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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
Large-Scale Empirical Analysis of Continuous Fuzzing: Ins...
Tatsuya Shirai, Olivier Nourry, Yutaro Kashiwa, Kenji Fujiwara, · 2025-10-18 · via cs.SE updates on arXiv.org

Software vulnerabilities are constantly being reported and exploited in software products, causing significant impacts on society. In recent years, the main approach to vulnerability detection, fuzzing, has been integrated into the continuous integration process to run in short and frequent cycles. This continuous fuzzing allows for fast identification and remediation of vulnerabilities during the development process. Despite adoption by thousands of projects, however, it is unclear how continuous fuzzing contributes to vulnerability detection. This study aims to elucidate the role of continuous fuzzing in vulnerability detection. Specifically, we investigate the coverage and the total number of fuzzing sessions when fuzzing bugs are discovered. We collect issue reports, coverage reports, and fuzzing logs from OSS-Fuzz, an online service provided by Google that performs fuzzing during continuous integration. Through an empirical study of a total of approximately 1.12 million fuzzing sessions from 878 projects participating in OSS-Fuzz, we reveal that (i) a substantial number of fuzzing bugs exist prior to the integration of continuous fuzzing, leading to a high detection rate in the early stages; (ii) code coverage continues to increase as continuous fuzzing progresses; and (iii) changes in coverage contribute to the detection of fuzzing bugs. This study provides empirical insights into how continuous fuzzing contributes to fuzzing bug detection, offering practical implications for future strategies and tool development in continuous fuzzing.