This package contains the material used in the manuscript.

For more information on how to understand the package structure, please read the README.md.

Developers often share their code snippets by packaging them and making them available to others through software packages. How much a package does and how big it is can be seen as positive or negative. Recent studies showed that many packages that exist in the npm ecosystem are trivial and may introduce high dependency overhead.

Hence, one question that arises is why developers choose to publish these trivial packages. Therefore, in this paper, we perform a developer-centered study to empirically examine why developers choose to publish such trivial packages. Specifically, we ask 1) why developers publish trivial packages, 2) what they believe to be the possible negative impacts of these packages, and 3) how such negative issues can be mitigated. The survey response of 59 JavaScript developers who publish trivial npm packages showed that the main reasons for publishing these trivial packages are to provide reusable components, testing & documentation, and separation of concerns. Even the developers who publish these trivial packages admitted to having issues when they publish such packages, which include the maintenance of multiple packages, dependency hell, finding the right package, and the increase of duplicated packages in the ecosystems. Furthermore, we found that the majority of the developers suggested grouping these trivial packages to cope with the problems associated with publishing them. Then, to quantitatively investigate the impact of these trivial packages on the npm ecosystem and its users, we examine grouping these trivial packages. We found that if trivial packages that are always used together are grouped, the ecosystem can reduce the number of dependencies by approximately 13%. Our findings shed light on the impact of publishing trivial packages and show that ecosystems and developer communities need to rethink their publishing policies since it can negatively impact the developers and the entire ecosystem.

The published data set contains the following:

1. List of identified trivial npm packages.
2. The survey questions.
3. The developers' responses to the survey.
4. The results of the co-usage analysis of trivial npm packages.

Discover the booming JavaScript Development Services market! Explore key trends, regional growth, and leading companies in this $7700.7 million (2025) industry. Learn about market forecasts to 2033 and the impact of React, Angular, and Node.js.

The JavaScript Web Frameworks Software market is experiencing robust growth, driven by the increasing demand for dynamic and interactive web applications across various industries. The market's expansion is fueled by factors such as the rising adoption of cloud-based technologies, the proliferation of mobile devices, and the growing need for improved user experiences. Companies are increasingly leveraging JavaScript frameworks to streamline development processes, enhance application performance, and create feature-rich web solutions. This has led to a considerable increase in market size, estimated to be around $15 billion in 2025, with a projected Compound Annual Growth Rate (CAGR) of approximately 15% from 2025 to 2033. This growth is further propelled by continuous innovations within the framework ecosystem, the emergence of new frameworks catering to specific needs, and a growing pool of skilled developers. The competitive landscape is dynamic, with established players like Google (Angular), Sencha Ext JS, and React (backed by Meta) competing alongside emerging frameworks. The market is segmented based on framework type (e.g., component-based, full-stack), licensing models, deployment methods, and industry verticals. North America currently holds a significant market share, owing to the presence of major technology companies and a high adoption rate of advanced technologies. However, other regions like Europe and Asia-Pacific are witnessing substantial growth driven by digital transformation initiatives and increasing internet penetration. While challenges remain, such as the need for continuous updates and the learning curve associated with new frameworks, the overall market outlook remains positive, indicating sustained growth in the coming years.

All versions of Node.js 8.x, 9.x, and 10.x are vulnerable and the severity is HIGH. An attacker can cause a denial of service (DoS) by causing a node server providing an http2 server to crash. This can be accomplished by interacting with the http2 server in a manner that triggers a cleanup bug where objects are used in native code after they are no longer available. This has been addressed by updating the http2 implementation.

