axay/javascript-dataset-js dataset hosted on Hugging Face and contributed by the HF Datasets community

This dataset contains an anonymized list of surveyed developers who provided their expertise level on three popular JavaScript libraries:

ReactJS, a library for building enriched web interfaces

MongoDB, a driver for accessing MongoDB databased

Socket.IO, a library for realtime communication

This dataset consists of multiple files which contain bug prediction training data.

The entries in the dataset are JavaScript functions either being buggy or non-buggy. Bug related information was obtained from the project EsLint contained in BugsJS (https://github.com/BugsJS/eslint). The buggy instances were collected throughout the lifetime of the project, however we added non-buggy entries from the latest version which is tagged as fix (entries which were previously included as buggy were not included as non-buggy later on).

The dataset is based on hybrid call graphs which are constructed by https://github.com/sed-szeged/hcg-js-framework. The result of this tool is a call graph where the edges are associated with a confidence level which shows how likely the given edge is a valid call edge.

We used different threshold values from which we considered the edges to be valid. The following threshold values were used:

0.00

0.05

0.20

0.30

The prefix in the dataset file names are coming from the used threshold. The the datasets include coupling metrics NII (Nubmer of Incoming Invocations) and NOI (Number of Outgoing Invocations) which were calculated by a static source code analyzer called SourceMeter. Hybrid counterparts of these metrics (HNII and HNOI) are based on the given threshold values.

There are four variants for all of these datasets:

Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics and information about the entries (file without any postfix). Column contained only in this dataset are:

ID

Name

Longname

Parent ID

Component ID

Path

Line

Column

EndLine

EndColumn

Both static (NII, NOi) and hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h+s' postfix)

Only static (NII, NOI) coupling metrics are included with additional static source code metrics (file with '_s' postfix)

Only hybrid (HNII, HNOI) coupling metrics are included with additional static source code metrics (file with '_h' postfix)

Static source code metrics which are contained in all dataset are the following:

McCC - McCabe Cyclomatic Complexity

NL - Nesting Level

NLE - Nesting Level Else If

CD - Comment Density

CLOC - Comment Lines of Code

DLOC - Documentation Lines of Code

TCD - Total Comment Density (Comment Lines in an emedded function will be also considered)

TCLOC - Total Comment Lines of Code (Comment Lines in an emedded function will be also considered)

LLOC - Logical Lines of Code (Comment and empty lines not counted)

LOC - Lines of Code (Comment and empty lines are counted)

NOS - Number of Statements

NUMPAR - Number of Parameters

TLLOC - Logical Lines of Code (Lines in embedded functions are also counted)

TLOC - Lines of Code (Lines in embedded functions are also counted)

TNOS - Total Number of Statements (Statements in embedded functions are also counted)

Dataset Card for dataset-JavaScript-general-coding

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/dmeldrum6/dataset-JavaScript-general-coding/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/dmeldrum6/dataset-JavaScript-general-coding.

Dataset Card for "instructional_code-search-net-javacript"

  Dataset Summary

This is an instructional dataset for JavaScript. The dataset contains two different kind of tasks:

Given a piece of code generate a description of what it does. Given a description generate a piece of code that fulfils the description.

  Languages

The dataset is in English.

  Data Splits

There are no splits.

  Dataset Creation

May of 2023

  Curation Rationale… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/instructional_code-search-net-javacript.

angie-chen55/javascript-github-code dataset hosted on Hugging Face and contributed by the HF Datasets community

JavaScript dataset.

Dataset Overview (React Q&A)

Our chatbot will be trained on a specialized Q&A dataset about the React JavaScript library. This React Q&A dataset is provided as a JSON file containing roughly 26,300 question-answer pairs (the exact number may vary slightly). Each entry in the JSON list has a "question" field and a corresponding "answer" field, e.g.:

{"question": "What is React?", "answer": "React is an open-source JavaScript library for building user interfaces..."}

This format (a list of objects with ‘question’ and ‘answer’ strings) is common in QA collections. For comparison, a well-known QA dataset like SQuAD (Stanford Question Answering Dataset) contains on the order of 100,000 question-answer pairs. Our React dataset is smaller but still substantial. It covers many topics relevant to React: definitions (e.g. “What is JSX?”), how-to guides (e.g. “How to install react-datepicker?”), component usage, common patterns, troubleshooting, and performance features.

