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144 results for “Test generation”
Supplementary Material to Context-tailored Workload Model Generation for Continuous Representative Load Testing
<p>This package contains files and instructions to replicate our experiments on context-tailored workload model generation for continuous representative load testing. Please download and extract the <code>context-tailoring.tar.gz</code> file and view <code>index.html</code> (also contained in the tarball) for further instructions.</p> <p><code>cobra-db.tar.gz </code>is only required for skipping parts of the steps (see the detailed instructions).</p>
Data from: Is biasing offspring sex ratio adaptive? a test of Fisher's principle across multiple generations of a wild mammal in a fluctuating environment
Fisher's principle explains that population sex ratio in sexually reproducing organisms is maintained at 1:1 due to negative frequency-dependent selection, such that individuals of the rare sex realize greater reproductive opportunity than individuals of the more common sex until equilibrium is reached. If biasing offspring sex ratio towards the rare sex is adaptive, individuals that do so should have a higher number of grandoffspring. In a wild population of North American red squirrels (Tamiasciurus hudsonicus) that experiences fluctuations in resource abundance and population density, we show that overall across 26 years, the secondary sex ratio was 1:1; however, stretches of years during which adult sex ratio was biased did not yield offspring sex ratios biased towards the rare sex. Females that had litters biased towards the rare sex did not have more grandoffspring. Critically, adult sex ratio was not temporally autocorrelated across years, thus the population sex ratio experienced by parents was independent of the population sex ratio experienced by their offspring by the time of their primiparity. Expected fitness benefits of biasing offspring sex ratio may be masked or negated by fluctuating environments across years, which limit the predictive value of the current sex ratio.
Data from: how to generate and test hypotheses about colour: insights from half a century of guppy research
<p><span>Colouration facilitates evolutionary investigations in nature because the interaction between genotype, phenotype and environment is relatively accessible. In a landmark set of studies, Endler addressed this complexity by demonstrating that the evolution of male Trinidadian guppy colouration is shaped by the local balance between selection for mate attractiveness versus crypsis. This became a textbook paradigm for how antagonistic selective pressures may determine evolutionary trajectories in the wild. Recent studies have, however, challenged the generality of this paradigm. Here we respond to these challenges by reviewing five important yet underappreciated factors that contribute to colour pattern evolution: (1) inter-population variation in female preference, (2) differences in how predators versus conspecifics view males, (3) biased assessment of pigmentary versus structural colouration, (4) the importance of accounting for multi-species predator communities, and (5) the importance of considering the multivariate genetic architecture and multivariate context of selection. We elaborate upon these points specifically in relation to two papers that illustrate the challenges raised by the recent body of work on guppies. More broadly, we emphasise the depth of consideration necessary for testing evolutionary hypotheses using complex multi-trait phenotypes such as guppy colour patterns.</span></p>
Block-oriented description of a fictional CHP generation plant for unit commitment testing
<p>Block-oriented description of a fictional combined heat&power generation plant for unit commitment testing.</p> <p>Example input data is available at: https://doi.org/10.5281/zenodo.10875471</p>
Test Data for iwc Pre-curation PretextMap generation workflow
Open the record for dataset details and reuse information.
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>
Refinement of an Attacker Model for Attack Paths Generation | Test Dataset
<p>This repository contains the test models and associated tests of the evaluation of the bachelor thesis "Refinement of an Attacker Model for Attack Paths Generation" / German: "Verfeinerung des Angreifermodells und Fähigkeiten in einer Angriffspfadgenerierung". The basis for the model used is the TravelPlaner CaseStudy. The tests for measuring the performance delta are also included.</p> <p>Palladio Bench 5, Java 11 and the following GitHub repositories are used for execution:<br> Analysis: https://github.com/Patrick-Spiesberger/Palladio-Addons-ContextConfidentialityAnalysis_Mitigation<br> Meta model: https://github.com/Patrick-Spiesberger/Palladio-Addons-ContextConfidentialityMetamodell_Mitigation<br> <br> Environment installation is included in the README files. See also the previous work by Maximilian Walter:<br> Analysis: https://github.com/FluidTrust/Palladio-Addons-ContextConfidentiality-Analysis<br> Meta Model: https://github.com/FluidTrust/Palladio-Addons-ContextConfidentiality-Metamodel</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>
Inference and Test Generation Using Program Invariants in Chemical Reaction Networks Artifacts
<p>The artifacts for Inference and Test Generation Using Program Invariants in Chemical Reaction Networks, published at ICSE 2022.</p> <p>The pdf of the paper can be accessed at <a href="https://ieeexplore.ieee.org/document/9794130">IEEEXplore</a>.</p> <p><strong>To cite this work, please use the citation below:</strong></p> <pre>@INPROCEEDINGS{GertenICSE22, author={Gerten, Michael C. and Marsh, Alexis L. and Lathrop, James I. and Cohen, Myra B. and Miner, Andrew S. and Klinge, Titus H.}, booktitle={2022 IEEE/ACM 44th International Conference on Software Engineering (ICSE)}, title={Inference and Test Generation Using Program Invariants in Chemical Reaction Networks}, month={May}, year={2022}, pages={1193-1205}, doi={10.1145/3510003.3510176}}</pre> <p>The artifacts are also available on <a href="https://github.com/LavaOps/ICSE-2022-Artifacts">GitHub</a>.</p> <p><strong>This is an updated version of the ChemFlow tool. The update addressed an overflow error when computing gaussian elimination that could result in incorrect invariants with certain model inputs. After verification, all models in this work were not affected by this bug and have the same set of invariants generated by both versions. We have updated the docker file to use the new code as well.</strong></p>
Test and Train data for the retrieval experiment in "A molecule generation-oriented lead compound optimization architecture: discovery of potent, selective, oral NLRP3 inflammasome inhibitors"
Open the record for dataset details and reuse information.
