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21 results for “debugging”
Debugging Questionnaire Dataset
<pre>We conducted an online questionnaire with 102 participants where we asked them about a recently fixed bug. The collected data contains for each bug failure and fault types, the time required to reproduce, localize, and correct the bug, the amount of necessary code changes, and the used debugging method. </pre>
TDA4ContextualEmbeddings - Public - Debug Data for the codebase of the publication "Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction"
<p>Debug dataset for testing the <a href="https://gitlab.cs.uni-duesseldorf.de/general/dsml/tda4contextualembeddings-public">codebase</a> of the paper <a href="https://doi.org/10.18653/v1/2024.sigdial-1.31">“Local Topology Measures of Contextual Language Model Latent Spaces With Applications to Dialogue Term Extraction”</a> published at the 25th Meeting of the Special Interest Group on Discourse and Dialogue, Kyoto, Japan (SIGDIAL 2024).</p>
Supplemental Material for a Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches
<p>Bug benchmarks are used in development and evaluation of debugging approaches. Quantitative performance comparison of different debugging approaches is only possible when they have been evaluated on the same dataset or benchmark. However, benchmarks are often specialized towards usage for certain debugging approaches in their contained data, metrics, and artifacts. Such benchmarks can not be easily used on debugging approaches outside their scope as such approach may rely on specific data such as bug reports or code metrics not included in the dataset. Furthermore, benchmarks vary in their size w.r.t. the number of subject programs and the size of the individual subject programs. For these reasons, we have performed a systematic literature review where we have identified 73 benchmarks that can be used to evaluate debugging approaches.</p> <p>We compare the different benchmarks with respect to their size and the provided information such as bug reports, contained test cases, and other code metrics. Furthermore, we have investigated how well the benchmarks realize the <a href="https://www.go-fair.org/fair-principles/">FAIR guiding principles</a>. This comparison is intended to help researchers to quickly identify all suitable benchmarks for evaluating their specific debugging approaches. More information can be found in the publication:</p> <blockquote> <p>Thomas Hirsch and Birgit Hofer: "A Systematic Literature Review on Benchmarks for Evaluating Debugging Approaches", Journal of Systems and Software, in press, 2022.</p> </blockquote>
Spreadsheet Debugging: The Perils of Tool Over-reliance
<p>Spreadsheets are widely used in organizations for various purposes such as data aggregation, reporting and decision-making. Since spreadsheets, like other types of software, can contain faulty formulas, it is important to provide developers with appropriate methods to find and fix such faults. Recently, various heuristic and statistics-based fault identification methods were proposed, which point developers to potentially faulty parts of the spreadsheets. Due to their heuristic nature, these methods might, however, miss some faults. As a result, if spreadsheet developers rely too strongly on these methods, they might not pay sufficient attention to problems that are not pinpointed by the methods. In this research, we are the first to study this potential problem of over-reliance in spreadsheet debugging, which may lead to limited debugging effectiveness. We report the outcome of a controlled experiment where 59 participants were tasked to find faulty formulas in a given spreadsheet with and without support of a novel spreadsheet debugging tool. </p> <p>The provided dataset contains detailed results of the study, the spreadsheet that was used for the study, and other supporting materials.</p>
A Clustering-Based Approach for Visual Debugging of Concurrent Java Programs
<p>Supplementary Material to: A Clustering-Based Approach for Visual Debugging of Concurrent Java Programs</p>
Evaluation of DebIso, a learning platform for systematic debugging
<p>This is the collection of data, produced during the evaluation of DebIso, a learning platform for systematic debugging.</p> <p>See <a href="https://doi.org/10.1145/3677619.3678105">https://doi.org/10.1145/3677619.3678105</a> for the related publication.</p>
Data Set for Article "Verification-Aided Debugging: An Interactive Web-Service for Exploring Error Witnesses", Proc. CAV'16
<p>This is the description of the supplementary archive of example interactive reports for the approach described in the article "Verification-Aided Debugging: An Interactive Web-Service for Exploring Error Witnesses", Proc. CAV'16.</p> <p>This archive contains a static snapshot of our system that allows the reader to<br> a) experience the features of our web-service without relying on its online availability and<br> b) reproduce the bug reports displayed in this static snapshot by validating the provided witnesses against the source code and the corresponding specifications using CPAchecker.</p> <p>The witness database is available at:<br> static/index.html<br> The supplied verification tasks can be found at:<br> static/programs/<br> The supplied error witnesses are grouped by their corresponding verification tasks and can be found at:<br> static/witnesses/<br> The software verifier CPAchecker is placed at:<br> CPAchecker/</p> <p>To browse the witness database and explore the supplied error reports, we recommend using the Firefox web browser,<br> because not all features of our bug reports are guaranteed to be available in other browsers.