Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
102
datasets available to search
ShareScore release 0.9.0
Dataset results
102 results for “Software Testing”
Reducing the Allocation of Software Testing Demand: A Study on the Pros and Cons using STELA Tool
<p><strong>Qualitative Research</strong></p> <p>1. How long have you been on the testing team?<br><br>2. How long have you been assigning requests?<br><br>3. Do you encounter difficulties in the manual assignment process?<br><br>4. If yes, which ones?<br><br>5. Do you use automated acceptance with STELA support?<br>6. If not, why?<br><br>7. What main differences do you see between the acceptance and distribution of requests using automated acceptance with STELA support and the manual process?<br><br>8. What advantages do you see in automated acceptance with the support of STELA?<br><br>9. What disadvantages do you see in automated acceptance with STELA support?<br><br>10. How do you feel about using automated acceptance within the STELA tool?<br><br>11. What are the differences between STELA and the system used previously?</p>
Example datasets for testing the GWASTic software
<p>The first dataset, called <strong>barley_set</strong>, contains barley data to validate the peak associations of two-row barley genes previously identified in Milner, Jost, and Taketa (2019). To replicate the Genome-Wide Association Study (GWAS) results, please use the pre-filtered and formatted genotypic file ‘WGS300_005_0020.bed’. The corresponding phenotype data for row type, pre-formatted for direct use with the GWAStic software, is available in ‘bridge_row_type_GWAS.txt’. To reproduce the genomic prediction experiments, please use the same files: ‘WGS300_005_0020.bed’ for the genotypic data and ‘bridge_row_type_GP.txt’ for the phenotypes. The file ‘validation_set.txt’ contains a set of 30 genotypes that have been excluded from the training data for use as a validation set.</p> <p>Additionally, a minimalistic dataset called <strong>small_dataset</strong> is provided to facilitate quick testing of the GWAStic software. This dataset includes:</p> <ul> <li>‘example.vcf.gz’ to test the VCF to BED conversion.</li> <li>‘example.bed’, a filtered genotypic file ready for use.</li> <li>‘pheno_gwas.csv’ as a phenotypic file for GWAS.</li> <li>‘pheno_gp.csv’ as a phenotypic file for genomic prediction.</li> </ul> <p>We generated two synthetic datasets using PLINK software, one with binary and one with quantitative phenotypes. Each synthetic dataset contains 2,000 samples—1,000 cases and 1,000 controls—with a total of 90,010 SNPs. For the dataset with binary phenotypes (called <strong>synthetic_binary</strong>), the SNPs were categorized into three groups: nullA, nullB, and nullC, each containing 30,000 SNPs not associated with the disease phenotype. Additionally, we included 5 SNPs labeled diseaseA and 5 labeled diseaseB, designed to mimic disease-associated loci. The diseaseA SNPs had allele frequencies between 0.1 and 0.2, with a relative risk of 2.5 under a multiplicative model, while the diseaseB SNPs had allele frequencies between 0.2 and 0.25, with a relative risk of 3.0. The remaining SNPs had a relative risk of 1.0, indicating no effect.</p> <p>For the dataset with quantitative phenotypes (called <strong>synthetic_qt</strong>), we followed a similar structure. The SNPs were again divided into nullA, nullB, and nullC categories, with 30,000 SNPs each. We also included 5 SNPs labeled qtlA and 5 labeled qtlB, representing quantitative trait loci. The qtlA SNPs had allele frequencies from 0.1 to 0.2, with an effect size of 0.02, while qtlB SNPs had allele frequencies from 0.2 to 0.25, with an effect size of 0.03. These effect sizes indicate the SNPs' impact on the quantitative trait variance.</p>
On the Rationale and Use of Assertion Messages in Test Code: Insights from Software Practitioners
<p>This is the dataset for the paper: <strong>On the Rationale and Use of Assertion Messages in Test Code: Insights from Software Practitioners</strong></p> <p>This paper was accepted for publication at the International Conference on Software Maintenance and Evolution (ICSME 2024)</p> <p>Preprint: <a href="https://arxiv.org/abs/2408.01751" target="_blank" rel="noopener">https://arxiv.org/abs/2408.01751</a></p>
Web portal for teaching and practicing controlled experiments in Software Engineering - Tests Dataset
<p>The Test Dataset for the Web portal for teaching and practicing controlled experiments<br>in Software Engineering, developed by Fernando S. Grande, Edson OliveiraJr and André F.R. Cordeiro</p>
Sling load - Dataset of software tests on column detection algorithm with laserscanner data
<p>This dataset contains data and scripts for plotting results of testing of the algorithm of column detection, using laserscanner data.</p>
Sling load - Dataset of software tests for control loop with baumer sensors
