km<br> Relative permeability: 1.0<br> Sag: 10 m<br> Height: 10 m</p> <p><strong>Impedance</strong></p> <p>Positive sequence0.018 +j0.339 Ω/km<br> Zero sequence0.266 +j1.017 Ω/km</p> <p> </p> <p><strong>These simulations were run for assessing time-domain protections. Therefore, the parallel CB protection across the series capacitor is disabled, as one can safely assume even with the highest current levels, the CB will take at least 20 ms to operate. This is higher than the time limits for time-domain protection. </strong></p>
Same Side Stance Classification Adversarial Test Cases
<p>This dataset contains 175 cases. It is manually created to reveal the ability of models to solve different types of <strong>adversarial</strong> cases for same side stance predictions more systematically. The examples selected here are derived from the dataset used in the <a href="https://sameside.webis.de/">Same Side Stance Classification</a> shared task.</p> <p>We have selected <strong>25</strong> distinct arguments from the <em>"gay marriage"</em> topic that are short and express their stance clearly. For each selected argument, we construct new arguments of four distinct types to obtain two pairs, one with the same stance, and one with an opposing stance:</p> <ul> <li><strong>Negation</strong>: a simple negation of the argument.</li> <li><strong>Paraphrase</strong>: alters important words from the argument to synonymous expressions with the same stance.</li> <li><strong>Argument</strong>: uses an argument from the same topic and stance, but semantically completely different regarding the first one.</li> <li><strong>Citation</strong>: repeats or summarizes the first argument and then expresses agreement or rejection (a case frequently occurring in the dataset).</li> </ul> <p>The types <em>Paraphrase</em>, <em>Argument</em>, and <em>Citation</em> are also formulated in a negated version to create additional test instances for the opposite stance.</p>
ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search – Replication Package
<p>This is the replication package associated with the paper "<em>ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search</em>" accepted at the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023) – Technical Track. Cite this paper using the following:</p> <p><em>@inproceedings{pan2023atm,<br> title={ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search},<br> author={Pan, Rongqi and Ghaleb, Taher A. and Briand, Lionel},<br> booktitle={Proceedings of the 45th IEEE/ACM International Conference on Software Engineering},<br> year={2023},<br> pages={1--12}<br> }</em></p> <p><strong>Replication Package Contents:</strong><br> The replication package contains all the necessary data and code required to reproduce the results reported in the paper. We also provide the results for other minimization budgets, and detailed <em>FDR,</em> execution time, and statistical test results. In addition, we provide the data and code required to reproduce the results of baselines techniques: FAST-R and random minimization.</p> <p><strong>Data:</strong><br> We provide in the <em><strong>Data</strong></em> directory the data used in our experiments, which is based on 16 projects from <a href="https://github.com/rjust/defects4j">Defects4J</a>, whose characteristics can be found in <em><strong>Data/subject_projects.csv</strong></em><em>.</em></p> <p><strong>Code:</strong><br> We provide in the <em><strong>Code</strong></em> directory the code and scripts (Java, Python, and Bash) required to run the experiments and reproduce the results.</p> <p><strong>Results:</strong><br> We provide in the <em><strong>Results</strong></em> directory the results for each technique independently, and also a summary of all results together for comparison purposes. The source code for this step is in the <em><strong>Code/ATM/CodeToAST</strong></em> directory. The source code for this step is in the <em><strong>Code/ATM/Similarity</strong></em> directory.</p> <p><strong>_________________________________</strong></p> <p><strong>ATM - Code to AST transformation:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory)</p> <p><strong>Input:</strong><br> All zipped data files should be unzipped before running each step.<br> * Data/test_suites/all_test_cases.zip → Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases.zip → Data/test_suites/changed_test_cases<br> * Data/test_suites/relevant_test_cases.zip → Data/test_suites/relevant_test_cases</p> <p><strong>Output:</strong><br> * Data/ATM/ASTs/all_test_cases<br> * Data/ATM/ASTs/changed_test_cases</p> <p><strong>Running the experiment:</strong><br> To generate ASTS for all test cases in the project test suites, the <em><strong>Code/ATM/CodeToAST/src/CodeToAST.