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76 results for “automated assessment”
Data for "Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments"
<p>Flow Cytometry and plate reader data from "High Throughput Experimentation to replicate Yeast Gates Experiment," accompanied with jupyter noteboooks to replicate the analyses. Sequencing data is <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA784977">available separately</a>.</p> <p>Files named <code>flow_cytometrya<em>x</em></code> should be concatenated: they are individual slices of a gzipped tar file. Concatenate them and then extract with <code>tar xzf <em>filename</em></code>.</p> <p>The paper is available on <a href="https://biorxiv.org/cgi/content/short/2022.05.31.493627">Biorxiv</a>.</p> <p>The Jupyter notebooks used to analyze this data for the paper are <a href="https://github.com/rpgoldman/replication-paper-data-analysis">available on GitHub</a>.</p>
Quality Assessment in DevOps: Automated Analysis of a Tax Fraud Detection System
<p>The dataset includes the results of the performance analysis of Big Blu case study under different workloads, number of available resources and execution demand of activities</p>
Student's logs and perceptions of an automated assessment tool in a software engineering MOOC specialization
<p>Our dataset contains students' perceptions and usage of an automated assessment tool (MOOCauto) for obtaining formative feedback in software engineering assignments that are part of a MOOC specialization at Universidad Politécnica de Madrid (Spain), delivered by the MiriadaX platform. The dataset has previously been used in a study to evaluate students' perceptions of the tool and to analyze their usage patterns using Growth Mixture Models <a href="https://www.computer.org/csdl/magazine/so/5555/01/10196480/1P9AhkBLYXK">(López-Pernas et al., 2023)</a>. The code of each of the assignments is available on Github: <a href="https://github.com/ging-moocs">https://github.com/ging-moocs</a>.</p> <p>Our dataset contains two files:</p> <h2>MOOCauto usage logs</h2> <p>The first file is called<strong> moocauto_logs.csv </strong>and it contains 9,108 anonymized logs of students' use of the automated assessment tool in the MOOC specialization assignments. The columns of the dataset are as follows:</p> <ul> <li><strong>MOOCid</strong>: Unique numeric identifier for the MOOC (1-4)</li> <li><strong>MOOC: </strong>Name of the MOOC: Frontend Development, Backend Development, Git & Github, Fullstack Development</li> <li><strong>AssignmentName</strong>: Name of the assignment.</li> <li><strong>AssignmentId</strong>: Unique identifier for each assignment (1-17)</li> <li><strong>user: </strong>Unique identifier of the student (it varies per assignment)</li> <li><strong>timestamp: </strong>Time in which the assessment was performed</li> <li><strong>score</strong>: Score obtained (0-10)</li> </ul> <h2>Students' perceptions of MOOCauto</h2> <p>The second file is called <strong>moocauto_questionnaire.csv</strong> and it contains 213 students' responses to the questionnaire conducted at the end of each MOOC in order to evaluate their opinion of the tool and perception on usefulness, ease of use, and other aspects related to the Technology Acceptance Model (TAM). The questions were as follows:</p> <ul> <li><strong>What is your general opinion of MOOCauto?</strong> (1 Horrible - 5 Excellent)</li> <li><strong>Indicate your level of agreement with the following statements </strong>(1 Strongly disagree - 5 Strongly agree) <ul> <li>MOOCauto has been easy to install</li> <li>MOOCauto has been easy to use</li> <li>The feedback provided by MOOCauto was easy to understand</li> <li>The feedback provided by MOOCauto was useful</li> <li>The feedback provided by MOOCauto helped me improve my assignments</li> <li>The documentation Of MOOCauto was useful</li> <li>MOOCauto has increased my motivation to work on the assignments</li> <li>I prefer the feedback from MOOCauto than from peer assessment</li> <li>I would like to have a bot like MOOCauto in other MOOCs</li> </ul> </li> <li><strong>How useful do you perceive the following features of MOOCauto?</strong> (1 Useless - 5 Very useful) <ul> <li>It works locally on my computer</li> <li>It allows to run the test suite as many times as I want</li> <li>It provides instantaneous feedback every time the test suite is executed</li> <li>It has documentation that explains its use and available options</li> </ul> </li> </ul>
