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37 results for “Automated Software”
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>
Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"
<p><strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong></p> <p><strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> </p> <p><em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis</p> <p> - "main-program-grid-search.py" file: Main program executing all experiments for the grid search analysis</p> <p><em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.</p>
Replication Data for "Exploring Developer Views on Software Carbon Footprint and its Potential for Automated Reduction"
<p># Replication Data for "Exploring Developer Views on Software Carbon Footprint and its Potential for Automated Reduction"</p> <p>## Overview</p> <p>Reducing software carbon footprint could contribute to efforts to avert climate change. Past research indicates that developers lack knowledge on energy consumption and carbon footprint, and existing reduction guidelines are difficult to apply. Therefore, we propose that automated reduction methods should be explored. However, such tools must be voluntarily adopted and regularly used to have an impact.</p> <p>In this study, we have conducted interviews and a survey (a) to explore developers' existing opinions, knowledge, and practices with regard to carbon footprint and energy consumption, and (b), to identify the requirements that automated reduction tools must meet to ensure adoption. Our findings offer a foundation for future research on practices, guidelines, and automated tools that address software carbon footprint.</p> <p>## Data Contained in This Package</p> <p>- interview_survey_guide.pdf</p> <p>This file contains the interview and survey questions.</p> <p>- interview_responses.docx</p> <p>This file contains relevant material from the interviews.</p> <p>- survey_responses.xlsx</p> <p>This file contains all survey responses.</p> <p>Both interview and survey data has been anonymized to protect the privacy of the participants.</p>
Dataset for the workshop paper titled "Automating GUI-based Software Testing with GPT-3" published at AIST 2023
<p>The training dataset for the research paper "Automating GUI-based Software Testing with GPT-3" presented at the 3rd International Workshop on Artificial Intelligence in Software Testing (AIST 2023), which was a part of the 16th IEEE International Conference on Software Testing, Verification and Validation (ICST 2023). The dataset contains prompt completion pairs acquired through user interaction with the software and was used to fine-tune the GPT-3 model. The dataset is in the .jsonl format specified by OpenAI.</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>
Automated improvement of stickleback reference genome assemblies with Lep-Anchor software
Open the record for dataset details and reuse information.
Filling the Gap: A Tool to Automate Parameter Estimation for Software Performance Models
<p>The data part of this release support the results <br /> presented in the paper <br /> "Filling the Gap: A Tool to Automate Parameter Estimation<br /> for Software Performance Models", by W. Wang, J. F. Perez, and G. Casale, accepted <br /> to QUDOS workshop 2015. </p>
Replication Package for "Mitigating Automated Obfuscation Attacks on Software Plagiarism Detection Systems"
<p>This is the replication package for the doctoral dissertation titled "<em>Mitigating Automated Obfuscation Attacks on Software Plagiarism Detection Systems</em>".</p> <p>The contributions of the dissertation were also integrated into the source code plagiarism detection system <a href="https://github.com/jplag/JPlag/">JPlag</a> to ensure they are widely accessible.</p> <p><strong>Contents Overview:</strong></p> <p>- <strong>Datasets</strong>: the artifacts of the evaluation datasets.<br>- <strong>Raw Results</strong>: the measured results of our evaluation.<br>- <strong>Evaluation Scripts</strong>: the evaluation code for plotting and statistical tests.<br>- <strong>Implementation</strong>: the source code of the JPlag-based implementation and the prebuilt application as a JAR file.<br>- <strong>Other</strong>: additional plots.</p>
Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery - Dataset
<h1>Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery - Dataset</h1> <h2> </h2> <h2>Dataset corresponding to <a href="https://doi.org/10.1145/3672608.3707798">https://doi.org/10.1145/3672608.3707798</a></h2> <div>This is the dataset corresponding to <a href="https://doi.org/10.1145/3672608.3707798">https://doi.org/10.1145/3672608.3707798</a> "Supporting Software Engineers in IT Security and Privacy through Automated Knowledge Discovery" with the goal of providing a systematic method to discover state-of-the-art security knowledge, focused on threats, measures, and properties from science and project literature with minimal manual effort, and providing software engineers with the awareness and means they need to apply the knowledge to their projects.</div> <p> </p> <h2>Structure of the files</h2> <div>The dataset contains the extraction prompt, the extraction results, and metadata.</div> <div>- <strong>ACM_IEEE_EU_Results.zip</strong> is an archive file with the metadata and extraction results with the following folder structure: </div> <div> - <strong>Meta/ </strong>contains the DOI, title, publication date, and pages (omitted for IEEE since it may contain intellectual property).</div> <div> - <strong>Extract/</strong> contains extraction results generated by the LLM in response to the extraction_prompt.txt file.</div> <div> - <strong>ExtractMeta/</strong> contains the metadata for the extraction, such as execution time.</div> <div>- <strong>extraction_prompt.txt</strong> is the prompt used to extract information from the science and project publications.</div>
