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

J
Java Code Geeks
Last Week in AI
Last Week in AI
T
Tailwind CSS Blog
WordPress大学
WordPress大学
B
Blog RSS Feed
T
The Blog of Author Tim Ferriss
F
Fortinet All Blogs
aimingoo的专栏
aimingoo的专栏
MongoDB | Blog
MongoDB | Blog
博客园 - Franky
C
Check Point Blog
P
Proofpoint News Feed
H
Help Net Security
月光博客
月光博客
博客园_首页
Stack Overflow Blog
Stack Overflow Blog
博客园 - 三生石上(FineUI控件)
Martin Fowler
Martin Fowler
Recent Announcements
Recent Announcements
人人都是产品经理
人人都是产品经理
U
Unit 42
美团技术团队
I
InfoQ
A
About on SuperTechFans

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
An Exploratory Analysis of the Impact of Named Ranges on ...
Ruth McKeever, Kevin McDaid, Brian Bishop · 2009-08-07 · via cs.SE updates on arXiv.org

This paper describes an exploratory empirical study of the effect of named ranges on spreadsheet debugging performance. Named ranges are advocated in both academia and industry, yet no experimental evidence has been cited to back up these recommendations. This paper describes an exploratory experiment involving 21 participants that assesses the performance of novices debugging a spreadsheet containing named ranges. The results are compared with the performance of a different set of novices debugging the same spreadsheet without named ranges. The findings suggest that novice users debug on average significantly fewer errors if the spreadsheet contains named ranges. The purpose of the investigative study is to derive a detailed and coherent set of research questions regarding the impact of range names on the debugging performance and behaviour of spreadsheet users. These will be answered through future controlled experiments.