Critical Assessment of automated Structure Determination of Proteins by NMR
<p>The community-wide initiative "Critical Assessment of Automated Structure Determination of Proteins by NMR (<strong>CASD-NMR</strong>)" was launched in 2009 to to evaluate the ability of automated methods to produce 3D protein structures from NMR data that closely match structures manually determined by experts.</p> <p>This dataset includes all the experimental data made available to the participants of CASD-NMR in the two completed rounds of the initiative.</p> <p>Also refer to http://www-nmr.cabm.rutgers.edu/blindtest/blind.html for additional details, including first release date and link to each final PDB entry</p>
Scores for calculating automated FAIR assessments in the low carbon energy domain
<p>Results for an automated FAIR assessment of 80 databases from the low carbon energy domain. The assessment was performed with the help of the FAIR maturity evaluation service of Wilkinson et al. The FAIR status with respect to 16 FAIR criteria is listed. The scores are defined to be consistent with the FAIR assessment tool of the Australian Research Data Commons. More details can be found in an additional publication on Zenodo as well as in an upcoming publication by Schwanitz et al.</p>
Replication package for "Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation"
<p>This repository contains the replication package for the paper "Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation" by Fernando Vallecillos Ruiz, Anastasiia Grishina, Max Hort and Leon Moonen, accepted for publication in ACM Transactions on Software Engineering and Methodology on 2025-10-09.</p> <p>A preprint is deposited on arXiv with DOI: <a href="https://doi.org/10.48550/arXiv.2401.07994">10.48550/arXiv.2401.07994</a>.</p> <p>The replication package is archived on Zenodo with DOI: <a href="https://doi.org/10.5281/zenodo.10500593">10.5281/zenodo.10500593</a>. It is maintained on GitHub at <a href="https://github.com/secureIT-project/RTT_for_APR">https://github.com/secureIT-project/RTT_for_APR</a>.</p> <p>This project builds on code from the <a href="https://github.com/lin-tan/clm/">clm</a> project, which is (c) 2023, The ASSET research group led by Lin Tan, Purdue University, licensed under the BSD 3-Clause License (see jasper/LICENSE.BSD). All modifications and new contributions are (c) 2025 by the authors of this replication package and distributed under the MIT License (see LICENSE.MIT). The data, models and preprint are distributed under the CC BY 4.0 license.</p> <h2>Citation<code> </code></h2> <p>If you build on this data or code, please cite this work by referring to the paper:</p> <div> <pre><code>@article{ruiz2025:rtt, title = {Assessing the Latent Automated Program Repair Capabilities of Large Language Models using Round-Trip Translation}, author = {Vallecillos Ruiz, Fernando and Anastasiia Grishina and Max Hort and Leon Moonen}, journal = {ACM Transactions on Software Engineering and Methodology (TOSEM)}, year = {2025}, publisher = {{ACM}} }</code></pre> </div> <h2>Organization</h2> <p>The replication package is organized as follows:</p> <ul> <li>clm-apr <ul> <li>plbart: code to generate patches with PLBART models.</li> <li>codet5: code to generate patches with CodeT5 models.</li> <li>transcoder: code to generate patches with the TransCoder model.</li> <li>incoder: code to generate patches with InCoder models.</li> <li>santacoder: code to generate patches with the SantaCoder model.</li> <li>starcoder: code to generate patches with the StarCoderBase model.</li> <li>quixbugs: code to validate patches generated for the QuixBugs benchmark.</li> <li>defects4j: code to validate patches generated for any of the Defects4J benchmarks.</li> <li>humaneval: code to validate patches generated for the HumanEval-Java benchmark.</li> </ul> </li> <li>humaneval-java: the HumanEval-Java benchmark proposed by Jiang et al. 2023</li> <li>jasper: a Java tool to parse Java programs needed to preprocess input.</li> <li>model: folder to download the language models.</li> <li>analysis_wandb: data from WandB and Jupyter notebook to create graphs.</li> <li>tmp_benchmarks: folder for temporary files used in patch validation. The folder may contain pairs of `paralell’ folders src and src_org for each benchmark, used to replace buggy code with candidate patches.</li> </ul> <h2>Replication</h2> <h3>Prerequisites</h3> <ul> <li>Python version: 3.8—3.10.</li> <li><a href="https://git-lfs.com/">Git LFS</a> is required for model downloading.