ecoguard-ai 0.1.3
AI-augmented software development pipeline solution for quality, security, and environmental sustainability
Requires Python: >=3.8

# EcoGuard AI: The Sentient Core for Future-Proof Software Pipelines

![EcoGuard AI Logo](https://img.shields.io/badge/EcoGuard-AI-green?style=for-the-badge)
![Version](https://img.shields.io/badge/version-0.1.3-blue?style=for-the-badge)
![License](https://img.shields.io/badge/license-MIT-brightgreen?style=for-the-badge)

## 🌟 Project Vision

EcoGuard AI is a transformative, AI-augmented software development pipeline solution designed to proactively champion excellence across the entire software lifecycle. It serves as a unified intelligence layer, meticulously analyzing both human-written and AI-generated code for quality, security, and environmental sustainability.

### Core Mission
To embed comprehensive oversight directly into developer workflows and CI/CD processes, making best practices intuitive and sustainable outcomes measurable while addressing inefficiencies unique to AI-generated code.

## 🎯 Key Principles

- **🔧 Modularization**: Build distinct, pluggable analysis modules
- **💡 Actionable Feedback**: Provide clear, context-sensitive suggestions with quantified impact
- **🤖 AI-Specificity**: Target inefficiencies and risks unique to AI/ML code and AI-driven development
- **🌱 Carbon-Aware**: Explicitly link software choices to environmental impact across the stack
- **🔓 Open-Source First**: Foster community contribution and transparency
- **☁️ Hybrid Deployment**: Support local execution, centralized server reporting (Cloud/On-Prem/Air-Gapped)
- **👨‍💻 Developer-Centric**: Prioritize seamless integration into developer workflows
- **🔄 Holistic Scope**: Cover application code, infrastructure, and containers

## 🚀 Core Capabilities

### Multi-Faceted Code Analysis
- **Static Analysis**: AST-based analysis for comprehensive code evaluation
- **Green Software (Eco-Linter)**: Detection of inefficient patterns with carbon impact estimation
- **AI-Code Specifics**: Identification of AI-generated code anti-patterns and inefficiencies
- **Security (SAST)**: Vulnerability detection and secure coding practice enforcement
- **Dependency Analysis**: Third-party library vulnerability scanning

### Sustainability & Resource Optimization
- **Carbon Quantification**: Static estimation and runtime measurement of code's carbon footprint
- **Cloud Resource Optimization**: Integration with cloud provider sustainability APIs
- **Infrastructure as Code Analysis**: Scanning for cost, security, and carbon optimization
- **Container Image Analysis**: Optimizing container images for efficiency and security

### AI-Augmented Intelligence & Automation
- **MCP Feedback Loop**: Structured findings sent to AI orchestration platforms
- **AI-Assisted Refactoring**: Complex code improvement suggestions
- **Automated Fixes**: Automatic corrections for common issues
- **Predictive Analysis**: Proactive guidance for AI code generation

## 📋 50-Stage Development Roadmap

### Phase 1: Foundation & Research (Stages 1-10)

**Stage 1: Project Setup & Architecture Design** ✅
- [x] Create GitHub repository structure
- [x] Define project architecture and module interfaces
- [x] Set up initial documentation framework
- [x] Establish coding standards and contribution guidelines
- [x] GitHub Actions CI/CD pipeline configured and working

**Stage 2: Core Dependencies & Environment Setup** ✅ **COMPLETED**
- [x] Enhanced Python development environment with comprehensive pyproject.toml
- [x] Configured advanced development tools (black, flake8, mypy, pytest, ruff, tox)
- [x] Enhanced CLI entry point structure with robust functionality
- [x] Advanced GitHub Actions CI/CD with multi-matrix testing, security scans, and artifact collection
- [x] Pre-commit hooks with comprehensive code quality checks
- [x] Development automation tools (Makefile, setup scripts, tox environments)
- [x] Fixed code formatting issues and line length violations
- [x] Verified complete development workflow and package building functionality

