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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
Bursts and Triggers: Socially-Driven Activity in Open-Sou...
[Submitted on 30 Sep 2025 (v1), last revised 13 Jul 2026 (this v · 2025-09-30 · via cs.SE updates on arXiv.org

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Abstract:The long-term sustainability of Open Source Software (OSS) communities depends on the activity of their developers, yet the social mechanisms driving this collective behavior remain poorly understood. Analyzing commit histories across 51 major OSS communities, we find that developer contributions are strongly "bursty" in time. To test whether this burstiness reflects social responsiveness rather than individual habit alone, we model developer interactions as temporal co-editing networks and introduce a method to detect activity triggers, episodes in which one developer editing another's code is followed by an unusually rapid response, and the cascades they form. Benchmarking these against a null model that destroys the temporal ordering of co-edits while preserving each developer's activity rate, we find statistically significant cascades in 28 of 51 projects (55%) under our default configuration, though prevalence ranges from 24% to 82% across detection thresholds. Whether a project exhibits significant cascades is governed primarily by its scale rather than governance or commit concentration. Finally, as a secondary application, we test whether these signals inform developer churn: features capturing the recent (in)activity of a developer's collaborators add some predictive value, but a developer's own inactivity dominates, propagation over the co-editing graph adds little, and simple models match graph neural networks. Our results characterize developer responsiveness as a measurable component of collective OSS dynamics.

Submission history

From: Lisi Qarkaxhija [view email]
[v1] Tue, 30 Sep 2025 12:28:35 UTC (142 KB)
[v2] Fri, 31 Oct 2025 12:27:12 UTC (142 KB)
[v3] Mon, 13 Jul 2026 08:43:07 UTC (170 KB)