<p>This dataset contains data and scripts for plotting results referred to different test scenarios: </p> <p>05: position estimation of baumer sensors, using sensors internal filter</p> <p>06: position estimation of baumer sensors, using lowpass filter in EEROS software</p> <p>10: position control by no force acting</p> <p>11: position control only with baumer sensors (no IMU)</p> <p>12: position control adding IMU input</p> <p>13: position control with IMU and Lowpass filter</p> <p>14: position control with IMU and cascaded control loop</p> <p>15: position control with IMU and platform inclination compensation</p> <p>16: position control with IMU and derivatives of position as actual speed</p> <p>17: position control baumer + IMU final</p> <p>18: position control baumer + IMU + laserscanner signal</p>
Sling load - Dataset of final software tests
<p>This dataset contains data and scripts for plotting results referred to different test scenarios: </p> <p>- 19 app_lab_baumer: tests performed in the lab with baumer laser sensors as position feedback</p> <p>- 20 app_lab_laser: tests performed in the lab with laserscanner as position feedback</p> <p>- 21 app_lab_camera: tests performed in the lab with pixycam as position feedback</p> <p>- 22 app_heli_camera: tests performed in the lab with pixycam as position feedback, with platform hanging from helicopter</p> <p>- 23 app_crane_camera: tests performed in the lab with pixycam as position feedback, with platform hanging from crane</p>
Sling load - Dataset of software test with odrives
<p>This dataset contains data and scripts for plotting results referred to different test scenarios:</p> <p>02 odrives limits check: odrives shutting down at a specific speed</p> <p>02 odrives step response</p> <p>03 odrives setpoint for 8 motors, giving force reference in x, y, ad rotation in z. </p> <p> </p> <p> </p>
Test case selection through novel methodologies for software application developments
<p>Test case selection is to minimize the time and effort spent for software testing in real time practice. During the course of software testing, the software firms are in want of techniques to finish the testing in a stipulated time, whilst uncompromising on quality. The motto is to select subset of test cases rather to take up all available test cases to uncover most of the bugs. Clustering of test cases using ranking and also based on similarity coefficients is to be implemented. The experimented results have to show up the techniques proposed improving the catching up of errors in a comparatively shorter duration. In this research, eleven different features were considered in order to cluster the test cases. There are two methodologies implemented. In the first methodology, each cluster will cover set of specific features to a certain percentage. Depending on the feature's coverage, cluster of test cases can be selected. These clusters were formed using ranking methodology. In the second methodology, similarity among test cases based on eleven features is found. Then max-min composition is used to find fuzzy equivalences, upon which clusters are formed. Most similar test cases are clustered.</p>
Dataset for the workshop paper titled "GUI-Based Software Testing: An Automated Approach Using GPT-4 and Selenium WebDriver" submitted at A-Test 2023
<p>The results dataset is associated with the research paper titled "GUI-Based Software Testing: An Automated Approach Using GPT-4 and Selenium WebDriver", which has been submitted to the 14th edition of A-TEST, a workshop co-located with ASE 2023. ASE, or the Automated Software Engineering conference, is a premier event in the software engineering domain that emphasizes the role of automation in the software development process. The conference is set to take place in Kirchberg, Luxembourg on September 15.</p> <p>Inside the zipped results folder, each test run is cataloged in a timestamped subdirectory. Each of these subdirectories contains three files:</p> <ul> <li><strong>config.json:</strong> A configuration file specific to that test run</li> <li><strong>output.log:</strong> An output log detailing the test's processes and results</li> <li><strong>past_actions.json:</strong> A JSON file designed for streamlined, programmatic access to the test run's data</li> </ul> <p> </p>
Supplementary Materials and Raw Data for "Automated Test Suite Generation for Software Product Lines based on Quality-Diversity Optimisation"
<p>Supplementary Materials and Raw Data for "Automated Test Suite Generation for Software Product Lines based on Quality-Diversity Optimisation"</p> <p>1. OnlineSupplement.pdf------Online supplementary data for the paper</p> <p>2. RQ1-4.rar----Raw data for the paper</p> <p>3. Runtime to generate, optimise and execution test suites.xlsx -----Data used in Section 7 PRACTICAL IMPLICATIONS</p> <p>Source code of the algorithms used to produce these data can be found at GitHub https://github.com/gzhuxiangyi/SPLTestingMAP</p>
Test case selection through novel methodologies for software application developments
Open the record for dataset details and reuse information.
