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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
On the Freshness of Pinned Dependencies in Maven
Vasudev Vikram, Yuvraj Agarwal, Rohan Padhye · 2025-10-27 · via cs.SE updates on arXiv.org

Library dependencies in software ecosystems play a crucial role in the development of software. As newer releases of these libraries are published, developers may opt to pin their dependencies to a particular version. While pinning may have benefits in ensuring reproducible builds and avoiding breaking changes, it bears larger risks in using outdated dependencies that may contain bugs and security vulnerabilities. To understand the frequency and consequences of dependency pinning, we first define the concepts of stale and fresh pins, which are distinguished based on how outdated the dependency is relative to the release date of the project. We conduct an empirical study to show that over 60% of consumers of popular Maven libraries contain stale pins to their dependencies, with some outdated versions over a year old. These pinned versions often miss out on security fixes; we find that 10% of all dependency upgrades in our dataset to the latest minor or patch version would reduce security vulnerabilities. We prototype an approach called Pin-Freshener that can encourage developers to freshen their pins by leveraging the insight that crowdsourced tests of peer projects can provide additional signal for the safety of an upgrade. Running Pin-Freshener on dependency upgrades shows that just 1-5 additional test suites can provide 35-100% more coverage of a dependency, compared to that of a single consumer test suite. Our evaluation on real-world pins to the top 500 popular libraries in Maven shows that Pin-Freshener can provide an additional signal of at least 5 passing crowdsourced test suites to over 3,000 consumers to safely perform an upgrade that reduces security vulnerabilities. Pin-Freshener can provide practical confidence to developers by offering additional signal beyond their own test suites, representing an improvement over current practices.