**Stage 3: AST Analysis Research & Prototyping**
- [ ] Deep dive into Python AST module capabilities
- [ ] Create proof-of-concept AST parser for basic Python constructs
- [ ] Research Tree-sitter and ANTLR for multi-language support
- [ ] Document AST traversal patterns and node identification strategies

**Stage 4: Green Software Principles Research**
- [ ] Analyze existing green software tools (ec0lint, Creedengo)
- [ ] Research CodeCarbon and CarbonTracker integration possibilities
- [ ] Identify top 10 Python anti-patterns for energy efficiency
- [ ] Create carbon impact estimation methodology

**Stage 5: AI Code Pattern Analysis**
- [ ] Research common AI-generated code inefficiencies
- [ ] Analyze LLM output patterns from popular AI coding assistants
- [ ] Document "hallucinated" code patterns and redundancies
- [ ] Create taxonomy of AI-specific code smells

**Stage 6: Security Analysis Framework Research**
- [ ] Study Bandit and Pysa implementation approaches
- [ ] Research OWASP Top 10 mapping to static analysis rules
- [ ] Investigate data flow analysis techniques
- [ ] Document security rule prioritization methodology

**Stage 7: Runtime Profiling Integration Research**
- [ ] Explore cProfile, memory_profiler, and timeit integration
- [ ] Research lightweight profiling techniques for CI/CD
- [ ] Investigate correlation methods between static and runtime analysis
- [ ] Design profiling data collection framework

**Stage 8: MCP Integration Architecture**
- [ ] Research Model Control Plane APIs and protocols
- [ ] Design feedback loop architecture for AI platforms
- [ ] Create communication protocol specifications
- [ ] Document integration patterns for popular AI orchestration tools

**Stage 9: Server Architecture Design**
- [ ] Design scalable server architecture (FastAPI + PostgreSQL)
- [ ] Plan database schema for scan results and historical data
- [ ] Design API endpoints and authentication mechanisms
- [ ] Create deployment strategies for Cloud/On-Prem/Air-Gapped

**Stage 10: IDE Integration Strategy**
- [ ] Research VS Code extension development
- [ ] Investigate JetBrains plugin architecture
- [ ] Design Language Server Protocol implementation
- [ ] Plan real-time analysis integration approach

### Phase 2: Core Engine Development (Stages 11-20)

**Stage 11: Basic AST Engine Implementation**
- [ ] Implement core AST parsing and traversal engine
- [ ] Create visitor pattern framework for rule execution
- [ ] Develop standardized Issue reporting class
- [ ] Add basic error handling and logging

**Stage 12: Rule Engine Framework**
- [ ] Design pluggable rule architecture
- [ ] Implement rule registration and discovery system
- [ ] Create rule configuration and severity management
- [ ] Add rule execution pipeline with performance monitoring

**Stage 13: First Code Quality Rules**
- [ ] Implement unused variable detection
- [ ] Add cyclomatic complexity analysis
- [ ] Create function length and parameter count rules
- [ ] Add basic naming convention checks

**Stage 14: Initial Green Software Rules**
- [ ] Implement inefficient string concatenation detection
- [ ] Add list comprehension optimization suggestions
- [ ] Create generator vs list usage recommendations
- [ ] Add basic loop optimization rules

**Stage 15: First AI-Specific Rules**
- [ ] Implement redundant import detection
- [ ] Add verbose code pattern identification
- [ ] Create duplicate function detection within classes
- [ ] Add AI-generated boilerplate identification

**Stage 16: CLI Interface Development**
- [ ] Create comprehensive command-line interface
- [ ] Add file and directory scanning capabilities
- [ ] Implement output formatting options (JSON, text, XML)
- [ ] Add configuration file support

**Stage 17: JSON Report Structure**
- [ ] Design comprehensive JSON report schema
- [ ] Implement structured output with metadata
- [ ] Add severity levels and impact metrics
- [ ] Create report validation and schema documentation