The global JavaScript Development Service market is poised for significant expansion, projected to reach an estimated market size of USD 5,367 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 6.1% anticipated throughout the forecast period of 2025-2033. This sustained growth is primarily fueled by the pervasive adoption of JavaScript across diverse applications, ranging from dynamic web interfaces and single-page applications (SPAs) to mobile applications and server-side development. The increasing demand for responsive and interactive user experiences, coupled with the rise of modern JavaScript frameworks and libraries like React, Angular, and Vue.js, are powerful drivers. Furthermore, the growing complexity of web applications and the continuous evolution of web technologies necessitate specialized JavaScript development services, contributing to market momentum. The market caters to a broad spectrum of clients, encompassing individual developers seeking specialized expertise and large enterprises requiring comprehensive web development solutions. The market landscape for JavaScript Development Services is characterized by a dynamic interplay of trends and opportunities. The escalating demand for cross-platform compatibility and the proliferation of hybrid mobile app development using frameworks like React Native and Ionic are creating new avenues for growth. Additionally, the increasing integration of JavaScript in emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence (AI) is opening up novel application areas. While the market exhibits strong growth, certain factors could influence its trajectory. The evolving landscape of JavaScript frameworks and the need for continuous skill upgrades among developers present a challenge, demanding adaptability from service providers. Furthermore, the competitive nature of the market necessitates a focus on delivering high-quality, scalable, and cost-effective solutions to maintain a competitive edge. Geographically, North America and Europe are expected to lead the market, owing to their advanced digital infrastructure and high adoption rates of web technologies, followed by the rapidly expanding Asia Pacific region. This report provides a comprehensive analysis of the global JavaScript Develop Service market, encompassing a detailed examination of its historical performance, current trends, and future projections. The study period spans from 2019 to 2033, with a base year of 2025 and a forecast period from 2025 to 2033. The historical period covers 2019-2024. The report delves into market concentration, key trends, regional dominance, product insights, and crucial driving forces, challenges, and emerging trends that will shape the industry landscape. With an estimated market value in the millions, this report offers invaluable insights for stakeholders seeking to understand and capitalize on opportunities within the JavaScript development ecosystem.

Dataset for use in Node Code Query, contains package information in a tab separated csv file. The unzipped size is ~700MB.

You do not need to manually download this file for use in NCQ, the setup scripts will handle this for you automatically.

The dataset contains the following fields:

Mined from the NPM registry:

Package name

Description

Keywords

License

repositoryUrl

timeModified

Derived from data on the NPM registry:

Array of Node.js code snippets extracted from the package README using https://github.com/Brittany-Reid/npm-code-snippets

Number of markdown code blocks in the README (number may be larger than node.js snippets, these are non-filtered)

Number of lines in the README

If an install example exists in the README (if a code block exists with npm install or a install header exists)

If a run example exists in the README (if a code block exists with npm run or a usage header exists)

Mined from GitHub for packages with a GitHub repository (values will be 0 or false for packages missing this data)

Number of stars

Is a fork?

Number of forks

Number of watchers

If a test directory exists (if the top level directory contains a folder called test or tests)

A remote attacker could possibly cause a Denial of Service condition, or conduct man-in-the-middle attacks.

The JavaScript Web Frameworks Software market is experiencing robust growth, driven by the increasing demand for dynamic and interactive web applications across various sectors. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $45 billion by 2033. This surge is fueled by several key factors. The rising adoption of cloud-based solutions offers scalability and cost-effectiveness, significantly boosting the market. Furthermore, the growing preference for responsive web design catering to diverse devices, coupled with the continuous evolution of JavaScript frameworks with enhanced features and performance optimization, contributes to this expansion. The enterprise segment dominates the market due to its need for sophisticated web applications to manage complex operations and streamline workflows. However, the individual developer segment is also experiencing substantial growth as more developers leverage these frameworks for personal projects and freelance work. Competitive intensity remains high, with established players like Google (Angular), Sencha, and Ag-Grid competing alongside emerging innovative companies like Fenopix Technologies and Bitovi. The market is segmented by application (individual, enterprise, others) and type (on-premise, cloud-based). Geographic distribution shows a concentration in North America and Europe, followed by Asia Pacific, with regions like China and India showing promising growth potential. While the market faces some restraints, such as the complexity of some frameworks and the need for continuous learning and adaptation, the overall trend points to consistent expansion driven by technological advancements, increasing digitalization, and the persistent need for robust and efficient web application development.

Abstract

The popularity and wide adoption of JavaScript both at the client and server-side makes its code analysis more essential than ever before. Most of the algorithms for vulnerability analysis, coding issue detection, or type inference rely on the call graph representation of the underlying program. Luckily, there are quite a few tools to get this job done already. However, their performance in vitro and especially in vivo has not yet been extensively compared and evaluated.

In this paper, we systematically compare five static and two dynamic approaches for building JavaScript call graphs on 26 WebKit SunSpider benchmark programs and two static and two dynamic methods on 12 real-world Node.js modules. The tools under examination using static techniques were npm call graph, IBM WALA, Google Closure Compiler, Approximate Call Graph, and Type Analyzer for JavaScript. We performed dynamic analyzes relying on the nodejs-cg tool (a customized Node.js runtime) and the NodeProf instrumentation and profiling framework.

