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

雷峰网
雷峰网
L
LangChain Blog
GbyAI
GbyAI
F
Fortinet All Blogs
腾讯CDC
Last Week in AI
Last Week in AI
A
About on SuperTechFans
J
Java Code Geeks
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - Franky
B
Blog
D
Docker
G
Google Developers Blog
月光博客
月光博客
博客园 - 三生石上(FineUI控件)
S
SegmentFault 最新的问题
Apple Machine Learning Research
Apple Machine Learning Research
酷 壳 – CoolShell
酷 壳 – CoolShell
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
T
Tailwind CSS Blog
宝玉的分享
宝玉的分享
U
Unit 42
Blog — PlanetScale
Blog — PlanetScale
B
Blog RSS Feed

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
Static Deadlock Detection in Low-Level C Code
Dominik Harmim, Vladimír Marcin, Lucie Svobodová, Tomáš Vojnar · 2022-07-05 · via cs.SE updates on arXiv.org

We present a novel scalable deadlock analyser L2D2 capable of handling C code with low-level unstructured lock manipulation. L2D2 runs along the call tree of a program, starting from its leaves, and analyses each function just once, without any knowledge of the call context. L2D2 builds function summaries recording information about locks that are assumed or known to be locked or unlocked at the entry, inside, and at the exit of functions, together with lock dependencies, and reports warnings about possible deadlocks when cycles in the lock dependencies are detected. We implemented L2D2 as a plugin of the Facebook/Meta Infer framework and report results of experiments on a large body of C as well as C++ code illustrating the effectiveness and efficiency of L2D2.