惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

C
Check Point Blog
美团技术团队
Jina AI
Jina AI
人人都是产品经理
人人都是产品经理
The Cloudflare Blog
V
Visual Studio Blog
Google DeepMind News
Google DeepMind News
Hugging Face - Blog
Hugging Face - Blog
云风的 BLOG
云风的 BLOG
有赞技术团队
有赞技术团队
T
The Blog of Author Tim Ferriss
WordPress大学
WordPress大学
月光博客
月光博客
宝玉的分享
宝玉的分享
小众软件
小众软件
MongoDB | Blog
MongoDB | Blog
Apple Machine Learning Research
Apple Machine Learning Research
A
About on SuperTechFans
J
Java Code Geeks
博客园_首页
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
N
Netflix TechBlog - Medium
Vercel News
Vercel News
博客园 - 聂微东

cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Exploring Micro Frontends: A Case Study Application in E-...
2025-06-26 · via cs.SE updates on arXiv.org

In the micro frontends architectural style, the frontend is divided into smaller components, which can range from a simple button to an entire page. The goal is to improve scalability, resilience, and team independence, albeit at the cost of increased complexity and infrastructure demands. This paper seeks to understand when it is worth adopting micro frontends, particularly in the context of industry. To achieve this, we conducted an investigation into the state of the art of micro frontends, based on both academic and gray literature. We then implemented this architectural style in a marketplace for handcrafted products, which already used microservices. Finally, we evaluated the implementation through a semi-open questionnaire with the developers. At the studied marketplace company, the need for architectural change arose due to the tight coupling between their main system (a Java monolith) and a dedicated frontend system. Additionally, there were deprecated technologies and poor developer experience. To address these issues, the micro frontends architecture was adopted, along with the API Gateway and Backend for Frontend patterns, and technologies such as Svelte and Fastify. Although the adoption of Micro Frontends was successful, it was not strictly necessary to meet the company's needs. According to the analysis of the mixed questionnaire responses, other alternatives, such as a monolithic frontend, could have achieved comparable results. What made adopting micro frontends the most convenient choice in the company's context was the monolith strangulation and microservices adoption, which facilitated implementation through infrastructure reuse and knowledge sharing between teams.