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10 results for “Code documentation”
Everglades Landscape Model (ELM) Code and Documentation
The Everglades Landscape Model (ELM) is an application instance of the generalized Ecological Landscape Modeling code package. For applications within the Florida Coastal Everglades (FCE) LTER, the ELM is one of the simulation modeling tools used to a) explore hypotheses of ecosystem processes in heterogenous spatial landscapes, b) extrapolate field-scale research findings across space and time, and c) predict the evolution of the Everglades landscape in response to plausible future scenarios. For ELM v.2.8.3-4, the 2 zip-archive packages here contains 1a) all (C) source code and unix (Bourne) shell scripts used to build and run the ELM (& includes Doxygen-generated hyperlinked source code documentation of every file/function/struct/parameter), 1b) all Everglades-specific input data used in historical (1981-2000) simulations; and 2) a complete documentation report with chapters including the Introduction&Goals, Model Data, Model Structure, Model Performance, and Model User's Guide. The ELM is being applied and updated routinely. Such updated information can be found at http://www.ecolandmod.com .
Can Developers Prompt? A Controlled Experiment for Code Documentation Generation [Replication Package]
<h2>Artifact Summary</h2> <p>This repository contains the replication package for the paper 'Can Developers Prompt? A Controlled Experiment for Code Documentation Generation,' presented at the <em><a href="https://conf.researchr.org/home/icsme-2024" target="_blank" rel="noopener">40th IEEE International Conference on Software Maintenance and Evolution (ICSME'24)</a></em>.</p> <p>The purpose of the package is to facilitate the verification and reproduction of the study results. It provides all data of the controlled experiment, the developed <em>Visual Studio Code (VS Code)</em> extension, as well as the slides of the conference presentations.</p> <h2>Paper Abstract</h2> <p>Large language models (LLMs) bear great potential for automating tedious development tasks such as creating and maintaining code documentation. However, it is unclear to what extent developers can effectively prompt LLMs to create concise and useful documentation. We report on a controlled experiment with 20 professionals and 30 computer science students tasked with code documentation generation for two Python functions. The experimental group freely entered ad-hoc prompts in a ChatGPT-like extension of Visual Studio Code, while the control group executed a predefined few-shot prompt. Our results reveal that professionals and students were unaware of or unable to apply prompt engineering techniques. Especially students perceived the documentation produced from ad-hoc prompts as significantly less readable, less concise, and less helpful than documentation from prepared prompts. Some professionals produced higher quality documentation by just including the keyword Docstring in their ad-hoc prompts. While students desired more support in formulating prompts, professionals appreciated the flexibility of ad-hoc prompting. Participants in both groups rarely assessed the output as perfect. Instead, they understood the tools as support to iteratively refine the documentation. Further research is needed to understand which prompting skills and preferences developers have and which support they need for certain tasks.</p> <h2>References</h2> <p>The published paper is available on <a href="https://doi.org/10.1109/ICSME58944.2024.00058" target="_blank" rel="noopener">IEEE Xplore</a> and the preprint on <a href="https://doi.org/10.48550/arXiv.2408.00686" target="_blank" rel="noopener">arXiv</a>.</p>
Dataset and documented R code for "Nouns and verbs in the speech signal"
<p>The files available constitute supplementary material to the following article:</p> <p>Lohmann, Arne. Nouns and verbs in the speech signal: Are there phonetic correlates of grammatical category? <em>Linguistics</em> - <em>An Interdisciplinary Journal of the Language Sciences</em>.</p> <p>The article is to be published online in 2020, and in 2021 in the print version of the journal.</p>
An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability - Data, Code and Documentation
<p>Release version of the data, code and documentation used in and generated by our data analysis and literature search to ensure reproducibility, repeatability, and transparency, to be published alongside our paper "An Empirical Validation of Cognitive Complexity as a Measure of Source Code Understandability".</p>
Documentation and code for reproducing analyses presented in: vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework