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/CodeToAST/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/CodeToAST bash transform_code_to_ast.sh</code></pre> <p>Each test file in the <em><strong>Data/test_suites/all_test_cases</strong></em> and <em><strong>Data/test_suites/changed_test_cases</strong></em> directories is parsed to generate a corresponding AST for each test case method (saved in an XML format in <strong>Data/ATM/ASTs/all_test_cases</strong> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> for each project version)<br> <strong>_________________________________</strong></p> <p><strong>ATM - Similarity Measurement:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em><strong> </strong>files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory)<br> <br> <strong>Input:</strong><br> * Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases<br> <br> <strong>Output:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Running the experiment:</strong><br> To measure the similarity between each pair of test cases, the <em><strong>Code/ATM/Similarity/src/SimilarityMeasurement.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/Similarity/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Similarity bash measure_similarity.sh</code></pre> <p>ASTs of each project in the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directories are parsed to create pairs of ASTs containing one test case from the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> directory with another test case from the <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directory (redundant pairs are discarded). Then, all similarity measurements are saved in the <em><strong>Data/ATM/similarity_measurements.zip</strong></em> file.<br> __________________________________________</p> <p><strong>Search-based Minimization Algorithms:</strong><br> The source code for this step is in the <em><strong>Code/ATM/Search</strong></em> directory.<br> <br> <strong>Requirements:</strong><br> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <strong>Code/AMT/Search/requirements.txt</strong> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Search pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Output:</strong><br> * Results/ATM/minimization_results<br> <br> <strong>Running the experiment:</strong><br> To minimize the test suites in our dataset, the following bash script should be executed:</p> <pre><code class="language-bash">bash minimize.sh</code></pre> <p>All similarity measurements are parsed for each version of the projects, independently. Each version is run 10 times using three minimization budgets (25%, 50%, and 75%). Genetic Algorithm (GA) is run using four similarity measures, namely top-down, bottom-up, combined, and tree edit distance. NSGA-II is run using two combinations of similarity measures: top-down & bottom-up and combined & tree edit distance. The minimization results are generated in the <em><strong>Results/ATM/minimization_results</strong></em> directory.<br> __________________</p> <p><strong>Evaluate results:</strong><br> To evaluate and summarize the minimization results, run the following:</p> <pre><code class="language-bash">cd Code/ATM/Evaluation bash evaluate.sh</code></pre> <p>This will generate summarized <em>FDR</em> and execution time results (per-project and per-version) for each minimization budget, which can all be found in <strong>Results/ATM</strong>. In this replication package, we provide the final, merged <em>FDR</em> with execution time results.</p> <p><strong>_________________________________</strong></p> <p><strong>Running FAST-R experiments</strong><br> ATM was compared to <a href="https://github.com/ICSE19-FAST-R/FAST-R">FAST-R</a>, a state-of-the-art baseline, which is a set of test case minimization techniques called: <em>FAST++, FAST-CS, FAST-pw, and FAST-all</em>, which we adapted to our data and experimental setup.</p> <p><strong>Requirements:</strong><br> To run this step, Python 3.7 is required. Also, the libraries in the <em><strong>Code/FAST-R/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/FAST-R pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/FAST-R/test_methods<br> * Data/FAST-R/test_classes</p> <p><strong>Output:</strong><br> * Results/FAST-R/test_methods/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> * Results/FAST-R/test_classes/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run FAST-R experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash fast_r.sh test_methods #method level bash fast_r.sh test_classes #class level</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong> in the <em><strong>Results/FAST-R/test_methods</strong></em> and <em><strong>Results/FAST-R/test_classes</strong></em> directories.</p> <p><strong>_________________________________</strong></p> <p><strong>Running the random minimization experiments</strong><br> ATM was also compared to random minimization as a standard baseline.</p> <p><strong>Requirements:</strong> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <em><strong>Code/RandomMinimization/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/RandomMinimization pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> <em>N/A</em></p> <p><strong>Output:</strong><br> * Results/RandomMinimization/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run the random selection experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash random_minimization.sh</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <em><strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong></em> in the <em><strong>Results/RandomMinimization</strong></em> directory.</p>
Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"
<p>Supplementary data for "Testing the efficacy of different molecular tools for parasite conservation genetics: a case study using horsehair worms (Phylum Nematomorpha)"</p> <p>alignments: alignments used for BEAST ("bayes") and PopArt ("popart"). The "popart" folder also has a traits file per each species.</p> <p>bayesian_plots: TSVs ("tsv") and PDF files ("ogs") generated by BEAST. The "tsv" folder also has the scripts for plotting the results in R.</p> <p>easysfs: scripts, population file and results from the VCF to SFS conversione done by easySFS.</p> <p>fineRADstructure: fineRADstructure input files and output PDF plots ("plots") for <em>C. formosanus</em> ipyrad and Stacks ("stacks") data. </p> <p>logs: logs for ipyrad, ModelTest, PGDspider, PopArt ("popart") and Stacks ("stacks"). The "popart" folder also have the generated networks in a TXT file. The "stacks" folder also has ODS files for calculating the amount of loci per each M/n fixed value.</p> <p>snapclust: STR files used with R for snapclust. Scripts included.</p> <p>stairway_plot: input (blueprint files) and outputs for Stairway Plot 2 analyses. The <em>C. formosanus</em> folder ("chordodes") also has scripts for R plotting.</p> <p>vcfs: VCF and HDF5 files used in this study. Also scripts for filtering/converting data and plotting the PCA with ipyrad (activate python first!) for <em>C. formosanus</em>.</p> <p>"acutogordius" = <em>A. taiwanensis</em><br> "chordodes" = <em>C. formosanus</em><br> "gordius" = <em>G. chiashanus</em></p>
Using inbreeding to test the contribution of non-additive genetic effects to additive genetic variance: A case study in Drosophila serrata
<p>Additive genetic variance, <em>V<sub>A</sub></em>, is the key parameter for predicting adaptive and neutral phenotypic evolution. Changes in demography (e.g., increased close-relative inbreeding) can alter <em>V<sub>A</sub></em>, but how depends on the, typically unknown, gene action and allele frequencies across many loci. For example, <em>V<sub>A</sub></em> increases proportionally with the inbreeding coefficient when allelic effects are additive, but larger (or smaller) increases can occur when allele frequencies are unequal at causal loci with dominance effects. Here, we describe an experimental approach to assess the potential for rare, recessive alleles to inflate <em>V<sub>A</sub></em> under inbreeding. Applying a powerful paired pedigree design in <em>Drosophila serrata</em>, we measured 11 wing traits on half-sibling families bred via either random or sibling mating, differing only in homozygosity (not allele frequency). Despite close inbreeding and substantial power to detect small <em>V<sub>A</sub></em>, we detected no deviation from the expected additive effect of inbreeding on genetic (co)variances. Our results suggest the average dominance coefficient is very small relative to the additive effect, or that allele frequencies are relatively equal at loci affecting wing traits. We outline the further opportunities for this paired pedigree approach to reveal the characteristics of <em>V<sub>A</sub></em>, providing insight into historical selection and future evolutionary potential.</p>
An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test Cases
<p>This is the code and dataset that accompanies the study: "<strong>An Exploratory Study on the Usage and Readability of Messages Within Assertion Methods of Test Cases</strong>." This study has been accepted for publication at the 2023 International Workshop on Natural Language-based Software Engineering.</p> <p><strong><em>Following is the abstract of the study:</em></strong></p> <p>Unit testing is a vital part of the software development process and involves developers writing code to verify or assert production code. Furthermore, to help comprehend the test case and troubleshoot issues, developers have the option to provide a message that explains the reason for the assertion failure. In this exploratory empirical study, we examine the characteristics of assertion messages contained in the test methods in 20 open-source Java systems. Our findings show that while developers rarely utilize the option of supplying a message, those who do, either compose it of only string literals, identifiers, or a combination of both types. Using standard English readability measuring techniques, we observe that a beginner's knowledge of English is required to understand messages containing only identifiers, while a 4th-grade education level is required to understand messages composed of string literals. We also discuss shortcomings with using such readability measuring techniques and common anti-patterns in assert message construction. We envision our results incorporated into code quality tools that appraise the understandability of assertion messages. </p>
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.