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36 results for “Code validation”
Artifacts for ISSTA 2021 Paper : Validating Static Warnings via Testing Code Fragments
<p><strong>This data set is for ISSTA 2021 Paper: Validating Static Warnings via Testing Code Fragments</strong></p> <p>Static analysis is an important approach for finding bugs and vulnerabilities in software. However, inspecting and confirming static warnings are challenging and time-consuming. In this paper, we present a novel solution that automatically generates test cases based on static warnings to validate true and false positives. We designed a syntactic patching algorithm that can generate syntactically valid, semantic preserving executable code fragments from static warnings. We developed a build and testing system to automatically test code fragments using fuzzers, KLEE and Valgrind. We evaluated our techniques using 12 real-world C projects and 1955 warnings from two commercial static analysis tools. We successfully built 68.5% code fragments and generated 1003 test cases. Through automatic testing, we identified 48 true positives and 27 false positives, and 205 likely false positives. We matched 4 CVE and real-world bugs using Helium, and they are only triggered by our tool but not other baseline tools. We found that testing code fragments is scalable and useful; it can trigger bugs that testing entire programs or testing procedures failed to trigger.</p>
Code to generate figures 3 and 4 of: "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."
<p>Code to generate figures 3 and 4 of the manuscript titled "A comprehensive LFQ benchmark dataset to validate data analysis pipelines on modern day acquisition strategies in proteomics."</p> <p> </p>
Supplementary data for A Systematic Literature Review on the Code Smells Datasets and Validation Mechanisms
<p>The attached Microsoft Excel files contain the data and diagrams of the paper:</p> <p><strong>A systematic literature review on the code smells datasets and validation mechanisms</strong></p> <p>The article is under review in the ACM Computing Surveys.</p> <p> </p>
An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability - Data, Code and Documentation
<p>Release version of the data, code and documentation used in and generated by our data analysis and literature search to ensure reproducibility, repeatability, and transparency, to be published alongside our paper "An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability".</p>
Codes used to identify hospital complications in validation of Charlson comorbidity index ICD-10 for the US
<p>Codes used to identify hospital complications in validation of Charlson comorbidity index ICD-10 for the US.</p>
Code list used to generate cohorts in ICD-10 CCI US validation study
<p>Code list used to generate cohorts in validating the Charlson comorbidity index (CCI) for ICD-10 in the United States.</p>
Edge turbulence in ISTTOK: a multi-code fluid validation
<p>Experimental data from ISTTOK, simulation outputs from GBS, BOUT++/Hermes, and HESEL, and processing scripts used in a paper to be submitted to PPCF.</p>
Data and Codes for Experimentally Validated Inverse design of Multi Property Fe-Co-Ni alloys: Data and codes release v1.0.1
<p>Data and Codes for Experimentally Validated Inverse design of Multi-Property Fe-Co-Ni alloys</p>
Data and Code for: Validating ATLAS: a regional-scale high-throughput tracking system
<p>Data and code for the manuscript: <strong>Validating ATLAS: a regional-scale high-throughput tracking system</strong></p>
Code of "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation"
<p>Code used to prepare a paper entitled "Inclusion of flood diversion canal operation in the H08 hydrological model with a case study from the Chao Phraya River basin: model development and validation" which was submitted to Hydrology and Earth System Sciences.</p>
Documentation and code for reproducing analyses presented in: vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework
<p>Documentation and code for reproducing analyses presented in: vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework. Please see the <strong>README.pdf</strong> for step-by-step instructions for reproducing the entire analysis described in the paper.</p>
Code and data for "Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer".
