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

The GitHub Blog
The GitHub Blog
Martin Fowler
Martin Fowler
Vercel News
Vercel News
U
Unit 42
Engineering at Meta
Engineering at Meta
aimingoo的专栏
aimingoo的专栏
MyScale Blog
MyScale Blog
Y
Y Combinator Blog
阮一峰的网络日志
阮一峰的网络日志
爱范儿
爱范儿
Apple Machine Learning Research
Apple Machine Learning Research
博客园_首页
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
B
Blog RSS Feed
N
Netflix TechBlog - Medium
GbyAI
GbyAI
F
Fortinet All Blogs
MongoDB | Blog
MongoDB | Blog
大猫的无限游戏
大猫的无限游戏
C
Check Point Blog
M
MIT News - Artificial intelligence
D
Docker
IT之家
IT之家
Stack Overflow Blog
Stack Overflow Blog

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
Opportunities and Security Risks of Technical Leverage: A...
Haya Samaana, Diego Elias Costa, Ahmad Abdellatif, Emad Shihab · 2024-12-10 · via cs.SE updates on arXiv.org

To comply with high productivity demands, software developers reuse free open-source software (FOSS) code to avoid reinventing the wheel when incorporating software features. The reliance on FOSS reuse has been shown to improve productivity and the quality of delivered software; however, reusing FOSS comes at the risk of exposing software projects to public vulnerabilities. Massacci and Pashchenko have explored this trade-off in the Java ecosystem through the lens of technical leverage: the ratio of code borrowed from FOSS over the code developed by project maintainers. In this paper, we replicate the work of Massacci and Pashchenko and we expand the analysis to include level-1 transitive dependencies to study technical leverage in the fastest-growing NPM ecosystem. We investigated 14,042 NPM library releases and found that both opportunities and risks of technical leverage are magnified in the NPM ecosystem. Small-medium libraries leverage 2.5x more code from FOSS than their code, while large libraries leverage only 3\% of FOSS code in their projects. Our models indicate that technical leverage shortens the release cycle for small-medium libraries. However, the risk of vulnerability exposure is 4-7x higher for libraries with high technical leverage. We also expanded our replication study to include the first level of transitive dependencies, and show that the results still hold, albeit with significant changes in the magnitude of both opportunities and risks of technical leverage. Our results indicate the extremes of opportunities and risks in NPM, where high technical leverage enables fast releases but comes at the cost of security risks.