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

L
LangChain Blog
Recent Announcements
Recent Announcements
GbyAI
GbyAI
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Microsoft Azure Blog
Microsoft Azure Blog
N
Netflix TechBlog - Medium
人人都是产品经理
人人都是产品经理
MongoDB | Blog
MongoDB | Blog
D
DataBreaches.Net
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
WordPress大学
WordPress大学
U
Unit 42
腾讯CDC
D
Docker
The GitHub Blog
The GitHub Blog
阮一峰的网络日志
阮一峰的网络日志
Vercel News
Vercel News
I
InfoQ
Jina AI
Jina AI
爱范儿
爱范儿
宝玉的分享
宝玉的分享
博客园 - Franky
G
Google Developers Blog
P
Proofpoint News Feed

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
Software Supply Chain Map: How Reuse Networks Expand
Hideaki Hata, Takashi Ishio · 2022-04-14 · via cs.SE updates on arXiv.org

Clone-and-own is a typical code reuse approach because of its simplicity and efficiency. Cloned software components are maintained independently by a new owner. These clone-and-own operations can be occurred sequentially, that is, cloned components can be cloned again and owned by other new owners on the supply chain. In general, code reuse is not documented well, consequently, appropriate changes like security patches cannot be propagated to descendant software projects. However, the OpenChain Project defined identifying and tracking source code reuses as responsibilities of FLOSS software staffs. Hence supporting source code reuse awareness is in a real need. This paper studies software reuse relations in FLOSS ecosystem. Technically, clone-and-own reuses of source code can be identified by file-level clone set detection. Since change histories are associated with files, we can determine origins and destinations in reusing across multiple software by considering times. By building software supply chain maps, we find that clone-and-own is prevalent in FLOSS development, and set of files are reused widely and repeatedly. These observations open up future challenges of maintaining and tracking global software genealogies.