The LocalM™ AiD framework is built on a foundation of academic research, industry best practices, and emerging standards in AI-assisted software development.
SOURCES30+ Research Papers & Articles
COVERAGE2023-2026
CATEGORIESAcademic, Industry, Standards
LAST UPDATEDJanuary 2026
Academic Research
Foundational Papers
Paper
Key Contribution
AI4SE: A Taxonomy for AI in Software Engineering
Comprehensive classification of AI applications across SE lifecycle stages
Metacognitive Framework for AI Programming Education
Educational principles for developing critical evaluation skills
Ten Simple Rules for AI-Assisted Coding
Evidence-based practical guidelines for human-AI collaboration
Evaluating Large Language Models for Code Generation
Benchmark methodologies for AI code quality
Human-AI Collaboration
Paper
Key Contribution
The Programmer’s Model of AI Interaction
Understanding how developers interact with AI assistants
Cognitive Load in AI-Assisted Programming
Impact of AI tools on developer cognition
Trust Calibration in Human-AI Systems
Framework for appropriate trust in AI outputs
Software Engineering Process
Paper
Key Contribution
AI-Native Software Development Lifecycle
Adapting SDLC for AI integration
Specification-Driven AI Development
Contract-first approaches with AI
Context Engineering for LLMs
Maximizing AI effectiveness through context
Industry Practitioner Sources
Methodology & Practices
Source
Key Contribution
V-Bounce Model
AI-native SDLC balancing automation with oversight
Single Conversation Methodology
Session management for AI interactions
Agentic Coding Best Practices
Patterns for AI agent workflows
Context Engineering Guide
Prompt and context optimization
Security & Governance
Source
Key Contribution
Rules for AI Coding Agents
Security-focused guidelines from production experience
OWASP LLM Top 10
Security vulnerabilities in LLM applications
Responsible AI Implementation
Enterprise governance frameworks
AI Security Best Practices
Government security standards
Adoption & Training
Source
Key Contribution
AI Coding Adoption Best Practices
Enterprise rollout patterns
Developer Productivity with AI
Measuring AI impact on development
AI Skills Development Framework
Training curriculum guidelines
Emerging Standards
Agent Protocols
Standard
Description
AGENTS.md
Agent capability declaration standard
SKILL.md
Reusable skill definitions for AI agents
Model Context Protocol (MCP)
Standardized AI-tool integration
Agent-to-Agent (A2A)
Inter-agent communication patterns
Enterprise Frameworks
Standard
Relevance
TOGAF
EA principle structure foundation
FEAF
Federal enterprise architecture alignment
DoDAF
Defense architecture framework patterns
ISO/IEC 42001
AI management system standard
Research Methodology
Source Selection Criteria
Relevance - Direct applicability to AI-assisted development
Recency - Published within rapidly evolving field timeline
Authority - Peer-reviewed or recognized practitioners