</li> </ul> <h4>Weight and Biases (WandB)</h4> <ol> <li>Create an account on <a href="https://wandb.ai/">Weights and Biases</a></li> <li>Install the <a href="https://docs.wandb.ai/ref/python">Weights and Biases</a> library</li> <li>Run <code>wandb login</code> and follow the instructions</li> </ol> <h4>Set up OpenAI access</h4> <p>OpenAI account is needed with access to <code>gpt-3.5-turbo</code> and <code>gpt-4</code> . The <code>OPENAI_API_KEY</code> environment variable should be set to your OpenAI API access token.</p> <h3>Dependencies</h3> <ul> <li><a href="https://github.com/rjust/defects4j">Defects4J</a> - To generate inputs for the Defects4J datasets or to validate them, you need to have installed <a href="https://github.com/rjust/defects4j">their tool</a>.</li> <li>Java 8</li> <li>Apache Maven</li> </ul> <h3>Setup</h3> <p>We recommend the use of the setup script:</p> <pre><code>setup.sh </code></pre> <p>which performs the following:</p> <ol> <li>Creates a virtual environment for Python and activate it.</li> <li>Install the packages in <code>requirements.txt</code>.</li> <li>Compiles Jasper.</li> <li>Downloads parsers.</li> <li>Check if the Defects4J installation is correct.</li> </ol> <h3>Download models</h3> <p>The following bash script contains the code to download all of the models used:</p> <pre><code>models/download_models.sh </code></pre> <p>We recommend downloading only the models you are going to use due to their size</p> <pre><code>cd models chmod +x download_models.sh ./download_models.sh </code></pre> <p>To run one specific model, for example, PLBART (C#), use the following commands:</p> <pre><code>cd models git lfs install git clone https://huggingface.co/uclanlp/plbart-java-cs git clone https://huggingface.co/uclanlp/plbart-cs-java cd ../.. </code></pre> <h3>Step 1: Preprocessing and Prompting:</h3> <p>Each script in each <code>clm-apr/[model]</code> folder connects one or more models with<br>one dataset. These scripts follow the template: [benchmark]_[model]_[technique].py.<br>The scripts first create an <code>[model]_input.json</code> file with the preprocessed<br>input. Then generate outputs based on that file with one or more models.<br>For example:</p> <pre><code>cd clm-apr/plbart python quixbugs_plbart_round.py # Generates input for QuixBugs and generate patches using Java<->C# RTT. python quixbugs_plbart_round_nl.py # Generates input for QuixBugs and generate patches using Java<->NL RTT. </code></pre> <p>Optionally, use argument <code>--device_map cpu</code> if you wish to run the script on<br>CPU, for example:</p> <pre><code>python quixbugs_plbart_round.py --device_map cpu </code></pre> <p>Otherwise, the script will be run on all available CUDA GPU’s.</p> <p>We have commented the generation of inputs in the scripts. Users are free to<br>uncomment this method and try for themselves. It is easily recognizable by<br>their name template <code>[model]_[benchmark]_input()</code>. In the previous case:</p> <pre><code>quixbugs_plbart_input() </code></pre> <h3>Step 2 and 3: Round Trip Translation and Postprocessing</h3> <p>These steps are also included in the [benchmark]_[model]_[technique].py<br>script mentioned above. They are modularized in the method recognizable by<br>their name template [model]_[benchmark]_output().<br>For example:</p> <pre><code>quixbugs_incoder_output() </code></pre> <p>This method:</p> <ol> <li>Reads the input json file.</li> <li>Generates outputs through the LLM.</li> <li>Postprocess the output (extract the patch, clean up extra token, etc.).</li> <li>Creates [model]_output_[technique]_[extra].json.</li> </ol> <p>The last 3 steps are repeated according to the number of runs set to performed<br>(10 in our experiments). Each run will produce a different file with the seed<br>used in its generation. For example, <code>quixbugs\_plbart\_round.py</code> and<br><code>quixbugs\_plbart\_round_nl.py</code> scripts create:</p> <pre><code>clm-apr/quixbugs/plbart_results/run_0/plbart_java_cs_java_output_round_csharp_batch.json clm-apr/quixbugs/plbart_results/run_0/plbart_java_nl_java_output_round_nl_batch.json </code></pre> <h3>Step 4: Evaluation of RTT Results:</h3> <p>The last step evaluates the generated outputs against the test-suites of each<br>benchmark. This script reads the previous outputs files and generates a new one<br>with the results of the test for one model. Furthermore, it connects with the<br><em>WandB</em> tool to calculate metrics and send them to analyze.