<p>Code and data for "<strong>Discovery and validation of tissue-specific DNA methylation as noninvasive diagnostic markers for colorectal cancer</strong>".</p> <ul> <li> <p>The publicly available datasets supporting the conclusions of this article are available in the Gene Expression Omnibus repository (<a href="https://www.ncbi.nlm.nih.gov/geo/">https://www.ncbi.nlm.nih.gov/geo/</a>) and UCSC Xena Browser (TCGA, <a href="https://xena.ucsc.edu/">https://xena.ucsc.edu/</a>).</p> </li> </ul>
Data used, summary and codes: A validation standard for Area of Habitat maps for terrestrial birds and mammals
<p>Birds_AOH_Metadata: Logistic modelling and point validation summary along with pixel summary of AOH maps for Birds .</p> <p>Mammals_AOH_Metadata: Logistic modelling and point validation summary along with pixel summary of AOH maps for Mammals.</p> <p>Column_Names_Index_AOH_Metedata: Description of column names of Birds_AOH_Metadata and Mammals_AOH_Metadata</p> <p>Point_localities_ebird: Latitude and longitude of points per species for birds downloaded from eBird used to validate the AOH maps. </p> <p>Point_localities_gbif: Latitude and longitude of points per species for mammals downloaded from GBIF used to validate the AOH maps. </p> <p>Logistic_Regression_Codes_AOH_Validation: R script for logistic modelling.</p> <p>Point_Validation_Codes_AOH_Validation: R script for point validation of AOH maps.</p> <p>Sample_AOH_Maps: Few sample AOH maps which were validated.</p> <p> </p>
Spatial Plus Cross-Validation experiments datasets and codes
<p>This zip file includes all materials of Spatial Plus Cross-Validation experiments. </p> <p>They are ordered by the first number of folder's name.</p> <p>In each folder, the order of running code scripts are labeled by the first number of code's name.</p>
CodiEsp codes: list of valid CIE10 codes for the CodiEsp task
<p><strong>Please cite if you use this dataset:</strong></p> <p>Antonio Miranda-Escalada, Aitor Gonzalez-Agirre, Jordi Armengol-Estapé and Martin Krallinger. Overview of automatic clinical coding: annotations, guidelines, and solutions for non-English clinical cases at CodiEsp track of CLEF eHealth 2020. In CLEF (Working Notes). 2020</p> <pre><code>@inproceedings{miranda2020overview, title={Overview of automatic clinical coding: annotations, guidelines, and solutions for non-english clinical cases at codiesp track of CLEF eHealth 2020}, author={Miranda-Escalada, Antonio and Gonzalez-Agirre, Aitor and Armengol-Estap{\'e}, Jordi and Krallinger, Martin}, booktitle={Working Notes of Conference and Labs of the Evaluation (CLEF) Forum. CEUR Workshop Proceedings}, year={2020} }</code></pre> <p> </p> <p>This compressed folder contains two files:</p> <p> + codiesp-D_codes.tsv: list of CIE10-Diagnósticos terms (2018 version) with their description in Spanish and in English.<br> + codiesp-P_codes.tsv: list of CIE10-Procedimiento terms (2018 version) with their description in Spanish and in English. In addition, the list also contains the codes until the 4th axis, which are also used in the CodiEsp-P track due to annotation reasons.</p> <p>A limited number of codes do not have an English description because they were removed from the English version but maintained in the Spanish version of the terminology.</p> <p> </p> <p>Format: <br> Tab-separated files with 3 columns<br> code es-description en-description</p> <p> </p> <p>Spanish to English description mapping:<br> For CodiEsp-D, the mapping to the English description was done through the files in the National Center for Health Statistics webpage: https://www.cdc.gov/nchs/icd/icd10cm.htm<br> Specifically, the file used was: ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD10CM/2018/2018-ICD-10-CM-Codes-File.zip/icd10cm_codes_2018.txt</p> <p>For CodiEsp-P, the mapping to the English description was done through the files in the Centers for Medicare Services webpage: https://www.cms.gov/Medicare/Coding/ICD10/2018-ICD-10-PCS-and-GEMs<br> Specifically, the file used was: 2018_icd10pcs_codes_file.zip/icd10pcs_codes_2018.txt</p>
Training and Testing Data, Associated Code, and SCAM Validations Code and Data for ResNet in moist physics (ResCu)
<p>Data and codes for a deep convolutional residual neural network moist physics parameterization (ResCu).</p> <p>In this new version, the randomly selected training data samples and part of testing data samples (June, July and August) are provided. They are processed into a new data structure, which can be directly utilized in training and testing. For the entire second year training samples and the entire third year testing samples, we provide them in a repository at Dryad (<a href="https://doi.org/10.6075/J0CZ35PP">https://doi.org/10.6075/J0CZ35PP</a> and https://doi.org/10.6075/J03J3BGF).</p> <p>Please download and decompress ResCu_Han_et_al_JAMES.tar.gz.</p> <p>Follow the instructions in README.txt and download the training and testing data (The Dryad depositary is provided in the description). </p> <p>Here we provide 3 parts of data and codes:</p> <p>1, Training and testing data from SPCAM;</p> <p>2, Training and testing codes for ResCu and many other NN architectures;</p> <p>3, SCAM validations.</p> <p> </p>
Model Output and Validation Data for Online Determination of GNSS Differential Code Biases using Rao-Blackwellized Particle Filtering
