惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

N
Netflix TechBlog - Medium
I
InfoQ
Engineering at Meta
Engineering at Meta
Jina AI
Jina AI
Recent Announcements
Recent Announcements
T
The Blog of Author Tim Ferriss
P
Proofpoint News Feed
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
D
Docker
Microsoft Security Blog
Microsoft Security Blog
宝玉的分享
宝玉的分享
Last Week in AI
Last Week in AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
GbyAI
GbyAI
博客园 - Franky
博客园 - 聂微东
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - 叶小钗
酷 壳 – CoolShell
酷 壳 – CoolShell
B
Blog RSS Feed
WordPress大学
WordPress大学
MyScale Blog
MyScale Blog
月光博客
月光博客
罗磊的独立博客

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
The Death Spiral of Open Source Projects: A Post-Mortem A...
Mohit Kaushik, Kuljit Kaur Chahal · 2026-05-12 · via cs.SE updates on arXiv.org

Open Source Software projects (OSS) are central to modern technology, yet their survival rates remain low. Prior research has examined project mortality through macro-level indicators such as commit activity, developer abandonment, and ecosystem dependencies, but the micro-level dynamics of the Pull Request (PR) workflow have been largely overlooked. This study provides the first large-scale post-mortem analysis of PR workflows across 1,736 inactive GitHub repositories and 1.3 million human-driven PRs. Using a mixed-method quantitative design, we investigate three dimensions of mortality. First, our comparative descriptive analysis shows that workflow friction, extended review cycles, and negativity penalties are endemic properties of the entire GitHub platform across both active and inactive projects. Rejected PRs consistently attract higher discussion and negativity regardless of project health. Second, our evolutionary analysis identifies a universal ``death spiral" marked by declining innovation rates, exponential backlog growth, rising merge latency. The collapse was defined by silence and disengagement. Labeling formalization remained endemic throughout the lifecycle, while toxicity did not intensify. Finally, our explanatory modeling demonstrates that project lifespan is not determined by workflow efficiency but by inherent value and ecosystem dynamics. Popularity and innovation emerge as strong positive predictors of survival, while friction, rejection rates, labeling formalization, and negativity scale with longevity as byproducts rather than causes of failure. Robustness checks across alternative inactivity thresholds confirm these findings. Together, this work reframes OSS mortality as a socio-technical phenomenon in which abandonment and ecosystem value dominate survival outcomes, while PR-level workflow discipline plays a secondary role.