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

钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
The GitHub Blog
The GitHub Blog
J
Java Code Geeks
Engineering at Meta
Engineering at Meta
N
Netflix TechBlog - Medium
A
About on SuperTechFans
博客园 - 三生石上(FineUI控件)
罗磊的独立博客
MongoDB | Blog
MongoDB | Blog
B
Blog RSS Feed
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
有赞技术团队
有赞技术团队
T
Tailwind CSS Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
Vercel News
Vercel News
腾讯CDC
博客园 - 聂微东
The Cloudflare Blog
F
Fortinet All Blogs
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
Visual Studio Blog
Last Week in AI
Last Week in AI
B
Blog

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
An Exploratory Approach for Game Engine Architecture Reco...
Gabriel C. Ullmann, Yann-Gaël Guéhéneuc, Fabio Petrillo, Nicolas · 2023-03-04 · via cs.SE updates on arXiv.org

Game engines provide video game developers with a wide range of fundamental subsystems for creating games, such as 2D/3D graphics rendering, input device management, and audio playback. Developers often integrate these subsystems with other applications or extend them via plugins. To integrate or extend correctly, developers need a broad system architectural understanding. However, architectural information is not always readily available and is often overlooked in this kind of system. In this work, we propose an approach for game engine architecture recovery and explore the architecture of three popular open-source game engines (Cocos2d-x, Godot, and Urho3D). We perform manual subsystem detection and use Moose, a platform for software analysis, to generate architectural models. With these models, we answer the following questions: Which subsystems are present in game engines? Which subsystems are more often coupled with one another? Why are these subsystems coupled with each other? Results show that the platform independence, resource management, world editor, and core subsystems are frequently included by others and therefore act as foundations for the game engines. Furthermore, we show that, by applying our approach, game engine developers can understand whether subsystems are related and divide responsibilities. They can also assess whether relationships among subsystems are appropriate for the game engine.