Data from: HExT, a software supporting tree-based screens for hybrid taxa in multilocus datasets, and an evaluation of the homoplasy excess test
Open the record for dataset details and reuse information.
Pragmatic Software Testing Education: Appendix
<p>Appendix of our SIGCSE 2019 paper "Pragmatic Software Testing Education"</p>
A Systematic Mapping Study on Software Testing forSystems-of-Systems
<p>Vídeo de apresentação do estudo : A Systematic Mapping Study on Software Testing forSystems-of-Systems para o SAST 2020</p>
Investigating Multi and Many-Objective Metaheuristics to Support Software Integration Testing
<p>In spite of the fact that Search-Based Software Testing (SBST) is a very appealing field today, there are few studies that deal with software integration testing and, even so, most of these works are not truly related to the generation of test cases to this testing level. In this paper, we present a method, InMeHy, which aims at investigating the use of metaheuristics to derive integration test cases based on C++ source code. A graph is created based on the code which represents the integration of several classes of the application. Multi and Many-Objective metaheuristics (Evolutionary Algorithms) were considered to generate integration test cases and were assessed via three quality indicators. Results show that the traditional Indicator-Based Evolutionary Algorithm (IBEA) turned out to be the best out of four algorithms evaluated, including newer Many-Objective strategies such as Nondominated Sorting Genetic Algorithm-III (NSGA-III).</p>
Neuronal cell labelled with anti PSD95 and homer antibodies - sample image for software testing of Easy SODA
<p><strong>Hippocampal neurons</strong> in culture seeded on glass coverslip. This 16 bits confocal picture of hippocampal neuron in culture has been taken at 63x (1024x1024 pixels - pixel size 92,45 nm) in sequential mode with two channels : one dedicated to the PSD95 protein, and the other one to Homer protein. Those protein are expressed at glutamatergic post-synaptic sites and can be found close within Post Synaptic Density. This is a good sample to test colocalization and association analysis. The homer channel can be used both to infere neuronal shape thanks to hk-means segmentation (<a href="http://icy.bioimageanalysis.org/protocol/easy-cell-shape-with-hk-means/">http://icy.bioimageanalysis.org/protocol/easy-cell-shape-with-hk-means/</a>), or to detect clusters thanks to wavelet segmentation (<a href="http://icy.bioimageanalysis.org/protocol/rainbow-spot-detector-scale-finder/">http://icy.bioimageanalysis.org/protocol/rainbow-spot-detector-scale-finder/</a>). This sample image can be used with Easy SODA protocol (Standard Object Distance Analysis) on Icy software (<a href="http://icy.bioimageanalysis.org/protocol/easy-soda-2-colors-1-image/">http://icy.bioimageanalysis.org/protocol/easy-soda-2-colors-1-image/) </a>to detect association between PSD95 and homer clusters.</p>
Neuronal cell labelled with anti MAP2 antibody - sample image for software testing
<p><strong>Hippocampal neurons</strong> in culture seeded on glass coverslip. Dendritic extensions are labeled with antibody against <strong>MAP2, a protein associated with microtubules and nucleus is labeled with DAPI</strong>. The resulting signal give acces to the neuronal cell body and dendrite. Here the axon is not labeled. This picture has been taken on a Leica DMRE microscope with a 40x objective. This is a sample demo to be used to test our automatic segmentation of neuronal cells thanks to the <strong>Easy cell shape protocol</strong>, available freely on Icy software. You can download the software (PC, Mac or linux) and have access to the full documentation of this solution on <a href="http://icy.bioimageanalysis.org/protocol/easy-cell-shape-with-hk-means/">http://icy.bioimageanalysis.org/protocol/easy-cell-shape-with-hk-means/ (</a>Publication ID: ICY-77638). It segments in a user friendly mode the cell shape using the hk-mean plugin that applies a N-class thresholding based on a K-Means classification of the image histogram.</p>
Survey data in PDF-What industry wants from academia in software testing research (phase 1)
<p>Survey data in PDF-What industry wants from academia in software testing research</p>
Combining Goal-Oriented Requirements Analysis and Testing for Software Energy-Awareness
<p>Artifacts for the paper "Combining GORE Analysis and Testing for Software Energy-Awareness"</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.