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

爱范儿
爱范儿
MyScale Blog
MyScale Blog
Recent Announcements
Recent Announcements
N
Netflix TechBlog - Medium
GbyAI
GbyAI
Vercel News
Vercel News
The GitHub Blog
The GitHub Blog
阮一峰的网络日志
阮一峰的网络日志
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
Visual Studio Blog
Martin Fowler
Martin Fowler
腾讯CDC
大猫的无限游戏
大猫的无限游戏
aimingoo的专栏
aimingoo的专栏
云风的 BLOG
云风的 BLOG
J
Java Code Geeks
WordPress大学
WordPress大学
P
Proofpoint News Feed
雷峰网
雷峰网
酷 壳 – CoolShell
酷 壳 – CoolShell
有赞技术团队
有赞技术团队
人人都是产品经理
人人都是产品经理
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Y
Y Combinator Blog

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
Context-Specific Instruction: A Longitudinal Study on Deb...
Ziyi Zhang, Devjeet Roy, Venera Arnaoudova · 2025-09-26 · via cs.SE updates on arXiv.org

Bug localization is a critical skill, yet novices often lack systematic approaches. Prior work tested abstract guidelines and general concrete steps; the impact of context-specific instruction is unclear. We ran an eight-week longitudinal study with four conditions: no instruction (G1), abstract guidelines (G2), concrete steps (G3), and our context-specific instruction that pairs concrete bug-localization steps with problem-specific details (G4). Forty-four undergraduates participated; 41 completed all five sessions (S1-S5). Each session included 2-3 debugging tasks to identify the minimal code element containing a seeded logical fault. We measured correctness (binary), time to completion, self-perceived scores (stress, difficulty, satisfaction, and strategy adherence). G4 achieved higher correctness and shorter time to completion: it reached 80% correctness after one session (vs. 20-44% for other groups) and maintained 80% after three weeks, outperforming all groups (p < 0.05); its time to completion stabilized at 13-15 minutes in S1, whereas other groups took 2-3 sessions to stabilize at 22-27 minutes. Qualitative responses showed lower stress and higher satisfaction in G4, with participants internalizing strategies via contextual examples. We conclude that context-specific instruction yields faster skill acquisition and stronger retention than abstract guidelines or context-agnostic steps. Even 1-2 sessions produced significant gains, while extended practice optimized and stabilized performance. Integrating contextual examples with abstract principles may bridge theory-practice gaps in bug-localization education and provide a more equitable path for novices.