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677 results for “Replication package”
Replication Package for "PyTraceBERT: Python Traceback-based Language Model for Detecting Compatibility Issues in Deep Learning Systems"
<p>This package contains the traceback data, pre-trained models, and static word embeddings used in the paper, PyTraceBERT: Python Traceback-based Language Model for Detecting Compatibility Issues in Deep Learning Systems.</p>
Replication Package For An Extended Study of Syntactic Breaking Changes in the Wild
<p>This is the replication package associated with the paper titled 'An Extended Study of Syntactic Breaking Changes in the Wild' published under the Empirical Software Engineering journal.<br>Modern software applications rely heavily on the usage of libraries, which provide reusable functionality, to accelerate the development process. As libraries evolve and release new versions, the software systems that depend on those libraries (the clients) should update their dependencies to use these new versions as the new release could, for example, include critical fixes for security vulnerabilities. However, updating is not always a smooth process, as it can result in software failures in the clients if the new version<br>includes breaking changes. Yet, there is little research on how these breaking changes impact the client projects in the wild. <br>To identify if changes between two library versions cause breaking changes at the client end, we perform an empirical study on Java projects built using Maven. For the analysis, we used 18,415 Maven artifacts, which declared 142,355 direct dependencies, of which 71.60% were not up-to-date. We updated these dependencies and found<br>that 11.58% of the dependency updates contain breaking changes that impact the client. We further analyzed these changes in the library which impact the client projects and examine if libraries have adhered to the semantic versioning scheme when introducing breaking changes in their releases. Our results show that changes in transitive dependencies were a major factor in introducing breaking changes during dependency updates and almost half of the detected client impacting breaking changes violate the semantic versioning scheme by introducing breaking changes in non-Major upda</p>
Replication Data for the retroharmonize R Package Case Study: Working With Arab Barometer Surveys
<p>Replication datasets for the <a href="https://retroharmonize.dataobservatory.eu/articles/arabbarometer.html">retroharmonize Case Study: Working With Arab Barometer Surveys</a></p>
Replication Package for ICSE'21 paper - Representation of Developer Expertise in Open Source Software
<p>Replication package for ICSE'21 paper: Representation of Developer Expertise in Open Source Software.</p> <p>See README for details.</p>
Replication package for the paper "What do Developers Discuss about Code Comments"
<pre><code class="language-markdown"># RP-commenting-practices-multiple-sources Replication package for the paper "What do Developers Discuss about Code Comments?" ## Structure ``` Appendix.pdf Tags-topics.md Stack-exchange-query.md RQ1/ LDA_input/ combined-so-quora-mallet-metadata.csv topic-input.mallet LDA_output/ Mallet/ output_csv/ docs-in-topics.csv topic-words.csv topics-in-docs.csv topics-metadata.csv output_html/ all_topics.html Docs/ Topics/ RQ2/ datasource_rawdata/ quora.csv stackoverflow.csv manual_analysis_output/ stackoverflow_quora_taxonomy.xlsx ``` ## Contents of the Replication Package --- - **Appendix.pdf**- Appendix of the paper containing supplement tables - **Tags-topics.md** tags selected from Stack overflow and topics selected from Quora for the study (RQ1 & RQ2) - **Stack-exchange-query.md** the query interface used to extract the posts from stack exchnage explorer. - **RQ1/** - contains the data used to answer RQ1 - **LDA_input/** - input data used for LDA analysis - `combined-so-quora-mallet-metadata.csv` - Stack overflow and Quora questions used to perform LDA analysis - `topic-input.mallet` - input file to the mallet tool - **LDA_output/** - **Mallet/** - contains the LDA output generated by MALLET tool - **output_csv/** - `docs-in-topics.csv` - documents per topic - `topic-words.csv` - most relevant topic words - `topics-in-docs.csv` - topic probability per document - `topics-metadata.csv` - metadata per document and topic probability - **output_html/** - Browsable results of mallet output - `all_topics.html` - `Docs/` - `Topics/` - **RQ2/** - contains the data used to answer RQ2 - **datasource_rawdata/** - contains the raw data for each source - `quora.csv` - contains the processed dataset (like removing html tags). To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool. - `stackoverflow.csv` - contains the processed stackoverflow dataset. To know more about the preprocessing steps, please refer to the reproducibility section in the paper. The data is preprocessed using Makar tool. - **manual_analysis_output/** - `stackoverflow_quora_taxonomy.xlsx` - contains the classified dataset of stackoverflow and quora and description of taxonomy. - `Taxonomy` - contains the description of the first dimension and second dimension categories. Second dimension categories are further divided into levels, separated by `|` symbol. - `stackoverflow-posts` - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories. - `quota-posts` - the questions are labelled relevant or irrelevant and categorized into the first dimension and second dimension categories. --- </code></pre> <p> </p>
Replication package for Workshop on Software Engineering 22' - What does the pytest plugins data say?
