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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
Complex System Design with Design Languages: Method, Appl...
Samuel Vogel, Stephan Rudolph · 2018-05-22 · via cs.SE updates on arXiv.org

Graph-based design languages in UML (Unified Modeling Language) are presented as a method to encode and automate the complete design process and the final optimization of the product or complex system. A design language consists of a vocabulary (digital building blocks) and a set of rules (digital composition knowledge) along with an executable sequence of the rules (digital encoding of the design process). The rule-based mechanism instantiates a central and consistent global product data structure (the so-called design graph). Upon the generation of the abstract central model, the domain-specific engineering models are automatically generated, remotely executed and their results are fed-back into the central design model for subsequent design decisions or optimizations. The design languages are manually modeled and automatically executed in a so-called design compiler. Up to now, a variety of product designs in the areas of aerospace, automotive, machinery and consumer products have been successfully accelerated and automated using graph-based design languages. Different design strategies and mechanisms have been identified and applied in the automation of the design processes. Approaches ranging from the automated and declarative processing of constraints, through fractal nested design patterns, to mathematical dimension-based derivation of the sequence of design actions are used. The existing knowledge for a design determines the global design strategy (top-down vs. bottom-up). Similarity-mechanics in the form of dimensionless invariants are used for evaluation to downsize the solution for an overall complexity reduction. Design patterns, design paradigms (form follows function) and design strategies (divide and conquer) from information science are heavily used to structure, manage and handle complexity.