The State of Disappearing Frameworks in 2023
- URL: http://arxiv.org/abs/2309.04188v1
- Date: Fri, 8 Sep 2023 08:02:37 GMT
- Title: The State of Disappearing Frameworks in 2023
- Authors: Juho Veps\"al\"ainen, Arto Hellas, Petri Vuorimaa
- Abstract summary: We look at the options available in the ecosystem in mid-2023 and characterize them in terms of functionality and features.
We found that the frameworks rely heavily on compilers, often support progressive enhancement, and most of the time support static output.
- Score: 2.671856791295011
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Disappearing frameworks represent a new type of thinking for web development.
In the current mainstream JavaScript frameworks, the focus has been on
developer experience at the cost of user experience. Disappearing frameworks
shift the focus by aiming to deliver as little, even zero, JavaScript to the
client. In this paper, we look at the options available in the ecosystem in
mid-2023 and characterize them in terms of functionality and features to
provide a state-of-the-art view of the trend. We found that the frameworks rely
heavily on compilers, often support progressive enhancement, and most of the
time support static output. While solutions like Astro are UI library agnostic,
others, such as Marko, are more opinionated.
Related papers
- SWE-bench Multimodal: Do AI Systems Generalize to Visual Software Domains? [64.34184587727334]
We propose SWE-bench Multimodal to evaluate systems on their ability to fix bugs in visual, user-facing JavaScript software.
SWE-bench M features 617 task instances collected from 17 JavaScript libraries used for web interface design, diagramming, data visualization, syntax highlighting, and interactive mapping.
Our analysis finds that top-performing SWE-bench systems struggle with SWE-bench M, revealing limitations in visual problem-solving and cross-language generalization.
arXiv Detail & Related papers (2024-10-04T18:48:58Z) - Toward the Automated Localization of Buggy Mobile App UIs from Bug Descriptions [19.304569170230316]
The identification of buggy UI screens and UI components is important to localizing the buggy behavior and fixing it.
This paper is the first to investigate the feasibility of automating the task of Buggy UI localization.
We find that incorporating localized buggy UIs leads to improvements of 9%-12% in Hits@10.
arXiv Detail & Related papers (2024-08-07T20:26:20Z) - OpenHands: An Open Platform for AI Software Developers as Generalist Agents [109.8507367518992]
We introduce OpenHands, a platform for the development of AI agents that interact with the world in similar ways to a human developer.
We describe how the platform allows for the implementation of new agents, safe interaction with sandboxed environments for code execution, and incorporation of evaluation benchmarks.
arXiv Detail & Related papers (2024-07-23T17:50:43Z) - Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track [51.25144287084172]
It is crucial to have an arena to build, test, visualize, and systematically evaluate RAG-based search systems.
We propose the TREC 2024 RAG Track to foster innovation in evaluating RAG systems.
arXiv Detail & Related papers (2024-06-24T17:37:52Z) - Skeet: Towards a Lightweight Serverless Framework Supporting Modern AI-Driven App Development [0.0]
Skeet was recently released to general use, alongside an initial evaluation.
Skeet provides an app structure that reflects current trends in architecture, and tool suites that allow developers with minimal knowledge of AI internals to easily incorporate such technologies into their apps and deploy them.
arXiv Detail & Related papers (2024-05-10T01:00:20Z) - Disappearing frameworks explained [0.0]
Disappearing frameworks show their meaning as an emerging topic within the space of web application development.
The purpose of this short book is to give a quick introduction to disappearing frameworks and show their meaning as an emerging topic within the space of web application development.
arXiv Detail & Related papers (2023-05-29T07:21:38Z) - Can Transformer Models Effectively Detect Software Aspects in
StackOverflow Discussion? [0.0]
Developers are constantly searching for all of the benefits and drawbacks of each API, framework, tool, and so on.
One of the typical approaches is to examine all of the features through official documentation and discussion.
In this paper, we have used a benchmark API aspects dataset (Opiner) collected from StackOverflow posts.
arXiv Detail & Related papers (2022-09-24T18:28:14Z) - A Multimodal Framework for Video Ads Understanding [64.70769354696019]
We develop a multimodal system to improve the ability of structured analysis of advertising video content.
Our solution achieved a score of 0.2470 measured in consideration of localization and prediction accuracy, ranking fourth in the 2021 TAAC final leaderboard.
arXiv Detail & Related papers (2021-08-29T16:06:00Z) - Omniscient Video Super-Resolution [84.46939510200461]
In this paper, we propose an omniscient framework to not only utilize the preceding SR output, but also leverage the SR outputs from the present and future.
Our method is superior to the state-of-the-art methods in objective metrics, subjective visual effects and complexity.
arXiv Detail & Related papers (2021-03-29T15:09:53Z) - JS-son -- A Lean, Extensible JavaScript Agent Programming Library [1.8275108630751837]
We provide a lean JavaScript library prototype for implementing reasoning-loop agents.
The library focuses on core agent programming concepts and refrains from imposing further restrictions on the programming approach.
We show how the library can be applied to multi-agent systems simulations on the web, deployed to cloud-hosted function-as-a-service environments, and embedded in Python-based data science tools.
arXiv Detail & Related papers (2020-03-10T13:27:59Z) - An Exploration of Embodied Visual Exploration [97.21890864063872]
Embodied computer vision considers perception for robots in novel, unstructured environments.
We present a taxonomy for existing visual exploration algorithms and create a standard framework for benchmarking them.
We then perform a thorough empirical study of the four state-of-the-art paradigms using the proposed framework.
arXiv Detail & Related papers (2020-01-07T17:40:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.