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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
A Reference Architecture for Quantum Computing as a Service
Aakash Ahmad, Ahmed B. Altamimi, Jamal Aqib · 2023-06-04 · via cs.SE updates on arXiv.org

Quantum computers (QCs) aim to disrupt the status-quo of computing -- replacing traditional systems and platforms that are driven by digital circuits and modular software -- with hardware and software that operates on the principle of quantum mechanics. QCs that rely on quantum mechanics can exploit quantum circuits (i.e., quantum bits for manipulating quantum gates) to achieve "quantum computational supremacy" over traditional, i.e., digital computing systems. Currently, the issues that impede mass-scale adoption of quantum systems are rooted in the fact that building, maintaining, and/or programming QCs is a complex and radically distinct engineering paradigm when compared to challenges of classical computing and software engineering. Quantum service orientation is seen as a solution that synergises the research on service computing and quantum software engineering (QSE) to allow developers and users to build and utilise quantum software services based on pay-per-shot utility computing model. The pay-per-shot model represents a single execution of instruction on quantum processing unit and it allows vendors (e.g., Amazon Braket) to offer their QC platforms, simulators, software services etc. to enterprises and individuals who do not need to own or maintain quantum systems. This research contributes by 1) developing a reference architecture for enabling quantum computing as a service, 2) implementing microservices with the quantum-classic split pattern as an architectural use-case, and 3) evaluating the reference architecture based on feedback by 22 practitioners. In the QSE context, the research focuses on unifying architectural methods and service-orientation patterns to promote reuse knowledge and best practices to tackle emerging and futuristic challenges of architecting and implementing Quantum Computing as a Service (QCaaS).