We provide a quantitative evaluation of the results, and a result quality analysis based on 941 manually validated call edges. On the SunSpider programs, which do not take any inputs, so dynamic extraction could be complete, all the static tools also performed well. For example, TAJS found 93% of all edges while having a 97% precision compared to the precise dynamic call graph. When it comes to real-world Node.js modules, our evaluation shows that static tools struggle with parsing the code and fail to detect a significant amount of call edges that dynamic approaches can capture. Nonetheless, a significant number of edges not detected by dynamic approaches are also reported. Among these, however, there are also edges that are real, but for some reason the unit tests did not execute the branches in which these calls were included.

Node.js < 12.22.9, < 14.18.3, < 16.13.2, and < 17.3.1 did not handle multi-value Relative Distinguished Names correctly. Attackers could craft certificate subjects containing a single-value Relative Distinguished Name that would be interpreted as a multi-value Relative Distinguished Name, for example, in order to inject a Common Name that would allow bypassing the certificate subject verification.Affected versions of Node.js that do not accept multi-value Relative Distinguished Names and are thus not vulnerable to such attacks themselves. However, third-party code that uses node's ambiguous presentation of certificate subjects may be vulnerable.

A Simple JavaScript/Node.js CSV to Markdown Table Converter

The booming full-stack development services market is projected to reach significant growth by 2033. Explore market trends, regional insights, leading companies, and key drivers in this comprehensive analysis. Learn how rising demand for web & mobile apps fuels this expanding sector.

What is this?

A monorepo of tools and examples for dynamic UX generation using React.

  Prerequisites

Node.js >= 20 (strongly suggest using nvs or nvm to manage Node.js versions)

  Quick start

corepack enable to allow using Yarn 4 yarn to install package dependencies yarn typecheck to build all app dependencies and typecheck all apps If you're coding in this repo, make sure to run yarn && yarn typecheck in the repo root after a merge or branch change

cd… See the full description on the dataset page: https://huggingface.co/datasets/xia01ongLi/temp.

Run and deploy your AI Studio app

This contains everything you need to run your app locally. View your app in AI Studio: https://ai.studio/apps/drive/1QTUmCjdHYm5lNOmdXpa4cISRae5Pl-qI

  Run Locally

Prerequisites: Node.js

Install dependencies: npm install Set the GEMINI_API_KEY in .env.local to your Gemini API key Run the app: npm run dev

A function to read the file using Node.js.

A NPM package for get data of Lëtzebuerger Online Dictionnaire (LOD) from data.public.lu. Repo on Github : https://github.com/robertoentringer/lod-opendata Npm package : https://www.npmjs.com/package/lod-opendata

Web Beast Dataset

The Web Beast Dataset is a large-scale, curated code dataset focused on modern backend and fullstack development. It includes high-quality source code files from open-source repositories, targeting:

PHP (Laravel) Node.js (Express, TypeScript) JavaScript (Frontend and Backend) HTML & Tailwind CSS

  💡 Purpose

Designed for:

Training code generation & completion models Building smart developer tools Fine-tuning LLMs (e.g. DeepSeek, StarCoder, Codellama)… See the full description on the dataset page: https://huggingface.co/datasets/brijmansuriya/web-beast.

The code base for IsoplotR’s graphical user interface (GUI) and its core data processing algorithms are surgically separated from each other. The command-line functionality is grouped in a lightweight package called IsoplotR, which has minimal dependencies and works on a basic R installation. It only uses commands that have been part of the R programming language for many decades and are unlikely to change in the future. In contrast, the GUI is written in html and Javascript and interacts with IsoplotR via an interface library. This interface is currently provided by the shiny package. shiny is free, open, and popular among R developers but has two important limitations: (1) it was created and is owned by a private company, which reduces the software’s future proofness; (2) shiny is a rather ‘bloated’ piece of code that does much more than is needed for IsoplotRgui. To avoid these issues, shinylight is a light-weight alternative to shiny that allows websites to call R functions in a similar fashion to the way in which node.js allows websites to use Javascript as a server language. Shinylight has been integrated in IsoplotRgui and all future software deliverables of the ‘Beyond Isoplot’ project, including the upcoming 'simplex' program for SIMS data processing.