</p> <p>Following the previous examples, to validate the results previously obtained,<br>we execute the following:</p> <pre><code>cd clm-apr/quixbugs python validate_quixbugs_parallel.py </code></pre> <p>Given the included JSON, this script would create:</p> <pre><code>clm-apr/quixbugs/plbart_results/run_0/plbart_java_cs_java_validate_round_csharp_batch.json </code></pre> <p>We have disabled <em>WandB</em> in the script to allow users to try the script first.<br>However, it can be easily activated by changing the parameter <code>mode="disabled"</code><br>to <code>mode="online"</code>.<br>We have set the variable <code>total_runs = 1</code>, as well as <code>input_file</code> and <code>output_file</code><br>to the results included. They should be modified accordingly to validate more runs<br>or to validate other files/models.</p> <h3>Included Results</h3> <p>We include two CSV files obtained through WandB.</p> <pre><code>'data_cleaned_grouped.csv': Aggregated metrics of the 25 outputs for all runs. 'full_data_all_runs.csv': All metrics for all outputs on all runs. </code></pre> <h2>Changelog</h2> <ul> <li>v1.0 - updates corresponding to the accepted version of the manuscript in TOSEM</li> <li>v0.1 - initial replication package corresponding to v1 of arXiv deposit: includes raw data, code, and example outputs.</li> </ul> <h2>References</h2> <p>Jiang, N.; Liu, K.; Lutellier, T.; and Tan, L. 2023. Impact of Code Language<br>Models on Automated Program Repair. In 45th International Conference on<br>Software Engineering (ICSE), 1430–1442. IEEE. ISBN 978-1-66545-701-9.</p> <div> </div>
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning
<p>COVID-19 plasma samples spectrometry datasets for machine learning input. Used in the work of article Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning, currently under submittion.</p> <p>Abstract:</p> <p>COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) was enrolled from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules that are related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings the disease’s pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using Machine Learning algorithms, transforming this screening approach in a tool with great potential for real-world application. </p>
FAIRness Assessment of Biomedical Data Using Automated Tools (Dataset)
<p>The data were collected as part of a Master's thesis project aimed at evaluating various automated FAIR assessment tools, applying them to biomedical data. The data sets identifiers were gathered as part of the Open Data LoM and IoM incentivization at Charité Universitätsmedizin Berlin, available at <a title="Dataset of the results of data validation for articles from 2021" href="https://doi.org/10.5281/zenodo.8249758">https://doi.org/10.5281/zenodo.8249758</a>, and reused in this project.</p> <p>The data represents cleaned, aggregated, and transformed results obtained from the API services of the following FAIR assessment tools: F-UJI, FAIR Enough, FAIR-Checker, and FAIR EVA.</p> <p>The raw data in .Rdata format will be shared on GitHub repository at <a title="FAIR Tools Analysis" href="https://github.com/anastasiabright/fair-tools-analysis">https://github.com/anastasiabright/fair-tools-analysis</a>.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 16. Architecture for Affective Situation Assessment of Perceptual Images (Internal Connections between Emotions are not Depicted for Better Clarity of the Graphic)
<p>Based on the concept of affective neuro-symbols, a model was developed according to<br> which emotions can be represented by affective neuro-symbolic networks (see right half of Figure<br> 16, referred to as architecture of “internal perception” in contrast to the “external perception”<br> architecture of the left half of Figure 16, which has already been presented in Section 4.2).<br> The individual affective neuro-symbols (depicted as circles) represent different emotions<br> (fear, anger, guilt, joy, rage, panic, love, happiness, etc.).</p>
An Online Integrated Development Environment for Automated Programming Assessment Systems Open Source Data
