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.
57
datasets available to search
ShareScore release 0.9.0
Dataset results
57 results for “Test Automation”
Automated and manual pooled sample testing with panther fusion and aptima SARS-CoV-2 assays
Open the record for dataset details and reuse information.
Understanding the Differences in the Unit Tests Produced by Humans and Coverage-Directed Automated Generation
<p>Automated test generation - the use of tools to create all or part of test cases - has a critical role in controlling the cost of testing. A particular area of focus in automated test generation research is unit testing. Unit tests are intended to test the functionality of a small isolated unit of code - typically a class. </p> <p>In automated test generation research, it is not abnormal to compare the effectiveness of the test cases generated by automation to those written by humans. Indeed, a common premise of automation research - implicitly or explicitly - is that effective automation can replace human effort. The hypothesis postulated is that, if we make enough advances, a tool could replace the tremendous effort expended by a human tester to create those unit tests. </p> <p>This observation leads to two natural questions. Do the tests produced by humans and automation differ in the types of faults they detect? If so, in what ways are the tests produced and the faults detected different? Understanding when and how to deploy automation requires a clearer understanding of how the tests produced by humans and automation are different, and how those differences in turn affect the ability of those test cases to detect faults. Insight into the differences between human and automation-produced test cases could lead not only to improvements in the ability of automation to replace human effort, but improvements in our ability to use automation to augment human effort. The goal of this study is to explore and attempt to quantify those differences. </p> <p>In this study, we make use of the EvoSuite test generation framework for Java. We generate test suites targeting two configurations - a traditional single-criterion configuration targeting Branch Coverage over the source code and a more sophisticated multi-objective configuration targeting eight criteria. Controlling for coverage level, we compare the suites generated by EvoSuite to those written by humans for five mature, popular open-source systems in terms of both their syntactic structure and their ability to detect 45 different types of faults. Our goal is not to declare a "winner'", but to identify the areas where humans and automation differ in their capabilities, and - in turn - to make recommendations on how human and automation effort can be combined to overcome gaps in the coverage of the other. We aim to identify lessons that will improve human practices, lead to the creation of more effective automation, and present natural opportunities to both augment and replace human effort. </p>
Data from: Automation and evaluation of the SOWH test with SOWHAT
The Swofford–Olsen–Waddell–Hillis (SOWH) test evaluates statistical support for incongruent phylogenetic topologies. It is commonly applied to determine if the maximum likelihood tree in a phylogenetic analysis is significantly different than an alternative hypothesis. The SOWH test compares the observed difference in log-likelihood between two topologies to a null distribution of differences in log-likelihood generated by parametric resampling. The test is a well-established phylogenetic method for topology testing, but it is sensitive to model misspecification, it is computationally burdensome to perform, and its implementation requires the investigator to make several decisions that each have the potential to affect the outcome of the test. We analyzed the effects of multiple factors using seven data sets to which the SOWH test was previously applied. These factors include a number of sample replicates, likelihood software, the introduction of gaps to simulated data, the use of distinct models of evolution for data simulation and likelihood inference, and a suggested test correction wherein an unresolved "zero-constrained" tree is used to simulate sequence data. To facilitate these analyses and future applications of the SOWH test, we wrote SOWHAT, a program that automates the SOWH test. We find that inadequate bootstrap sampling can change the outcome of the SOWH test. The results also show that using a zero-constrained tree for data simulation can result in a wider null distribution and higher p-values, but does not change the outcome of the SOWH test for most of the data sets tested here. These results will help others implement and evaluate the SOWH test and allow us to provide recommendations for future applications of the SOWH test. SOWHAT is available for download from https://github.com/josephryan/SOWHAT.
