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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
Detection of Syntactic Aspect Interaction in UML State Di...
Zaid Altahat, Tzilla Elrad, Luay Tahat, Nada Almasri · 2013-12-25 · via cs.SE updates on arXiv.org

Aspect Oriented Modeling separates crosscutting concerns by defining Aspects and composition mechanisms at the model level. Composition of multiple Aspects will most likely result in more than one Aspect matching the same join points. Consequently, Aspects do not always interact in a predictable manner when woven together. Intended interaction among aspects is designed by the system designer. Unintended interaction (or interference) must be automatically managed. When the woven aspect demonstrates a behavior that is different than its autonomous behavior, then this is a potential interference. Interference has been recently reported in Aspect Oriented Software Development (AOSD) by the industry. Leaving this problem unsolved may result in erratic software behavior and will hinder the adaptation of AOSD by the industry. This identified problem is similar to a phenomenon that exists in graph transformation systems where multiple Graph Transformation rules share some conflicting elements, it is referred to as Critical Pair Analysis and it provides an algebraic-based mechanism to detect and analyze the interaction of the rules. In this paper we propose a framework to detect unintended Aspect interaction at the model level. The proposed framework transforms Aspects modeled in UML State Diagram to Graph Transformation Rules, and then it applies Critical Pair Analysis to detect unintended interactions among aspects. This will enable developers to specify only the order of precedence for intended interaction among aspects without the need to manually investigate unintended interactions for the combinations of every Aspect to every other Aspect in the system. The proposed interaction detection solution is automated, modular, and independent of the base model; which adds the advantage of not having to re-evaluate the interaction each time the base model changes.