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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
How Deep Does Your Dependency Tree Go? An Empirical Study...
Jahidul Arafat · 2025-12-12 · via cs.SE updates on arXiv.org

Modern software development relies on package ecosystems where a single declared dependency can pull in many additional transitive packages. This dependency amplification, defined as the ratio of transitive to direct dependencies, has major implications for software supply chain security, yet amplification patterns across ecosystems have not been compared at scale. We present an empirical study of 500 projects across ten major ecosystems, including Maven Central for Java, npm Registry for JavaScript, crates io for Rust, PyPI for Python, NuGet Gallery for dot NET, RubyGems for Ruby, Go Modules for Go, Packagist for PHP, CocoaPods for Swift and Objective C, and Pub for Dart. Our analysis shows that Maven exhibits mean amplification of 24.70 times, compared to 4.48 times for Go Modules, 4.32 times for npm, and 0.32 times for CocoaPods. We find significant differences with large effect sizes in 22 of 45 pairwise comparisons, challenging the assumption that npm has the highest amplification due to its many small purpose packages. We observe that 28 percent of Maven projects exceed 10 times amplification, indicating a systematic pattern rather than isolated outliers, compared to 14 percent for RubyGems, 12 percent for npm, and zero percent for Cargo, PyPI, Packagist, CocoaPods, and Pub. We attribute these differences to ecosystem design choices such as dependency resolution behavior, standard library completeness, and platform constraints. Our findings suggest adopting ecosystem specific security strategies, including systematic auditing for Maven environments, targeted outlier detection for npm and RubyGems, and continuation of current practices for ecosystems with controlled amplification. We provide a full replication package with data and analysis scripts.