<p>This database stores the information used to run the experiment in the article: <strong>What does the pytest plugins data say?</strong></p>
Replication Package: Model-Driven Engineering for the Interoperability of Simulation Modeling Languages: a Case Study in the Space Industry
<p>Replication package "Architectural Support for Software Performance in Continuous Software Engineering: a Systematic Mapping Study".</p>
Replication package for: The Lost Capital Asset Pricing Model
<p>The package contains the codes and the data analysis files necessary to reproduce the figures and tables in Andrei, Cujean, and Wilson (forthcoming), "The Lost Capital Asset Pricing Model," Review of Economic Studies. Detailed instructions are also given about accessing the raw data.</p>
Replication Package for: Streaming vs. Functions: A Cost Perspective on Cloud Event Processing
<p>In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.</p>
Replication package for Shishkin ® Ortoleva "Ambiguous Information and Dilation: An Experiment" (Journal of Economic Theory)
<p>Replication package for Shishkin ® Ortoleva "Ambiguous Information and Dilation: An Experiment" (Journal of Economic Theory).</p> <p>It contains raw experimental data and code producing tables and figures from the paper.</p>
Replication package for The Use of Domain-Specific Languages for Visual Analytics: A Systematic Literature Review
<p>In order to provide reproducibility, we have made all the data collected in the study titled: "The Use of Domain-Specific Languages for Visual Analytics: A Systematic Literature Review" as a replication package. This package includes the following files:</p> <ol> <li>A zip file containing the codes used for this Systematic Literature Review from NVIVO software. One separate file for each of the codes in the Zip file. (Code Summary.zip)</li> <li>Data collection form for different rounds of study. (Data_Collection_Form_Final.xlsx)</li> <li>Summary of number of papers retrieved in each round. (Number_of_Retrieved_studies.pdf)</li> </ol>
Replication package for the study "Digital Sufficiency in Flexible Work"
<p>This dataset is published for transparency and open data purposes, as part of the work "<em>'We are always on, is that really necessary?' Exploring the Path to Digital Sufficiency in Flexible Work</em>" published at <a href="https://conf.researchr.org/home/ict4s-2023">ICT4S 2023</a> conference.</p> <p><strong>The study:</strong></p> <p>We conducted three focus groups with a total of 11 participants, inside two different companies. Our aim was to investigate the notion of digital sufficiency in the context of flexible work.</p> <p><strong>Content:</strong></p> <ul> <li><em>Preliminary interview guide.pdf</em>: the interview guide containing the questions for the preliminary interview</li> <li><em>Focus group slides.pdf</em>: the slides presented to the participants of the three focus groups</li> <li><em>Focus group participant print-outs.pdf</em>: the sheets distributed to the participants of the three focus groups, for individual note-taking</li> <li><em>Codebook.xlsx</em>: the codes extracted (with the help of the software <a href="http://www.saturateapp.com">Saturate</a>) from the transcripts of the three focus groups: <ul> <li>tab <em>CODEBOOK</em>: the codes, sorted by three levels and by research question, along with their definition and a description on when the code is applicable,</li> <li>tab <em>Code Count</em>: the number of occurrence of each code in the transcripts,</li> <li>tab <em>Clustering Tactics</em>: the thematic analysis performed on the codes with level 1 "tactic". Corresponds to the Table III in the paper</li> </ul> </li> </ul> <p>For privacy reason, all names are removed and the transcript and audio recording are not part of this replication package.</p>
Replication Package for: Mapping Firms' Locations in Technological Space: A Topological Analysis of Patent Statistics
<p>This replication package contains the data and the code to generate the paper’s main results, as well as the Online Appendix, for “Mapping Firms’ Locations in Technological Space: A Topological Analysis of Patent Statistics” by Emerson G. Escolar, Yasuaki Hiraoka, Mitsuru Igami, and Yasin Ozcan (published in <em>Research Policy</em>, volume 52, issue 8, October 2023; full text available online at https://doi.org/10.1016/j.respol.2023.104821).</p>
Empirical Study on Test Generation Using GitHub Copilot --- Replication Package
