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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
Diagnosing Violations of State-based Specifications in iCFTL
Cristina Stratan, Claudio Mandrioli, Domenico Bianculli · 2025-09-22 · via cs.SE updates on arXiv.org

As modern software systems grow in complexity and operate in dynamic environments, the need for runtime analysis techniques becomes a more critical part of the verification and validation process. Runtime verification monitors the runtime system behaviour by checking whether an execution trace - a sequence of recorded events - satisfies a given specification, yielding a Boolean or quantitative verdict. However, when a specification is violated, such a verdict is often insufficient to understand why the violation happened. To fill this gap, diagnostics approaches aim to produce more informative verdicts. In this paper, we address the problem of generating informative verdicts for violated Inter-procedural Control-Flow Temporal Logic (iCFTL) specifications that express constraints over program variable values. We propose a diagnostic approach based on backward data-flow analysis to statically determine the relevant statements contributing to the specification violation. Using this analysis, we instrument the program to produce enriched execution traces. Using the enriched execution traces, we perform the runtime analysis and identify the statements whose execution led to the specification violation. We implemented our approach in a prototype tool, iCFTL-Diagnostics, and evaluated it on 112 specifications across 10 software projects. Our tool achieves 90% precision in identifying relevant statements for 100 of the 112 specifications. It reduces the number of lines that have to be inspected for diagnosing a violation by at least 90%. In terms of computational cost, iCFTL-Diagnostics generates a diagnosis within 7 min, and requires no more than 25 MB of memory. The instrumentation required to support diagnostics incurs an execution time overhead of less than 30% and a memory overhead below 20%.