<p>This dataset accompanies the paper <em>"An Online Integrated Development Environment for Automated Programming Assessment Systems"</em>. It contains data from the usability evaluation of a feature-rich online IDE designed for integration into Automated Programming Assessment Systems (APASs). The dataset includes survey responses from 27 participants based on the Technology Acceptance Model (TAM), performance metrics such as memory usage, and qualitative user feedback. The study highlights challenges in integrating online IDEs with APASs, such as memory efficiency, load balancing, and user experience. The dataset supports further research in developing scalable, effective, and user-friendly programming education tools.<br><br>Here you can find the code changes required for the online IDE in Artemis: <a href="https://github.com/ls1intum/Artemis/pull/6706/files" target="_blank" rel="noopener">Github</a></p>
Dataset for the article: Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment
<p>This open access repository houses the dataset utilized in the research article:</p><p>Pankiewicz, M. (2023). Evaluating the Predictive Performance of Quick Methods for Estimating Task Difficulty and Student Ability in Automated Computer Programming Assessment. In T. Bastiaens (Ed.), Proceedings of EdMedia + Innovate Learning (pp. 1413-1418). Vienna, Austria: Association for the Advancement of Computing in Education (AACE). Retrieved from https://www.learntechlib.org/primary/p/222666</p><p>The repository includes these files:</p><p>"submissions.csv": This data file captures the evaluation results of programming assignments. It is organized by the following columns:</p><p> </p><ul><li>"user_id": The unique identifier for each student who submitted the assignment.</li><li>"task_id": The unique identifier for each task that received submissions.</li><li>"submission_seconds": The number of seconds since the first user accessed the initial task's description within the system.</li><li>"correct": The outcome of the evaluation (1 denotes correct; 0 denotes incorrect).</li><li>"subject": The specific subject matter that the task addresses.</li></ul><p> </p><p>"subjects.csv": This data file comprises the roster of subjects for which tasks have been assigned within the system. It includes these columns:</p><p> </p><ul><li>"subject_id": The unique identifier for each subject.</li><li>"subject": The actual name of the subject.</li></ul><p> </p>
EvalQuiz - LLM-based Automated Generation of Self-Assessment Quizzes in Software Engineering Education
<p>Self-assessment quizzes after lectures, educational videos, or chapters are a commonly used method in software engineering (SE) education to give students the opportunity to test their gained knowledge. However, the creation of these quizzes is time-consuming, cognitively exhausting, and complex, as an expert in the field needs to create the quizzes and review the lecture material for validity. Therefore, this paper presents a concept to automatically generate self-assessment quizzes based on lecture material using a large language model (LLM) to reduce lecturers' workload and simplify the general quiz creation process. The developed prototype was handed to experts, who subsequently evaluated the approach. The results show that automatic quiz generation saves time and the quizzes cover the delivered lecture material well. However, the generated quizzes often lack originality and versatility. Therefore, further prompt engineering might be required to achieve more elaborate results.</p>
The dataset of the ASE'20 paper titled "Automated Patch Correctness Assessment: How Far are We?"
<p>This is the experiment result of the ASE'20 paper titled "<strong>Automated Patch Correctness Assessment: How Far are We?</strong>".</p> <p>If you use our data for academic research, please cite our paper as:</p> <pre><code class="language-html">@inproceedings{wang2020automated, title={Automated Patch Correctness Assessment: How Far are We?}, author={Wang, Shangwen and Wen, Ming and Lin, Bo and Wu, Hongjun and Qin, Yihao and Zou, Deqing and Mao, Xiaoguang and Jin, Hai}, booktitle={Proceedings of the 35th International Conference on Automated Software Engineering (ASE)}, year={2020}, organization={ACM} }</code></pre> <p>The file <strong><em>Patches.zip</em></strong> includes all the patches we take into consideration in this study. Note that 269 patches come from "<a href="http://arxiv.org/pdf/1909.13694">Automated Patch Assessment for Program Repair at Scale</a> (Ye et al.), Technical report 1909.13694, arXiv, 2019".</p> <p>The file <em><strong>Patches_for_Static</strong></em> include all the class files we used for static method.</p> <p>The file <em><strong>Tests-oracle </strong></em>includes all the test cases generated by <strong>Evosuite</strong> and <strong>Randoop</strong> on the fixed version programs.