**Stage 18: Basic Testing Framework**
- [ ] Set up pytest testing infrastructure
- [ ] Create test files with known issues for validation
- [ ] Implement rule-specific unit tests
- [ ] Add integration tests for CLI functionality

**Stage 19: Configuration Management**
- [ ] Implement YAML/JSON configuration file support
- [ ] Add rule enabling/disabling mechanisms
- [ ] Create severity threshold configuration
- [ ] Add custom rule parameter support

**Stage 20: Performance Optimization**
- [ ] Profile AST parsing and rule execution performance
- [ ] Implement caching mechanisms for repeated scans
- [ ] Add parallel processing for multiple files
- [ ] Optimize memory usage for large codebases

### Phase 3: Security & Dependencies (Stages 21-30)

**Stage 21: Security Rule Framework**
- [ ] Implement security-focused AST analysis patterns
- [ ] Create vulnerability severity classification system
- [ ] Add CWE (Common Weakness Enumeration) mapping
- [ ] Implement basic data flow analysis

**Stage 22: Core Security Rules**
- [ ] Add SQL injection vulnerability detection
- [ ] Implement hardcoded secrets detection
- [ ] Create insecure file handling rules
- [ ] Add dangerous function usage detection (exec, eval)

**Stage 23: Dependency Vulnerability Scanner**
- [ ] Integrate with OSV.dev vulnerability database
- [ ] Implement requirements.txt and pyproject.toml parsing
- [ ] Add CVE severity mapping and reporting
- [ ] Create dependency update recommendations

**Stage 24: Enhanced Green Software Rules**
- [ ] Add memory-efficient data structure recommendations
- [ ] Implement file I/O optimization suggestions
- [ ] Create database query efficiency rules
- [ ] Add cloud resource usage optimization patterns

**Stage 25: Advanced AI Code Analysis**
- [ ] Implement tensor operation efficiency analysis (PyTorch/TensorFlow)
- [ ] Add model loading optimization detection
- [ ] Create data pipeline efficiency rules
- [ ] Add GPU utilization pattern analysis

**Stage 26: Secrets Detection Enhancement**
- [ ] Implement entropy-based secret detection
- [ ] Add API key pattern recognition
- [ ] Create credential scanning with context awareness
- [ ] Add false positive reduction mechanisms

**Stage 27: Code Complexity Analysis**
- [ ] Implement advanced complexity metrics (Halstead, maintainability index)
- [ ] Add cognitive complexity analysis
- [ ] Create technical debt estimation
- [ ] Add refactoring priority suggestions

**Stage 28: Pre-commit Hook Integration**
- [ ] Create pre-commit framework integration
- [ ] Implement selective scanning for changed files
- [ ] Add commit blocking based on severity thresholds
- [ ] Create developer-friendly error messages

**Stage 29: Git Integration**
- [ ] Implement Git blame integration for issue attribution
- [ ] Add commit history analysis for recurring patterns
- [ ] Create branch-specific configuration support
- [ ] Add pull request comment automation

**Stage 30: Error Handling & Resilience**
- [ ] Implement comprehensive error handling and recovery
- [ ] Add graceful degradation for partial scan failures
- [ ] Create detailed logging and debugging capabilities
- [ ] Add scan result integrity validation

### Phase 4: Runtime Analysis & Server (Stages 31-40)

**Stage 31: Runtime Profiling Integration**
- [ ] Implement cProfile integration for CPU analysis
- [ ] Add memory_profiler for memory usage tracking
- [ ] Create execution time measurement framework
- [ ] Add resource consumption correlation with static analysis

**Stage 32: Carbon Footprint Measurement**
- [ ] Integrate CodeCarbon for runtime emissions tracking
- [ ] Implement static-to-runtime carbon impact correlation
- [ ] Add cloud provider carbon intensity data integration
- [ ] Create carbon savings estimation algorithms