Study Data: Semi-Automated Prioritization of Industrial Security Findings in Agile Software Development
<p>Dataset for the study of the paper "Semi-Automated Prioritization of Industrial Security Findings in Agile Software Development"</p>
Leveraging Relational Concept Analysis for Automated Feature Location in Software Product Lines - Artefacts DataSet
<p>This Archive contains the Artefact of the paper, submitted at GPCE2021 :<br> <a href="https://doi.org/10.1145/3486609.3487208">Leveraging Relational Concept Analysis for Automated Feature Location in Software Product Lines</a></p> <p> </p> <p>It contains the dataset and the results of our Feature Location techniques when applied to this dataset.</p>
Supplementary Material - Dataset for "Automating Quantum Software Maintenance: Flakiness Detection and Root Cause Analysis"
<h2>README</h2> <p>The dataset consists of the following components:<br> <br>- `<strong>prompts.txt</strong>`: This file contains the prompts used for large language models.<br> <br>- `<strong>Dataset</strong>` directory: includes general information about the dataset. Specifically, the `dataset.xlsx` file lists flaky and non-flaky tests, along with their root causes and fix types.<br> <br>- `<strong>Full</strong>` directory contains two subdirectories: `Flaky` and `Non-flaky`. Each of these directories is organized by individual GitHub organization projects, with each project having its list of repository subdirectories. These subdirectories are further divided into “issues” and “pull requests” (PRs).</p> <p><br>- `<strong>Method</strong>` level subdirectory has a similar structure but contains extracted code snippets at the method level instead of full code listings. The `code.diff` file is copied over and left unaltered. </p> <p><br>- <strong>Issue Directories (IRs):</strong> Named with an `issueID` template, each issue directory contains a `log.issue` file that includes the extracted description, comments, and metadata.<br> <br>- <strong>PR Directories (PRs)</strong>: Named using the `prID` template, each PR directory contains the text, comments, and metadata in the `pr.log` file. The text of the associated issue is stored in the `log.issue` file. Code listings are stored in a file with the `.bug` suffix, while the corresponding fixed version is in a `.fix` file. The `code.diff` file contains the patch that transforms the `.bug` version into the `.fix` version.</p> <p><br><strong>Additional notes:</strong><br>Issues with associated pull requests in `dataset.xlsx` are combined into the pull request directory template. If two pull requests are listed for a row, a PR directory is created for each. Due to updates in the extended dataset, some repositories have been renamed or archived, meaning the current repository directory names in `Dataset` will include both the previous and new names if it has been changed (e.g., a repository previously saved as Qiskit/qiskit-terra may now be saved as Qiskit/qiskit following the renaming from qiskit-terra to qiskit).</p> <h2>Directory Structure:</h2> <p><br>├── prompts.txt<br>├── Dataset/<br> └── dataset.xlsx<br>├── Full/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-Flaky/<br> └── <Organization>/<Repository>/...<br>├── Method/<br> ├── Flaky/<br> └── <Organization>/<Repository>/...<br> ├── Non-flaky/<br> └── <Organization>/<Repository>/...</p> <p> </p>
Automated Support for Searching and Selecting Evidence in Software Engineering: A Cross-domain Systematic Mapping
<p>Dataset -- Brief summary of the automated approaches for searching and selecting studies for secondary studies in software engineering </p>
Automated Support for Searching and SelectingEvidence in Software Engineering: A Cross-domainSystematic Mapping
<p>Data from the studies that adrress search and selection approaches. </p>
Automated refactoring actions for performance improvement in component-based software engineering
<p>Dataset of UML models randomly generated to validate the portfolio of refactoring actions.</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>
Dataset for Thesis "Automated Consistency of Legal and Software Architecture System Specifications for Data Protection Analysis"
<p>Dataset for Thesis "Automated Consistency of Legal and Software Architecture System Specifications for Data Protection Analysis"</p>
Automated UltraSound Software for Identification of Lumbar Vertebral Levels
ClinicalTrials.gov study NCT02982317. IPD Sharing: NO. Countries: 1. Publications: 1.
Acceptance and Commitment Therapy Delivered by Automated Software Messaging
ClinicalTrials.gov study NCT04329897. IPD Sharing: NO. Countries: 1. Publications: 6.
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International Brain Laboratory public data
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OpenNeuro
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