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

量子位
B
Blog
Last Week in AI
Last Week in AI
Jina AI
Jina AI
WordPress大学
WordPress大学
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
宝玉的分享
宝玉的分享
T
Tailwind CSS Blog
P
Proofpoint News Feed
酷 壳 – CoolShell
酷 壳 – CoolShell
Google DeepMind News
Google DeepMind News
U
Unit 42
雷峰网
雷峰网
J
Java Code Geeks
Apple Machine Learning Research
Apple Machine Learning Research
MongoDB | Blog
MongoDB | Blog
F
Fortinet All Blogs
美团技术团队
Y
Y Combinator Blog
腾讯CDC
B
Blog RSS Feed
T
The Blog of Author Tim Ferriss
Microsoft Azure Blog
Microsoft Azure Blog
Recent Announcements
Recent Announcements

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
A Change Support Model for Distributed Collaborative Work
Phan Thi Thanh Huyen, Koichiro Ochimizu · 2012-12-08 · via cs.SE updates on arXiv.org

Distributed collaborative software development tends to make artifacts and decisions inconsistent and uncertain. We try to solve this problem by providing an information repository to reflect the state of works precisely, by managing the states of artifacts/products made through collaborative work, and the states of decisions made through communications. In this paper, we propose models and a tool to construct the artifact-related part of the information repository, and explain the way to use the repository to resolve inconsistencies caused by concurrent changes of artifacts. We first show the model and the tool to generate the dependency relationships among UML model elements as content of the information repository. Next, we present the model and the method to generate change support workflows from the information repository. These workflows give us the way to efficiently modify the change-related artifacts for each change request. Finally, we define inconsistency patterns that enable us to be aware of the possibility of inconsistency occurrences. By combining this mechanism with version control systems, we can make changes safely. Our models and tool are useful in the maintenance phase to perform changes safely and efficiently.