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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
Semantic Analysis of Macro Usage for Portability
Brent Pappas, Paul Gazzillo · 2024-01-19 · via cs.SE updates on arXiv.org

C is an unsafe language. Researchers have been developing tools to port C to safer languages such as Rust, Checked C, or Go. Existing tools, however, resort to preprocessing the source file first, then porting the resulting code, leaving barely recognizable code that loses macro abstractions. To preserve macro usage, porting tools need analyses that understand macro behavior to port to equivalent constructs. But macro semantics differ from typical functions, precluding simple syntactic transformations to port them. We introduce the first comprehensive framework for analyzing the portability of macro usage. We decompose macro behavior into 26 fine-grained properties and implement a program analysis tool, called Maki, that identifies them in real-world code with 94% accuracy. We apply Maki to 21 programs containing a total of 86,199 macro definitions. We found that real-world macros are much more portable than previously known. More than a third (37%) are easy-to-port, and Maki provides hints for porting more complicated macros. We find, on average, 2x more easy-to-port macros and up to 7x more in the best case compared to prior work. Guided by Maki's output, we found and hand-ported macros in four real-world programs. We submitted patches to Linux maintainers that transform eleven macros, nine of which have been accepted.