<p>This replication package contains the data and scripts used in the "Empirical Study on Test Generation Using GitHub Copilot" thesis. </p>
Replication Package: An Expert Survey on the Use of Informal Models in the Automotive Industry
<p>This repository contains the replication package for the paper <em>An Expert Survey on the Use of Informal Models in the Automotive Industry</em> by <a href="https://orcid.org/0000-0001-6410-6769">Dominik Fuchß</a>, <a href="https://orcid.org/0000-0001-7312-2891">Thomas Kühn</a>, <a href="https://orcid.org/0000-0002-8953-1064">Jérôme Pfeiffer</a>, <a href="https://orcid.org/0000-0003-3534-253X">Andreas Wortmann</a>, and <a href="https://orcid.org/0000-0002-1593-3394">Anne Koziolek</a>. The paper has been accepted at the <a href="https://www.iese.fraunhofer.de/en/twinarch.html">TwinArch 2023: The 2nd International Workshop on Digital Twin Architecture</a> co-located with <a href="https://conf.researchr.org/home/ecsa-2023">ECSA 2023</a>.</p>
Replication Package for "A Catch-22--the Test-Retest Method of Reliability Estimation"
<p>Replication package for the paper "A Catch-22--the Test-Retest Method of Reliability Estimation".</p><p>Files included in the replication package and purpose of each file:</p><p>[1]Datafiles containing variables used in the analysis: question content, stability and reliability estimates (in SPSS and Stata format):<br>01_GSS_gammaV26_April2022_extract.sav<br>01_GSS_gammaV26_April2022_extract.dta<br>01_GSS_gammaV26_April2022_long_extract.dta</p><p>[2]SPSS Syntax file for replicating Tables 2,3 and Appendix Table: <br>02_SPSS_syntax_Catch22.sps</p><p>[3]Stata .do file containing the code for the regression models (Table 4):<br>03_Stata_code_Catch22.do</p><p>[4]List of GSS variables, wordings, and responses for variables included in the analysis (Excel file)<br>04_GSS variable wordings.xlsx </p>
Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
<h2>Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</h2><p>This is our replication package for our study on <i>Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</i>.</p><p>All scalability experiments are performed with the scalability benchmarking framework <a href="https://www.theodolite.rocks/">Theodolite</a> at <a href="https://www.se.informatik.uni-kiel.de/en/research/software-performance-engineering-lab-spel">Kiel University's Software Performance Engineering Lab (SPEL)</a> or Google Cloud.</p><p>With this replication package, we provide:</p><ul><li><a href="https://www.theodolite.rocks/concepts/benchmarks-and-executions.html">Benchmark execution files</a> in <i>executions</i>,</li><li>our benchmark (raw) results in <i>results</i>, and</li><li>analysis script for our results in <i>analysis</i>.</li></ul><h3>Repeating Benchmark Executions</h3><p>All our Theodolite executions are tailored to either the SPEL cluster or the Google Cloud.</p><h4>Kiel University's Software Performance Engineering Lab (SPEL)</h4><p>The SPEL cluster consists of 5 nodes, named <i>kube1-1</i> to <i>kube1-5</i> and labeled with <i>env=dev</i>. To run them in your local cluster, make sure to provide the same infrastructure or rename node selectors in the execution files accordingly.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml</p></blockquote><p>or for the vertical scalability experiment:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml -f se-cluster-dev-vertical.yaml</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h4>Google Cloud</h4><p>In the public cloud baseline experiments, the cluster consists of 5 e2-standard-32 nodes.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-dev.yaml</p></blockquote><p>For the experiments testing higher load intensities, the cluster consists of 4 e2-standard-16 nodes labeled with <i>type=infra</i> and 4 or 8 e2-standard-16 nodes with label <i>type=sut</i>. To install Theodolite in this cluster, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-stress.yaml</p></blockquote><p>In both cases, change the maximum load generated per load generator instance:</p><blockquote><p># Generate max. 100000 rec/sec per load generator instance export MAX_RECORDS_PER_INSTANCE=100000 kubectl patch benchmarks uc1-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]"</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h3>Repeating Results Analysis</h3><p>To inspect, repeat, or extend our results analysis, see <i>results</i> or run the corresponding notebooks in <i>analysis</i>.</p><p>For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.7 or 3.8 is required (e.g., in a virtual environment). Moreover, we require some Python libraries, which can be installed by:</p><blockquote><p>python3.8 -m venv .venv # source .venv/bin/activate pip install -r analysis/requirements.txt</p></blockquote><p> </p>