</p> <p>The file <em><strong>Tests-buggy</strong></em> includes all the test cases generated by <strong>Evosuite</strong> and <strong>Randoop</strong> on the buggy version programs.</p> <p>The file <em><strong>DiffTGen-result</strong></em> includes ingredients and output information of <strong>DiffTGen</strong>.</p> <p>The file <em><strong>Daikon-output</strong></em> includes inferred invariants of each patch and its corresponding ground-truth.</p> <p>The file <em><strong>PATCH-SIM_result</strong></em> includes the output vector files from <strong>PATCH-SIM</strong> and<strong> E-PATCH-SIM</strong>.</p> <p>The file <em><strong>Training_result</strong></em> includes the output of six ML algorithms with or without oracle.</p> <pre><code class="language-xml">Chart: 1-26; Closure: 14, 18, 31, 33, 38, 40, 57, 62, 63, 70, 73, 86, 92, 93, 115, 123, 126; Lang: 6, 7, 10, 16, 20, 21, 22, 24, 26, 27, 33, 35, 38, 39, 41, 43, 44, 45, 50, 51, 55, 57, 58, 59, 60, 61, 63; Math: 2, 3, 4, 5, 6, 8, 20, 22, 25, 28, 30, 31, 32, 33, 34, 35, 39, 41, 49, 50, 53, 56, 57, 58, 59, 60, 61, 63, 65, 68, 70, 71, 73, 74, 75, 79, 80, 81, 82, 85, 86, 88, 89, 90, 93, 97, 98, 99, 104; Time: 4, 7, 11, 14, 15, 19. </code></pre> <p>Please note that for bugs in the above table, the Evosuite tests on the fixed version programs are reused from <a href="https://arxiv.org/abs/1909.13694">a previous study</a>. We thank <strong>He Ye</strong>, <strong>Matias Martinez</strong>, and <strong>Martin Monperrus</strong> so much for sharing their data.</p> <p> </p> <p><strong>Notice!</strong> For patches under the folder <em>Patches_ICSE</em>, those under <em>Ddifferent</em> and <em>Dsame</em> folders are all correct patches. <em>Different</em> and <em>Same</em> only indicate whether the patch is syntactically identical to the ground truth patch.</p> <p>Patches generated for Mockito project (2 in total): Kali-A-Mockito-10; Arja-Mockito-10</p> <p>Patches do not pass plausibility check (6 in total): Kali-Closure-133; kPAR-Chart-12; FixMiner-Chart-12; patch1-Lang-6-SketchFix-plausible; patch2-Lang-6-SketchFix-plausible; patch1-Math-2-SOFix</p> <p>Patches that are mistakenly labeled (12 in total): patch2-Lang-51-Jaid; patch1-Lang-43-CapGen; patch2-Lang-43-CapGen; patch2-Math-53-CapGen; patch2-Math-53-Jaid; jKali-Lang-7; ACS-Lang-35; Arja-Math-35; SimFix-Math-72; SimFix-Closure-19; Arja-Math-50; SimFix-Lang-60</p> <p>Detailed reasons for the mislabeled patches: 1. the ground-truth patch modifies multiple locations while the generated patch only modifies one of them (2/12, SimFix-Math-72, SimFix-Lang-60); 2. the edit points in the generated patch are different from those in ground-truth patch (8/12, patch2-Lang-51-Jaid, patch2-Math-53-Jaid, patch1-Lang-43-CapGen, patch2-Lang-43-CapGen, patch2-Math-53-CapGen, ACS-Lang-35, SimFix-Closure-19, Arja-Math-50); 3. the generated patch doesnot fulfill the intended function in ground-truth (2/12, jKali-Lang-7, Arja-Math-35).</p> <p>Take <em>Arja-Math-50</em> as an example, this patch deletes a conditional statement which deals with an unexpected input (<em>null</em>) in the method <em><strong>verifyBracketing</strong></em>.<em><strong> </strong></em>However, in the oracle program, this conditional statement still exists. Then, <strong>Randoop</strong> generated a test case by calling <strong><em>verifyBracketing</em></strong> with a <em>null</em> argument. This test passed on the ground-truthpatch but failed on the patch generated by Arja due to the removeof the exception handling statements. As a result, this patch is actually overfitting but mistakenly labeled as correct. We have confirmed this case with Kui Liu, the first author of the recent ICSE'20 paper (Title: <em>On the Efficiency of Test Suite based Program Repair</em>) which makes up our patch benchmark.</p> <p> </p> <p>Border line Patches (3 in total): ACS-Lang-7; kPAR-Lang-7; TBar-Lang-7. Reasons for overfitting: Evosuite generates some tests that fail on those patches, e.g., test049 in Seed 1; the Java documentation above the function states that it needs to deal with the situation where the input cannot be converted. Reasons for correct: it synthesizes the correct modification; currently, in the program, <em>createBigDecimal()</em> is not called directly in other part of the production code except <em>createNumber()</em> and the test code. In our paper, we consider these three patches as correct and that's why Evosuite has 3 false positives.</p>
Supplementary material for paper: Unified Functional Safety Assessment of Industrial Automation Systems