Because this dataset is domain-specific (about React), it serves as a tailored knowledge base for the chatbot. Using a focused corpus like this is recommended: as noted by experts, “if your QA system focuses on a particular domain (e.g., technical), consider domain-specific corpora” and even curate your own Q&A pairs. This helps the model learn React terminology and concepts deeply. The dataset’s JSON structure (a flat list of QA entries) is simple and ready for loading into typical training pipelines.

Dataset

This dataset was created by sSchumat

Contents

Dataset Card for "code-search-net-javascript"

  Dataset Summary

This dataset is the JavaScript portion of the CodeSarchNet annotated with a summary column.The code-search-net dataset includes open source functions that include comments found at GitHub.The summary is a short description of what the function does.

  Languages

The dataset's comments are in English and the functions are coded in JavaScript

  Data Splits

Train, test, validation labels are… See the full description on the dataset page: https://huggingface.co/datasets/Nan-Do/code-search-net-javascript.

This dataset is about books. It has 1 row and is filtered where the book is Reliable JavaScript. It features 7 columns including author, publication date, language, and book publisher.

This dataset is about books. It has 1 row and is filtered where the book is Eloquent JavaScript : a modern introduction to programming. It features 7 columns including author, publication date, language, and book publisher.

This dataset is about book subjects. It has 2 rows and is filtered where the books is Learning JavaScript. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Dataset is imported from CodeXGLUE and pre-processed using their script.

  Where to find in Semeru:

The dataset can be found at /nfs/semeru/semeru_datasets/code_xglue/code-to-text/javascript in Semeru

  CodeXGLUE -- Code-To-Text





  Task Definition

The task is to generate natural language comments for a code, and evaluted by smoothed bleu-4 score.

  Dataset

The dataset we use comes from CodeSearchNet and we filter the dataset as the following:… See the full description on the dataset page: https://huggingface.co/datasets/semeru/code-text-javascript.

saurabh5/rlvr-code-data-JavaScript dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset contains extracted attributes from websites that can be used for Classification of webpages as malicious or benign. The dataset also includes raw page content including JavaScript code that can be used as unstructured data in Deep Learning or for extracting further attributes. The data has been collected by crawling the Internet using MalCrawler [1]. The labels have been verified using the Google Safe Browsing API [2]. Attributes have been selected based on their relevance [3]. The details of dataset attributes is as given below: 'url' - The URL of the webpage. 'ip_add' - IP Address of the webpage. 'geo_loc' - The geographic location where the webpage is hosted. 'url_len' - The length of URL. 'js_len' - Length of JavaScript code on the webpage. 'js_obf_len - Length of obfuscated JavaScript code. 'tld' - The Top Level Domain of the webpage. 'who_is' - Whether the WHO IS domain information is compete or not. 'https' - Whether the site uses https or http. 'content' - The raw webpage content including JavaScript code. 'label' - The class label for benign or malicious webpage.

Python code for extraction of the above listed dataset attributes is attached. The Visualisation of this dataset and it python code is also attached. This visualisation can be seen online on Kaggle [5].

Dataset

This dataset was created by Jordan Tantuico

Contents

This dataset is about book subjects. It has 5 rows and is filtered where the books is Sams teach yourself jQuery and JavaScript in 24 hours. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset is about book subjects. It has 8 rows and is filtered where the books is React Native in action : developing iOS and Android apps with JavaScript. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

This dataset consists of 1 .xlsx file, 2 .png files, 1 .json file and 1 .zip file:annotation_details.xlsx: The distribution of annotations in the previously mentioned six classes (mitosis, apoptosis, tumor nuclei, non-tumor nuclei, tubule, and non-tubule) is presented in a Excel spreadsheet.original.png: The input image.annotated.png: An example from the dataset. In the annotated image, blue circles indicate the tumor nuclei, pink circles show non-tumor nuclei such as blood cells, stroma nuclei, and lymphocytes; orange and green circles are mitosis and apoptosis, respectively; light blue circles are true lumen for tubules, and yellow circles represent white regions (non-lumen) such as fat, blood vessel, and broken tissues.data.json: The annotations for the BreCaHAD dataset are provided in JSON (JavaScript Object Notation) format. In the given example, the JSON file (ground truth) contains two mitosis and only one tumor nuclei annotations. Here, x and y are the coordinates of the centroid of the annotated object, and the values are between 0, 1.BreCaHAD.zip: An archive file containing dataset. Three folders are included: images (original images), groundTruth (json files), and groundTruth_display (groundTruth applied on original images)