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

G
Google Developers Blog
人人都是产品经理
人人都是产品经理
爱范儿
爱范儿
云风的 BLOG
云风的 BLOG
Last Week in AI
Last Week in AI
H
Hackread – Cybersecurity News, Data Breaches, AI and More
B
Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
H
Help Net Security
B
Blog RSS Feed
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
N
Netflix TechBlog - Medium
S
SegmentFault 最新的问题
The Cloudflare Blog
I
InfoQ
美团技术团队
博客园 - 三生石上(FineUI控件)
MyScale Blog
MyScale Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 司徒正美
L
LangChain Blog
A
About on SuperTechFans
T
The Blog of Author Tim Ferriss
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
A Systematic Analysis of Higher Education on Software Eng...
[Submitted on 14 Dec 2025 (v1), last revised 9 Jul 2026 (this ve · 2025-12-14 · via cs.SE updates on arXiv.org

View PDF HTML (experimental)

Abstract:Objectives. Software engineering educators strive to continuously improve and refine their courses and programs. Understanding the current state of practice of software engineering higher education can empower educators to critically assess their courses, fine-tune them, and ultimately enhance their educational curricula. In this study, we provide an encompassing analysis of higher education on software engineering by considering the educational offering of the Netherlands.
Study methods. We adopt a crowdsourced analysis considering 10 Dutch universities and 207 courses. Courses are analysed via a set of key knowledge areas adapted from the SWEBOK, which are mapped to courses by educators of their universities. The mapping process is refined via homogenisation and internal consistency improvement phases, followed by a data analysis phase.
Findings. Given its fundamental nature, Construction and Programming is the most covered knowledge area at Bachelor level. Other knowledge areas are equally covered at Bachelor and Master level (e.g., software engineering models), while more advanced ones are almost exclusively provided at Master level (e.g., Maintenance). Three clusters of tightly coupled knowledge areas emerge: (i) requirements, architecture, and design, (ii) testing, verification, and security, and (iii) process-oriented and DevOps topics. Dutch universities cover all knowledge areas uniformly, with minor deviations reflecting institutional research strengths.
Conclusions. Our results highlight correlations among key software engineering knowledge areas. We also identify underrepresented areas, such as software economics, which educators may consider including in curricula. We invite researchers to make use of our research method in their own geographical region to globally compare software engineering education programs.

Submission history

From: Fabiano Dalpiaz [view email]
[v1] Sun, 14 Dec 2025 11:37:16 UTC (511 KB)
[v2] Thu, 9 Jul 2026 09:43:56 UTC (297 KB)