<p>This collection contains supplementary material for the paper titled <strong>Unified Functional Safety Assessment of Industrial Automation Systems</strong> accepted in IEEE TII. This archive provides all information needed to recreate the results presented in the paper including the following:</p> <ol> <li>A technical report presenting proofs of trace equivalence between Function Block Networks and the generated PRISM Models.</li> <li>A set of examples and benchmarks used for experimentation in the said research paper.</li> <li>The results of the analysis presented in the paper are also contained here in the form of spreadsheets contained in file <em>PRISMResults.xlsx</em>.</li> <li>Each of the included sub-folders contains a README.md file describing the system and provides instructions to recreate these results.</li> </ol>
Dataset related to the publication "Procedure for automated low uncertainty assessment of empty cavity mode frequencies in Fabry-Pérot cavity based refractometry"
<p>The data set consists of; The published paper, all figures that present measurement or simulation data in .png and .fig format and the underlying data plotted in the figures in text format. The published plots were generated from the fig files. The text files were generated by reading the plotted data from the fig files. The files are named Fig_XX were XX corresponds to the figure number in the publication. The format of the text file is as follows. Before every data set there is a header consisting of; The number of the subplot where the data is plotted (Plot: XX), the number of the data set in the sub plot (DataSet: XX), and the color of the line or marker in the plot (Color: XX). The description of what each color represents can be found in the publication.</p>
One-Dimensional Mathematical Model-Based Automated Assessment of Fractional Flow Reserve
ClinicalTrials.gov study NCT03797118. IPD Sharing: NO. Countries: 1. Publications: 1.
Study to Assess in Home Use of Evolocumab (AMG 145) Administration Using Either an Automated Mini-doser or a Prefilled Autoinjector/Pen
ClinicalTrials.gov study NCT01879319. IPD Sharing: Not stated. Countries: 2. Publications: 4.
Flow virometry for water-quality assessment: Protocol optimization for a model virus and automation of data analysis
Open the record for dataset details and reuse information.
Automated Patch Assessment for Program Repair at Scale
<p>In this package, we share 257 patches from Dcorrect and 381 patches from Doverfitting.</p> <p>We also a script used in our experiment for doing sanity checks and automated patch assessments.</p> <p> </p> <p>If you use this dataset, please cite:</p> <pre><code>@Article{Ye2021EMSE, author = {Ye, He and Martinez, Matias and Monperrus, Martin}, title = "Automated Patch Assessment for Program Repair at Scale", journal="Empirical Software Engineering", volume = "26", issn = "1573-7616", doi = "https://doi.org/10.1007/s10664-020-09920-w", year = "2021" } </code></pre> <p> </p>
Automated analysis of scanning electron microscopic images for assessment of hair surface damage
<p>Mechanical damage of hair can serve as an indicator of health status and its assessment relies on the measurement of morphological features via microscopic analysis, yet few studies have categorized the extent of damage sustained, and instead, have depended on qualitative profiling based on the presence or absence of specific features. We describe the development and application of a novel quantitative measure for scoring hair surface damage in scanning electron microscopic (SEM) images without predefined features, and automation of image analysis for characterization of morphological hair damage after exposure to an explosive blast. Application of an automated normalization procedure for SEM images revealed features indicative of contact with materials in an explosive device and characteristic of heat damage, though many were similar to features from physical and chemical weathering. Assessment of hair damage with tailing factor, a measure of asymmetry in pixel brightness histograms and proxy for surface roughness, yielded 81% classification accuracy to an existing damage classification system, indicating good agreement between the two metrics. Further ability of tailing factor to score features of hair damage reflecting explosion conditions demonstrates the broad applicability of the metric to assess damage to hairs containing a diverse set of morphological features. </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.