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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
FLOW-Methode - Methodenbeschreibung zur Anwendung von FLOW
Kai Stapel, Kurt Schneider · 2012-02-27 · via cs.SE updates on arXiv.org

Information of many kinds is flowing in software projects and organizations. Requirements have to flow from the customer to the developers. Testers need to know the requirements as well. Boundary conditions and design decisions have to be at the right place at the right time. Information flow analysis with FLOW facilitates modeling of mode and route of the flow of information and experience independent of the development methodology. Experience often acts as a control factor, because experienced developers can process and route information more efficiently. Therefore, experience needs to be at the right place at the right time, too. However, most valuable experiences never get documented. Since information and experience is flowing in agile as well as in traditional environments, the FLOW method does not distinguish between agile and traditional, but only between how the flows are shaped. ---- In Softwareprojekten fließen vielerlei Informationen. Anforderungen müssen vom Kunden zu den Entwicklern gelangen. Auch Tester müssen die Anforderungen kennen. Randbedingungen und Entwurfsentscheidungen müssen zur rechten Zeit am rechten Ort sein. Die Informationsflussanalyse mit FLOW ermöglicht es, unabhängig von der Entwicklungsmethode zu modellieren, wie und auf welchem Wege Informationen und Erfahrungen fließen. Erfahrungen spielen dabei oft die Rolle von Steuergrößen, denn erfahrene Mitarbeiter können Informationen kompetenter bearbeiten und weiterleiten. Auch die Erfahrungen müssen in geeigneter Form zur rechten Zeit am rechten Ort sein. Viele Erfahrungen werden aber nie dokumentiert. Da Informationen und Erfahrungen sowohl in agilen als auch in traditionellen Umgebungen fließen müssen, wird in FLOW ein Modell aufgebaut, das nicht nach agil, traditionell oder anderen Bezeichnungen unterscheidet, sondern einzig danach, wie die Flüsse gestaltet sind.