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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
Consensus-Free Spreadsheet Integration
Brandon Baylor, Eric Daimler, James Hansen, Esteban Montero, Rya · 2022-09-29 · via cs.SE updates on arXiv.org

We describe a method for merging multiple spreadsheets into one sheet, and/or exchanging data among the sheets, by expressing each sheet's formulae as an algebraic (equational) theory and each sheet's values as a model of its theory, expressing the overlap between the sheets as theory and model morphisms, and then performing colimit, lifting, and Kan-extension constructions from category theory to compute a canonically universal integrated theory and model, which can then be expressed as a spreadsheet. Our motivation is to find methods of merging engineering models that do not require consensus (agreement) among the authors of the models being merged, a condition fulfilled by our method because theory and model morphisms are semantics-preserving. We describe a case study of this methodology on a real-world oil and gas calculation at a major energy company, describing the theories and models that arise when integrating two different casing pressure test (MASP) calculation spreadsheets constructed by two non-interacting engineers. We also describe the automated theorem proving burden associated with both verifying the semantics preservation of the overlap mappings as well as verifying the conservativity/consistency of the resulting integrated sheet. We conclude with thoughts on how to apply the methodology to scale engineering efforts across the enterprise.