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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
Drop it All or Pick it Up? How Developers Responded to th...
Vittunyuta Maeprasart, Ali Ouni, Raula Gaikovina Kula · 2024-07-05 · via cs.SE updates on arXiv.org

Although using third-party libraries has become prevalent in contemporary software development, developers often struggle to update their dependencies. Prior works acknowledge that due to the migration effort, priority and other issues cause lags in the migration process. The common assumption is that developers should drop all other activities and prioritize fixing the vulnerability. Our objective is to understand developer behavior when facing high-risk vulnerabilities in their code. We explore the prolific, and possibly one of the cases of the Log4JShell, a vulnerability that has the highest severity rating ever, which received widespread media attention. Using a mixed-method approach, we analyze 219 GitHub Pull Requests (PR) and 354 issues belonging to 53 Maven projects affected by the Log4JShell vulnerability. Our study confirms that developers show a quick response taking from 5 to 6 days. However, instead of dropping everything, surprisingly developer activities tend to increase for all pending issues and PRs. Developer discussions involved either giving information (29.3\%) and seeking information (20.6\%), which is missing in existing support tools. Leveraging this possibly-one of a kind event, insights opens up a new line of research, causing us to rethink best practices and what developers need in order to efficiently fix vulnerabilities.