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

F
Fortinet All Blogs
Last Week in AI
Last Week in AI
IT之家
IT之家
A
About on SuperTechFans
M
MIT News - Artificial intelligence
Y
Y Combinator Blog
T
The Blog of Author Tim Ferriss
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
博客园 - 三生石上(FineUI控件)
博客园 - 【当耐特】
V
Visual Studio Blog
Microsoft Security Blog
Microsoft Security Blog
博客园_首页
aimingoo的专栏
aimingoo的专栏
The Cloudflare Blog
Vercel News
Vercel News
博客园 - Franky
有赞技术团队
有赞技术团队
B
Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
GbyAI
GbyAI
量子位
云风的 BLOG
云风的 BLOG
T
Tailwind CSS 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
Towards a Transpiler for C/C++ to Safer Rust
Dhiren Tripuramallu, Swapnil Singh, Shrirang Deshmukh, Srinivas · 2024-01-16 · via cs.SE updates on arXiv.org

Rust is a multi-paradigm programming language developed by Mozilla that focuses on performance and safety. Rust code is arguably known best for its speed and memory safety, a property essential while developing embedded systems. Thus, it becomes one of the alternatives when developing operating systems for embedded devices. How to convert an existing C++ code base to Rust is also gaining greater attention. In this work, we focus on the process of transpiling C++ code to a Rust codebase in a robust and safe manner. The manual transpilation process is carried out to understand the different constructs of the Rust language and how they correspond to C++ constructs. Based on the learning from the manual transpilation, a transpilation table is created to aid in future transpilation efforts and to develop an automated transpiler. We also studied the existing automated transpilers and identified the problems and inefficiencies they involved. The results of the transpilation process were closely monitored and evaluated, showing improved memory safety without compromising performance and reliability of the resulting codebase. The study concludes with a comprehensive analysis of the findings, an evaluation of the implications for future research, and recommendations for the same in this area.