Replication Package of Pandemic Pedagogy: Evaluating Remote Education Strategies during COVID-19
<p>The COVID-19 pandemic precipitated an abrupt shift in the educational landscape, compelling universities to transition from in-person to online instruction. This sudden shift left many university instructors grappling with the intricacies of remote teaching. Now, with the pandemic behind us, we present a retrospective study aimed at understanding and evaluating the remote teaching practices employed during that period. Drawing from a cross-sectional analysis of 300 computer science students who underwent a full year of online education during the lockdown, our findings indicate that while remote teaching practices moderately influenced students' learning outcomes, they had a pronounced positive impact on student satisfaction. Remarkably, these outcomes were consistent across various demographics, including country, gender, and educational level. As we reflect on the lessons from this global event, this research offers evidence-based recommendations that could inform educational strategies in unwelcoming future scenarios of a similar nature, ensuring both student satisfaction and effective learning outcomes in online settings.</p>
Replication Package for "Improving the Readability of Generated Tests Using GPT-4 and ChatGPT Code Interpreter"
<p>While automated test generation can decrease the human burden associated with testing, it does not eliminate this burden. Humans must still work with generated test cases to interpret testing results, debug the code, build and maintain a comprehensive test suite, and many other tasks. Therefore, a major challenge with automated test generation is understandability of generated test test cases. </p> <p>Large language models (LLMs), machine learning models trained on massive corpora of textual data - including both natural language and programming languages - are an emerging technology with great potential for performing language-related predictive tasks such as translation, summarization, and decision support. </p> <p>In this study, we are exploring the capabilities of LLMs with regard to improving test case understandability.</p> <p>This package contains the data produced during this exploration:</p> <ul> <li>The examples directory contains the three case studies we tested our transformation process on: <ul> <li>queue_example: Tests of a basic queue data structure</li> <li>httpie_sessions: Tests of the sessions module from the httpie project. </li> <li>string_utils_validation: Tests of the validation module from the python-string-utils project.</li> <li>Each directory contains the modules-under-test, the original test cases generated by Pynguin, and the transformed test cases. </li> <li>Two trials were performed per case example of the transformation technique to assess the impact of different results from the LLM.</li> </ul> </li> <li>The survey directory contains the survey that was sent to assess the impact of the transformation on test readability. <ul> <li>survey.pdf contains the survey questions.</li> <li>responses.xlsx contains the survey results.</li> </ul> </li> </ul>
Replication package for the paper: "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions"
<p>This is the replication package for the article "Machine Learning for the Identification and Classification of Technical Debt Types on StackOverflow Discussions". The article was published in the Research Track of the third Brazilian Workshop on Intelligent Software Engineering (ISE'23).</p> <p>The replication package consists of 8 files:<br> 1) dataset.csv, 2) code_anayses.ipynb and 3) example_test_balanced.csv and the others are results of word cloud generation.</p> <p>In dataset.csv, we provide the data for future replications.</p> <p>In code_anayses.ipynb, we provide the code we use to arrive at the results.</p> <p>In example_test_balanced.csv, we provide an example input dataset for training the models.</p> <p>For future references in this article, please contact lead author Eliakim Gama, or one of the co-authors.</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.