Automation of Test Skeletons within Test-Driven Development Projects
<p>This is the data sheets created by authors for Automatic Test-Skeleton Generation within Test-Driven Development Projects</p>
A new automated test system for electrochemical tests
<p>In this record, we shared a new automated electrolyte-gate FET test system used for electrochemical tests. The videos show different working steps in a task focused on the test of multiple sensors with the automated system. The python scripts and sample experimental data are shared for repeating.</p>
HeteroGen: Transpiling C to Heterogeneous HLS Code with Automated Test Generation and Program Repair
<p>This artifact submission includes 1. an error study, 2. a fuzzing-based test generation tool, and 3. a code-editing tool for error removal.</p>
ESEC/FSE 2022 SRC - Automated Generation of Test Oracles for RESTful APIs
<p>Replication package of the paper entitled "Automated Generation of Test Oracles for RESTful APIs". Specifically, the directory contains the following files:</p> <p>- bugs_videos.zip: Videos showing the replication of bugs found in real systems.</p> <p>- daikon_modified.zip: Modified version of Daikon used for the experimentation.</p> <p>- evaluation.zip: Contains several spreadsheets with the classification of all the generated invariants as true positives, false positives or bugs. It also contains a final report of the results (Final report.xlsx).</p> <p>- oas-instrumenter.zip: Instrumenter implemented. This project also contains (inside the src/test/resources/evaluation directory) the OAS specifications for each API, the RESTest configuration files for each operation (including the data dictionaries used), the test cases in .csv format, the detected invariants and the DeclsFiles and DtraceFiles.<br> </p>
Dataset for Automated Unit Test Generation via Chain of Thought Prompt and Reinforcement Learning
<p>This is the replication package including three types datasets: training dataset with CoT prompts, reward dataset for training reward model, rl dataset for optimizing policy model. The training dataset includes filter_test_cot_rule_50k.csv, filter_train_cot_rule_50k.csv, and filter_valid_cot_rule_50k.csv. These three datasets includes multiple fields (i.e., src_fm, intention, plan, elaboration, gpt_test, src_fm_cot_gpt, target, src_fm_fc_ms_ff,src_fm_intention,src_fm_plan,src_fm_elaboration,idx,rule_cot,rule_cot_nlp,combine_cot,src_fm_rule_cot_nlp,src_fm_cot_nlp_gpt,gpt_cot_filter,src_fm_plan_intention). The reward dataset includes test_athena.json, train_athena.json, and valid_athena.json three files. The rl dataset includes three files: filter_test_cot_gpt_rl.csv, filter_train_cot_gpt_rl.csv, filter_valid_cot_gpt_rl.csv. These files include mulitple fields: src_fm,intention,plan,elaboration,gpt_test,src_fm_cot_gpt,target,src_fm_fc_ms_ff,src_fm_intention,src_fm_plan,src_fm_elaboration,gpt_cot_filter.</p>
A Quantitative Approach for the Assessment of Microservice Architecture Deployment Alternatives by Automated Performance Testing
<p>Reprodicibility package, raw data and results for the paper "A Quantitative Approach for the Assessment of Microservice Architecture Deployment Alternatives by Automated Performance Testing". </p>
Replication Kit for the work "Automated and non-Automated Usability Testing of Touchscreens in Virtual Reality"
<p>This replication kit for the work "Automated and non-Automated Usability Testing of Touchscreens in Virtual Reality" contains the Unity project on which the case study was performed, the used AutoQUEST version, the with AutoQUEST recorded data and the AutoQUEST save file of the evaluated usability smells.</p>
Automated Cell type Annotation Testing with Clear Cell Renal Cell Carcinoma
Open the record for dataset details and reuse information.
A Survey on Automated Driving System Testing: Landscapes and Trends [Supplementary material]
<p>This is a supplementary material for our paper <a href="https://arxiv.org/abs/2206.05961">"A Survey on Automated Driving System Testing: Landscapes and Trends"</a>.</p>
A Toolkit for Automated Testing of Dafny
<p>This artifact presents DUnit, DMOck, and DTest, the three components of the testing toolkit developed for the Dafny programming language. The artifact should be executed on the virtual machine provided together with the testing toolkit.</p>
Artefact for "Combining Type Inference and Automated Unit Test Generation for Python"
<p>Contains the artefact for our ASE 2023 submission “Combining Type Inference and Automated Unit Test Generation for Python”.</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>
Beaumont Health Large-scale Automated Serologic Testing for COVID-19
ClinicalTrials.gov study NCT04349202. IPD Sharing: NO. Countries: 1. Publications: 2.
Automated Diagnostic Test for Diabetic Retinopathy in Brazilian Mass Screening
ClinicalTrials.gov study NCT02927561. IPD Sharing: NO. Countries: 1. Publications: 8.
Data from: Automation and evaluation of the SOWH test with SOWHAT
Open the record for dataset details and reuse information.
[dataset] Performance Analysis of Microservice Applications via Automated Load Testing and Bayesian Inference
<p>Anonymized replication package of the experiments presented in the research paper: "Performance Analysis of Microservice Applications via Automated Load Testing and Bayesian Inference".</p> <p>See the README.md file.</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.