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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
Ontology-Driven Model-to-Model Transformation of Workflow...
Francisco Abreu, Luís Cruz, Sérgio Guerreiro · 2025-11-18 · via cs.SE updates on arXiv.org

Proprietary workflow modeling languages such as Smart Forms & Smart Flow hamper interoperability and reuse because they lock process knowledge into closed formats. To address this vendor lock-in and ease migration to open standards, we introduce an ontology-driven model-to-model pipeline that systematically translates domain-specific workflow definitions to Business Process Model and Notation (BPMN) 2.0. The pipeline comprises three phases: RML-based semantic lifting of JSON to RDF/OWL, ontology alignment and reasoning, and BPMN generation via the Camunda Model API. By externalizing mapping knowledge into ontologies and declarative rules rather than code, the approach supports reusability across vendor-specific formats and preserves semantic traceability between source definitions and target BPMN models. We instantiated the pipeline for Instituto Superior Técnico (IST)'s Smart Forms & Smart Flow and implemented a converter that produces standard-compliant BPMN diagrams. Evaluation on a corpus of 69 real-world workflows produced 92 BPMN diagrams with a 94.2% success rate. Failures (5.81%) stemmed from dynamic behaviors and time-based transitions not explicit in the static JSON. Interviews with support and development teams indicated that the resulting diagrams provide a top-down view that improves comprehension, diagnosis and onboarding by exposing implicit control flow and linking tasks and forms back to their sources. The pipeline is generalizable to other proprietary workflow languages by adapting the ontology and mappings, enabling interoperability and reducing vendor dependency while supporting continuous integration and long-term maintainability. The presented case study demonstrates that ontology-driven M2M transformation can systematically bridge domain-specific workflows and standard notations, offering quantifiable performance and qualitative benefits for stakeholders.