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

aimingoo的专栏
aimingoo的专栏
I
InfoQ
B
Blog RSS Feed
D
Docker
GbyAI
GbyAI
N
Netflix TechBlog - Medium
Y
Y Combinator Blog
F
Fortinet All Blogs
P
Proofpoint News Feed
Microsoft Azure Blog
Microsoft Azure Blog
人人都是产品经理
人人都是产品经理
Martin Fowler
Martin Fowler
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
M
MIT News - Artificial intelligence
C
Check Point Blog
Vercel News
Vercel News
云风的 BLOG
云风的 BLOG
博客园 - Franky
Google DeepMind News
Google DeepMind News
WordPress大学
WordPress大学
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
V
V2EX
Last Week in AI
Last Week in AI
L
LangChain 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 Survey of Maturity Models from Nolon to DevOps and Thei...
James J. Cusick · 2019-07-02 · via cs.SE updates on arXiv.org

This paper traces the history of Maturity Models and their impact on Process Improvement from the early work of Shewhart to their current usage with DevOps. The history of modern process improvement can be traced at least to Shewhart. From his foundational process contributions and those of other innovators a variety of methods and tools to aid in process quality advancement were developed. This paper begins by reviewing those early steps and then focuses on the emergence of Maturity Models in the 1970s with initial approach by Nolan. The broad adoption of Maturity Models that followed through the success of the CMM and then the CMMI approaches is detailed. This then leads to a general survey of additional models developed for such areas as IT Service Management, ITIL, Project Management, Agile Development, DevOps, CERT, and MDM among others. Finally, this paper discusses the application of these models in the support of process improvement and their limitations. Readers of this paper can expect to gain an appreciation for the origins of these models and surrounding methods as well as an ability to conduct comparative analysis of such models to aid in their selection and application. Keywords: Process Improvement, Process Engineering, Maturity Models, Capability Maturity Models, CMM, CMMI, ITSM, ITIL, Agile, DevOps, History of Science, History of Computing, Software Engineering, Quality.