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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 Empirical Study of Impact of Solidity Compiler Updates...
Chihiro Kado, Naoto Yanai, Jason Paul Cruz, Kyosuke Yamashita, S · 2023-06-07 · via cs.SE updates on arXiv.org

Vulnerabilities of Ethereum smart contracts often cause serious financial damage. Whereas the Solidity compiler has been updated to prevent vulnerabilities, its effectiveness has not been revealed so far, to the best of our knowledge. In this paper, we shed light on the impact of compiler versions of vulnerabilities of Ethereum smart contracts. To this end, we collected 503,572 contracts with Solidity source codes in the Ethereum blockchain and then analyzed their vulnerabilities. For three vulnerabilities with high severity, i.e., Locked Money, Using tx.origin, and Unchecked Call, we show that their appearance rates are decreased by virtue of major updates of the Solidity compiler. We then found the following four key insights. First, after the release of version 0.6, the appearance rate for Locked Money has decreased. Second, regardless of compiler updates, the appearance rate for Using tx.origin is significantly low. Third, although the appearance rate for Unchecked Call has decreased in version 0.8, it still remains high due to various factors, including code clones. Fourth, through analysis of code clones, our promising results show that the appearance rate for Unchecked Call can be further decreased by removing the code clones.