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>
Replication Package: Microservice-tailored Generation of Session-based Workload Models for Representative Load Testing
<p>This is the replication package for the publication <em>Microservice-tailored Generation of Session-based Workload Models for Representative Load Testing</em>, MASCOTS 2019. It holds the experiment setup, results, and detailed analyses of the results.</p> <p>The README.md (or README.pdf) contains further descriptions and instructions.</p>
Data for Effective Unit Test Generation for Java Null Pointer Exceptions
<p>This is to provide all contents of our work, NPETest, including all raw experimental data used in our paper which will be published at ASE`24.</p> <p> </p> <p>NPETest is an unit test generation tool for Java projects, which utilizes both static and dynamic analysis techniques for effective NPE detection. This tool is implemented on the top of EvoSuite, a publicly available unit test generation tool for Java.</p> <p>For more technical details, please read our paper which will be published at ASE`24.</p> <p> </p> <p>The descriptions for the uploaded files are as follows:</p> <p>npetest_result.zip: results of NPETest for all benchmarks, containing the generated test-cases</p> <p>evosuite_opt_result.zip: results of EvoSuite with fine-tuned options for all benchmarks, containing the generated test-cases</p> <p>evosuite_def_result.zip: results of EvoSuite with default options for all benchmarks, containing the generated test-cases</p> <p>randoop_NPEX.tar.gz: results of Randoop for NPEX benchmarks, containing only the log-files. </p> <p>randoop_other.tar.gz: results of Randoop for Bears, BugSwarm, Defects4J, Genesis benchmarks, containing only the log-files.</p> <p>subject_gits.tar.gz: information of the buggy version for each benchmark. </p> <p>NPETestArtifact-main.zip: all contents of NPETest from the public Github respository <a href="https://github.com/kupl/NPETestArtifact" target="_blank" rel="noopener">NPETestArtifact</a>.</p> <p> </p> <p> </p> <p>The detailed description for NPETest (e.g., Install, Usage) of the tool is available on the public repository: <a href="https://github.com/kupl/NPETestArtifact" target="_blank" rel="noopener">NPETestArtifact</a>.<br>You can also download VM from the following link: <a href="https://doi.org/10.5281/zenodo.13371823" target="_blank" rel="noopener">Zenodo</a></p>
Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"
<p>Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"</p> <p>Includes scripts for generating raw results, postprocessing scripts, as well as scripts/notebooks for generating plots for the manuscript</p>
Artifacts for paper "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge" submitted to TOSEM
<p>The project includes the data and code used in the submitted TOSEM paper titled "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge"</p>
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This artifact contains:</p> <p>1. The binary folder. Its instruction can be found in [./binary/README.md](./binary/README.md).</p> <p>2. All classes used in the experiments [./classes.csv](./classes.csv).</p> <p>3. The experimental data zip file ([experimental_data.zip](./experimental_data.zip)).</p> <p> </p>
Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation
<p><strong>Artifact of Method-Level Java Class Splitter for Search-Based Unit Test Generation</strong></p> <p> </p> <p>This zip file is the artifact that contains the following:</p> <p>1. The binary. </p> <p>2. All classes used in the experiments.</p> <p>3. The experimental data.</p> <p> </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>
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>
Artifact of Enhancing Search-Based Unit Test Generation with Method-Level Java Class Splitter
<p># Artifact of Enhancing Search-Based Unit Test Generation with Method-Level Java Class Splitter</p> <p> </p> <p>This artifact contains:</p> <p>1. The binary folder. Its instruction can be found in [./binary/README.md](./binary/README.md).</p> <p>2. All classes used in the experiments [./classes.csv](./classes.csv).</p> <p>3. The experimental data zip file ([experimental_data.zip](./experimental_data.zip)).</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.