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
博客园_首页
Google DeepMind News
Google DeepMind News
博客园 - Franky
The GitHub Blog
The GitHub Blog
GbyAI
GbyAI
有赞技术团队
有赞技术团队
Microsoft Azure Blog
Microsoft Azure Blog
G
Google Developers Blog
Recent Announcements
Recent Announcements
A
About on SuperTechFans
博客园 - 【当耐特】
博客园 - 三生石上(FineUI控件)
酷 壳 – CoolShell
酷 壳 – CoolShell
美团技术团队
罗磊的独立博客
IT之家
IT之家
博客园 - 聂微东
Stack Overflow Blog
Stack Overflow Blog
Jina AI
Jina AI
腾讯CDC
P
Proofpoint News Feed
Hugging Face - Blog
Hugging Face - Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com

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 Manifesto for Semantic Model Differencing
Shahar Maoz, Jan Oliver Ringert, Bernhard Rumpe · 2014-09-08 · via cs.SE updates on arXiv.org

Models are heavily used in software engineering and together with their systems they evolve over time. Thus, managing their changes is an important challenge for system maintainability. Existing approaches to model differencing concentrate on heuristics matching between model elements and on finding and presenting differences at a concrete or abstract syntactic level. While showing some success, these approaches are inherently limited to comparing syntactic structures. This paper is a manifesto for research on semantic model differencing. We present our vision to develop semantic diff operators for model comparisons: operators whose input consists of two models and whose output is a set of diff witnesses, instances of one model that are not instances of the other. In particular, if the models are syntactically different but there are no diff witnesses, the models are semantically equivalent. We demonstrate our vision using two concrete diff operators, for class diagrams and for activity diagrams. We motivate the use of semantic diff operators, brie y discuss the algorithms to compute them, list related challenges, and show their application and potential use as new fundamental building blocks for change management in model-driven engineering.