</p> <p>Like the supplementary archive originally provided to the reviewers, this witness database contains only a small selection of the witnesses harvested from the "Competition on Software Verification 2016", because we do not want to burden the reader with an enormous amount of data that likely is not relevant for understanding the concepts. Also, error witnesses produced by some competition candidates that were not even syntactically correct were removed, because they do not add any value to the evaluation. However, the full data is still available online via our web service, for example, the list of witnesses for a verification task can be requested by computing the SHA-1 hash of the verification task's source code and submitting the following query:<br> http://vcloud.sosy-lab.org/webclient/master/witness?inputFile=<program-hash><br> The resulting JSON data contains all hashes of witnesses stored for the given program.<br> A witness stored in the database can be requested via its SHA-1 hash by submitting the following query:<br> https://vcloud.sosy-lab.org/webclient/files/<hash><br> All verification tasks are available at the SV-COMP repository:<br> https://github.com/dbeyer/sv-benchmarks<br> If you use verification tasks from the repository and are interested in validating witnesses produced for SV-COMP '16,<br> please use the 'svcomp16' tag, because the tasks and their hashes might have changed since then.</p> <p>You can use CPAchecker to validate a witness for a verification task and generate an error report.<br> First, navigate to the CPAchecker directory:</p> <p> cd CPAchecker/</p> <p>Now, perform the validation by providing the verification task (consisting of specification and program source code) and a witness:</p> <p> scripts/cpa.sh -generateReport -witness-validation \<br> -spec <specification> \<br> <source-code> \<br> -spec <witness></p> <p>For example:</p> <p> scripts/cpa.sh -generateReport -witness-validation \<br> -spec ../static/programs/loop-acceleration/ALL.prp \<br> ../static/programs/loop-acceleration/array_false-unreach-call3.i \<br> -spec ../static/witnesses/loop-acceleration/array_false-unreach-call3.i/a4572a0c1b505b1d1170b7347e48a2a93cb3f4c1</p> <p>The report will be generated in the subdirectory<br> output/report/</p> <p> </p>
XRP Ledger Consensus Protocol Debug-level Log Traces
<p>A dataset of log traces from the consensus protocol of a <a href="https://github.com/ripple/rippled">rippled server</a> instance. The traces are filtered at the debug (DBG) level. Each file contains a separate trace, representing a full round of the consensus protocol.</p>
Bugs4Q: A Benchmark of Existing Bugs to Enable Controlled Testing and Debugging Studies for Quantum Programs
<p>Realistic benchmarks of reproducible bugs and fixes are vital to good experimental evaluation of debugging and testing approaches. Bugs4Q is a benchmark of forty-two real, manually validated Qiskit bugs from three popular platforms (GitHub, StackOverflow, and Stack Exchange) in programming, supplemented with test cases to reproduce buggy behaviors. Bugs4Q Database allows users to access the bugs we collected directly. Bugs4Q Framework provides interfaces for accessing the buggy and fixed versions of the Qiskit programs and executing the corresponding source code and unit tests, facilitating reproducible empirical studies and comparisons of Qiskit program debugging and testing tools.</p>
ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging
<p>Dataset for the paper: <em>ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging.</em></p> <div> </div>
Debugging Dataset for Scivision
<p>Zip file dataset with 1 image per sub-directory, to allow debugging of Scivision data loading from non-flat Zenodo zip files</p>
Data from: Debugging diversity – a pan‐continental exploration of the potential of terrestrial blood‐feeding leeches as a vertebrate monitoring tool
The use of environmental DNA (eDNA) has become an applicable non-invasive tool with which to obtain information about biodiversity. A sub-discipline of eDNA is iDNA (invertebrate-derived DNA), where genetic material ingested by invertebrates is used to characterise the biodiversity of the species that served as hosts. While promising, these techniques are still in their infancy, as they have only been explored on limited numbers of samples from only a single or a few different locations. In this study, we investigate the suitability of iDNA extracted from more than 3,000 haematophagous terrestrial leeches as a tool for detecting a wide range of terrestrial vertebrates across five different geographical regions on three different continents. These regions cover almost the full geographical range of haematophagous terrestrial leeches, thus representing all parts of the world where this method might apply. We identify host taxa through metabarcoding coupled with high-throughput sequencing on Illumina and IonTorrent sequencing platforms to decrease economic costs and workload and thereby make the approach attractive for practitioners in conservation management. We identified hosts in four different taxonomic vertebrate classes: mammals, birds, reptiles, and amphibians, belonging to at least 42 different taxonomic families. We find that vertebrate blood ingested by haematophagous terrestrial leeches throughout their distribution is a viable source of DNA with which to examine a wide range of vertebrates. Thus, this study provides encouraging support for the potential of haematophagous terrestrial leeches as a tool for detecting and monitoring terrestrial vertebrate biodiversity.
SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning
<p>This is a dataset for our paper: "SixthSense: Debugging Convergence Problems in Probabilistic Programs via Program Representation Learning" published at FASE 2022. Find more details at https://github.com/uiuc-arc/sixthsense</p>
Data repository Childprogramming + Debugging + Robotics
<p>Este repositorio de datos alberga información sobre las investigaciones realizadas en el marco de dos investigaciones relacionadas y centradas en el fomento del pensamiento computacional en la educación primaria mediante la metodología Childprogramming. El repositorio sirve de eje central para almacenar y organizar datos valiosos relativos a la aplicación de conceptos de depuración y robótica educativa. Contiene información relativa a los proyectos realizados en este ámbito. Incluye los resultados de los casos de estudio aplicados.</p> <p> </p>
Supplemental material for: Automatic Debugging of Design Faults in MapReduce applications
<p>This is the supplemental material of the paper titled as “Automatic Debugging of Design Faults in MapReduce applications” published in IEEE Transactions on Software Engineering. <a href="https://doi.org/10.1109/TSE.2024.3369766" target="_blank" rel="noopener">Link</a></p> <p> </p> <p>It contains both the test cases used in the evaluation and the statistical analysis to reproduce the experiments. The supplemental material contains the following files:</p> <ul> <li>1_testCases.zip: all test cases randomly generated for the experiments. The description of the test cases is in ./1_testCases/README.txt</li> <li>2_executionTestCases.zip: the aggregated data obtained after the execution of the test cases in the debugging techniques: fault localization technique (MRDebug-FL), input reduction technique (MRDebug-IR) and the combination of both techniques (MRDebug-IR-FL). The folder contains csv with the results the experimentation unit, and they are detailed in the ./2_executionTestCases/README.pdf file.</li> <li>3_notebook.zip: jupyter notebook that contains the analysis done in the experiments. This notebook allows the interactive execution of statistical test and plots. The instructions to install the notebook are in the file 3_notebook.zip/installation.txt.</li> </ul> <p> </p> <p>To cite this work: </p> <p>J. Morán, A. Bertolino, C. de la Riva and J. Tuya, "Automatic Debugging of Design Faults in MapReduce Applications," in <em>IEEE Transactions on Software Engineering</em>, vol. 50, no. 4, pp. 956-978, April 2024, doi: 10.1109/TSE.2024.3369766</p>
Replication package for "An Exploratory Eye Tracking Study on How Developers Classify and Debug Python Code in Different Paradigms"
<p>See the README.md file for more details.</p>
Data from: Debugging diversity – a pan‐continental exploration of the potential of terrestrial blood‐feeding leeches as a vertebrate monitoring tool
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DeSQL: Interactive Debugging of SQL in Data-Intensive Scalable Computing
<p>Anonymous Anonymous Anonymous</p>
ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging
<p>Dataset for the paper: <em>ClearCausal: Cross Layer Causal Analysis for Automatic Microservice Performance Debugging.</em></p>
Tracing Data for Unveiling the Energy Vampires: A Methodology for Debugging Software Energy Consumption
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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.