<p>Documentation and code for reproducing analyses presented in: vcferr: Development, Validation, and Application of a SNP Genotyping Error Simulation Framework. Please see the <strong>README.pdf</strong> for step-by-step instructions for reproducing the entire analysis described in the paper.</p>
Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles (Dataset, Models & Code)
<p>Many digital libraries recommend literature to their users considering the similarity between a query document and their repository. However, they often fail to distinguish what is the relationship that makes two documents alike. In this paper, we model the problem of finding the relationship between two documents as a pairwise document classification task. To find the semantic relation between documents, we apply a series of techniques, such as GloVe, Paragraph-Vectors, BERT, and XLNet under different configurations (e.g., sequence length, vector concatenation scheme), including a Siamese architecture for the Transformer-based systems. We perform our experiments on a newly proposed dataset of 32,168 Wikipedia article pairs and Wikidata properties that define the semantic document relations. Our results show vanilla BERT as the best performing system with an F1-score of 0.93,<br> which we manually examine to better understand its applicability to other domains. Our findings suggest that classifying semantic relations between documents is a solvable task and motivates the development of recommender systems based on the evaluated techniques. The discussions in this paper serve as first steps in the exploration of documents through SPARQL-like queries such that one could find documents that are similar in one aspect but dissimilar in another.</p> <p>Additional information can be found on <a href="https://github.com/malteos/semantic-document-relations/">GitHub</a>.</p> <p>The following data is supplemental to the experiments described in our research paper. The data consists of:</p> <ul> <li>Datasets (articles, class labels, cross-validation splits)</li> <li>Pretrained models (Transformers, GloVe, Doc2vec)</li> <li>Model output (prediction) for the best performing models</li> </ul> <p><strong>Dataset</strong></p> <p>The Wikipedia article corpus is available in <code>enwiki-20191101-pages-articles.weighted.10k.jsonl.bz2</code>. The original data have been downloaded as <a href="https://dumps.wikimedia.org/enwiki/">XML dump</a>, and the corresponding articles were extracted as plain-text with <a href="https://radimrehurek.com/gensim/scripts/segment_wiki.html">gensim.scripts.segment_wiki</a>. The archive contains only articles that are available in training or test data.</p> <p>The actual dataset is provided as used in the stratified k-fold with <code>k=4</code> in <code>train_testdata__4folds.tar.gz</code>.</p> <pre><code>├── 1 │ ├── test.csv │ └── train.csv ├── 2 │ ├── test.csv │ └── train.csv ├── 3 │ ├── test.csv │ └── train.csv └── 4 ├── test.csv └── train.csv 4 directories, 8 files </code></pre> <p>Pretrained models</p> <p>PyTorch: vanilla and Siamese BERT + XLNet</p> <p>Pretrained model for each fold is available in the corresponding model archives:</p> <pre><code># Vanilla model_wiki.bert_base__joint__seq512.tar.gz model_wiki.xlnet_base__joint__seq512.tar.gz # Siamese model_wiki.bert_base__siamese__seq512__4d.tar.gz model_wiki.xlnet_base__siamese__seq512__4d.tar.gz </code></pre>
Comments On Comment: Where Code Review and Documentation Meet
<p>Comments on Comments dataset from the paper titled 'Comments on Comments: Where Code Review and Documentation Meet'</p> <p>DOI of the paper: <a href="https://doi.org/10.1145/3524842.3528475">https://doi.org/10.1145/3524842.3528475</a></p>
Cryptologue Arcade - Crypto Darkpaper - Crypto Color Prints - Simplementation Scheme : A Color-Coded Method For Indexing Research and Documentation
<p>Crypto Color Prints are a memorable and data-dense documentation paradigm with a color-coded, structural scheme. The scheme forms a simple framework for publishing and improving primitives and protocols with a focus on both cooperation and implementation.</p>
Using Machine Learning With Supplementary NC Code To Predict Machining Energy - Excel Documents
<p>The Excel Files Housed within this DOI represent the raw data collected during machining each of the test parts, and the excel documents made which prevent model summaries for each model created., during the execution of the, "Using Machine Learning With Supplementary NC Code to Predict Machining Energy. These files were created by Samuel D. Stencel, a Graduate Research Assistant and Purdue University.</p>
Coding Improvement Project: Resident Education on Documentation
ClinicalTrials.gov study NCT04538196. IPD Sharing: Not stated. Countries: 1. Publications: 6.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.