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

V
Visual Studio Blog
Recent Announcements
Recent Announcements
有赞技术团队
有赞技术团队
The GitHub Blog
The GitHub Blog
N
Netflix TechBlog - Medium
腾讯CDC
A
About on SuperTechFans
博客园 - 叶小钗
大猫的无限游戏
大猫的无限游戏
小众软件
小众软件
D
DataBreaches.Net
D
Docker
宝玉的分享
宝玉的分享
量子位
Microsoft Azure Blog
Microsoft Azure Blog
Martin Fowler
Martin Fowler
博客园 - 三生石上(FineUI控件)
Jina AI
Jina AI
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
V
V2EX
Last Week in AI
Last Week in AI
H
Help Net Security
Hugging Face - Blog
Hugging Face - Blog
M
MIT News - Artificial intelligence

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
Four presumed gaps in the software engineering research c...
Lutz Prechelt · 2019-11-22 · via cs.SE updates on arXiv.org

Background: The state of the art in software engineering consists of a myriad of contributions and the gaps between them; it is difficult to characterize. Questions: In order to help understanding the state of the art, can we identify gaps in our knowledge that are at a very general, widely relevant level? Which research directions do these gaps suggest? Method: 54 expert interviews with senior members of the ICSE community, evaluated qualitatively using elements of Grounded Theory Methodology. Results: Our understanding of complexity, of good-enoughness, and of developers' strengths is underdeveloped. Some other relevant factors' relevance is apparently not clear. Software engineering is not yet an evidence-based discipline. Conclusion: More software engineering research should concern itself with emergence phenomena, with how engineering tradeoffs are made, with the assumptions underlying research works, and with creating certain taxonomies. Such work would also allow software engineering to become more evidence-based.