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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
Bridging the Gap: A Comparative Study of Academic and Dev...
Francesco Salzano, Lodovica Marchesi, Cosmo Kevin Antenucci, Sim · 2025-04-17 · via cs.SE updates on arXiv.org

In this paper, we investigate the strategies adopted by Solidity developers to fix security vulnerabilities in smart contracts. Vulnerabilities are categorized using the DASP TOP 10 taxonomy, and fixing strategies are extracted from GitHub commits in open-source Solidity projects. Each commit was selected through a two-phase process: an initial filter using natural language processing techniques, followed by manual validation by the authors. We analyzed these commits to evaluate adherence to academic best practices. Our results show that developers often follow established guidelines for well-known vulnerability types such as Reentrancy and Arithmetic. However, in less-documented categories like Denial of Service, Bad Randomness, and Time Manipulation, adherence is significantly lower, suggesting gaps between academic literature and practical development. From non-aligned commits, we identified 27 novel fixing strategies not previously discussed in the literature. These emerging patterns offer actionable solutions for securing smart contracts in underexplored areas. To evaluate the quality of these new fixes, we conducted a questionnaire with academic and industry experts, who assessed each strategy based on Generalizability, Long-term Sustainability, and Effectiveness. Additionally, we performed a post-fix analysis by tracking subsequent commits to the fixed files, assessing the persistence and evolution of the fixes over time. Our findings offer an empirically grounded view of how vulnerabilities are addressed in practice, bridging theoretical knowledge and real-world solutions in the domain of smart contract security.