惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

H
Help Net Security
宝玉的分享
宝玉的分享
The Cloudflare Blog
Apple Machine Learning Research
Apple Machine Learning Research
V
Visual Studio Blog
Last Week in AI
Last Week in AI
Hugging Face - Blog
Hugging Face - Blog
博客园 - 司徒正美
博客园 - 三生石上(FineUI控件)
A
About on SuperTechFans
MyScale Blog
MyScale Blog
aimingoo的专栏
aimingoo的专栏
Microsoft Security Blog
Microsoft Security Blog
D
Docker
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
Recent Announcements
Recent Announcements
大猫的无限游戏
大猫的无限游戏
IT之家
IT之家
P
Proofpoint News Feed
L
LangChain Blog
Blog — PlanetScale
Blog — PlanetScale
The GitHub Blog
The GitHub Blog
博客园 - 【当耐特】
Martin Fowler
Martin Fowler

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
Spreadsheet Auditing Software
David Nixon, Mike O'Hara · 2010-01-25 · via cs.SE updates on arXiv.org

It is now widely accepted that errors in spreadsheets are both common and potentially dangerous. Further research has taken place to investigate how frequently these errors occur, what impact they have, how the risk of spreadsheet errors can be reduced by following spreadsheet design guidelines and methodologies, and how effective auditing of a spreadsheet is in the detection of these errors. However, little research exists to establish the usefulness of software tools in the auditing of spreadsheets. This paper documents and tests office software tools designed to assist in the audit of spreadsheets. The test was designed to identify the success of software tools in detecting different types of errors, to identify how the software tools assist the auditor and to determine the usefulness of the tools.