<p>This dataset contains the outputs of 10 test runs of the A-CHAIM data assimilation model used to evaluate the performance of the bias estimation procedure used by the model. The 5-minute output files for each test run are included in a separate folder.</p> <p>The IGS DCBs in SINEX format and the various global ionospheric maps used in the study are included.</p> <p>This dataset also includes all of the processed GNSS data used in the study, which were used to generate the DCBs used in each test run.<br> <br> The in-situ electron density measurements from DMSP as well as the ionosonde data from GIRO which were used to measure the performance are also included.</p>
Temporal validity of software datasets for code metrics: an empirical assessment of sampling strategies
<p>This is the repository for the scripts and data of the study "Building and updating software datasets: an empirical assessment".</p> <h2>Data collected</h2> <p>The data generated for the study it can be downloaded as a zip file. Each folder inside the file corresponds to one of the datasets of projects employed in the study (qualitas, currentSample and qualitasUpdated). Every dataset comprised three files "class.csv", "method.csv" and "sample.csv", with class metrics, method metrics and repository metadata of the projects respectively. Here is a description of the datasets:</p> <ul> <li>qualitas: includes code metrics and repository metrics from the projects in the release 20130901r of the Qualitas Corpus.</li> <li>currentSample: includes code metrics and repository metrics from a recent sample collected with our sampling procedure.</li> <li>qualitasUpdated: includes code metrics and repository metrics from an updated version of the Qualitas Corpus applying our maintenance procedure.</li> </ul> <h2>Plot graphics</h2> <p>To plot the results and graphics in the article there is a Jupyter Notebook "Experiment.ipynb". It is initially configured to use the data in "datasets" folder.</p> <h2>Replication Kit</h2> <p>For replication purposes, the datasets containing recent projects from Github can be re-generated. To do so, the virtual environment must have installed the dependencies in "requirements.txt" file, add Github's tokens in "./token" file, re-define or leave as is the paths declared in the constants (variables written in caps) in the main method, and finally run "main.py" script. The portable versions of the source code scanner <a href="https://sourcemeter.com/" target="_blank" rel="noopener">Sourcemeter</a> are located as zip files in "./Sourcemeter/tool" directory. To install Sourcemeter the appropriate zip file must be decompressed excluding the root folder "SourceMeter-10.2.0-x64-<OS>".</p> <p>The script comprise 5 steps:</p> <ol> <li>Project retrieval from Github: at first the sampling frame with projects complying with a specific quality criteria are retrieved from Github's API.</li> <li>Create samples: with the sampling frame retrieved, the current samples are selected (currentSample and qualitasUpdated). In the case of qualitasUpdated, it is important to have first the "sample.csv" file inside the qualitas folder of the dataset originally created for the study. This file contains the metadata of the projects in Qualitas Corpus.</li> <li>Project download and analysis: when all the samples are selected from the sampling frame (currentSample and qualitasUpdated), the repositories are downloaded and scanned with SourceMeter. In the cases in which the analysis is not possible, the projects are replaced with another one with similar size.</li> <li>Outlier detection: once the datasets are collected, it is necessary to manually look for possible outliers in the code metrics under study. In the notebook "Experiment.ipynb" there are specific sections dedicated for it ("Outlier detection (Section 4.2.2)").</li> <li>Outlier replacement: when the outliers are detected, in the same notebook there is also a section for outlier replacement ("Replace Outliers") where the outliers' url have to be listed to find the appropriate replacement.</li> </ol> <ul> <li>If it is required, the metrics from the Qualitas Corpus can also be re-generated. First, it is necessary to download the release 20130901r from its <a href="http://www.qualitascorpus.com/download/" target="_blank" rel="noopener">official webpage</a>. Second, decompress the .tar files downloaded. Third, make sure that the compressed files with source code from the projects (.java files) are placed in the "compressed" folder, in some cases it is necessary to read the "QC_README" file in the project's folder. Finally, run the original main script "Generate metrics for the Qualitas Corpus (QC) dataset" part of the code. </li> </ul>
AlphaTrans: A Neuro-Symbolic Compositional Approach for Repository-Level Code Translation and Validation
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Data and code used for 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'
<p>This repository contains all data necessary to reproduce the results for the manuscript titled 'Assessing the Accuracy of Activity Classification Using Thigh-Worn Accelerometry: A Validation Study of ActiPASS in School-Aged Children'.</p>
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.