**Stage 33: Server Foundation**
- [ ] Implement FastAPI server with PostgreSQL backend
- [ ] Create database schema for scan results and metadata
- [ ] Add authentication and authorization framework
- [ ] Implement basic API endpoints for data ingestion

**Stage 34: Results Storage & Retrieval**
- [ ] Implement scan result storage with indexing
- [ ] Add historical data management and archiving
- [ ] Create efficient querying mechanisms
- [ ] Add data migration and backup strategies

**Stage 35: Dashboard Backend APIs**
- [ ] Create REST APIs for dashboard data
- [ ] Implement aggregation and analytics endpoints
- [ ] Add filtering and search capabilities
- [ ] Create trend analysis and reporting APIs

**Stage 36: MCP Feedback Implementation**
- [ ] Implement structured feedback formatting for AI platforms
- [ ] Add webhook system for real-time feedback delivery
- [ ] Create feedback effectiveness tracking
- [ ] Add integration with popular AI orchestration platforms

**Stage 37: Server-Client Communication**
- [ ] Implement secure client-server communication protocol
- [ ] Add result synchronization mechanisms
- [ ] Create offline mode support with later sync
- [ ] Add conflict resolution for concurrent scans

**Stage 38: Basic Web Dashboard**
- [ ] Create React-based web dashboard
- [ ] Implement project overview and scan history views
- [ ] Add issue browsing and filtering capabilities
- [ ] Create basic reporting and export features

**Stage 39: Multi-tenant Architecture**
- [ ] Implement organization and project isolation
- [ ] Add user management and role-based access control
- [ ] Create resource usage tracking and limits
- [ ] Add tenant-specific configuration management

**Stage 40: Server Deployment Options**
- [ ] Create Docker containerization for easy deployment
- [ ] Add Kubernetes deployment configurations
- [ ] Implement air-gapped deployment support
- [ ] Create cloud provider specific deployment guides

### Phase 5: Advanced Features & Ecosystem (Stages 41-50)

**Stage 41: Automated Fix Engine**
- [ ] Implement automatic code fix generation
- [ ] Add safe transformation verification
- [ ] Create fix preview and approval mechanisms
- [ ] Add rollback capabilities for automated changes

**Stage 42: AI-Powered Refactoring**
- [ ] Integrate LLM for complex refactoring suggestions
- [ ] Implement context-aware code improvement recommendations
- [ ] Add refactoring impact analysis
- [ ] Create human-in-the-loop approval workflows

**Stage 43: IDE Extension Development**
- [ ] Create VS Code extension with real-time analysis
- [ ] Implement JetBrains plugin for IntelliJ/PyCharm
- [ ] Add Language Server Protocol support
- [ ] Create inline fix suggestions and quick actions

**Stage 44: Infrastructure as Code Analysis**
- [ ] Add Terraform configuration analysis
- [ ] Implement CloudFormation template scanning
- [ ] Create Kubernetes manifest security and efficiency rules
- [ ] Add Docker image optimization suggestions

**Stage 45: Container Security & Optimization**
- [ ] Implement Dockerfile best practice analysis
- [ ] Add container image vulnerability scanning
- [ ] Create container resource optimization suggestions
- [ ] Add multi-stage build efficiency analysis

**Stage 46: Multi-Language Support**
- [ ] Add JavaScript/TypeScript analysis capabilities
- [ ] Implement Java basic analysis rules
- [ ] Create language-agnostic rule framework
- [ ] Add support for additional popular languages

**Stage 47: Enterprise Features**
- [ ] Implement SSO integration (SAML, OAuth2, LDAP)
- [ ] Add comprehensive audit logging
- [ ] Create compliance reporting (SOC2, ISO27001)
- [ ] Add advanced policy management and enforcement

**Stage 48: Custom Rule Development Platform**
- [ ] Create web-based rule development interface
- [ ] Implement rule testing and validation framework
- [ ] Add rule sharing and marketplace capabilities
- [ ] Create SDK for external rule development

**Stage 49: Advanced Analytics & ML**
- [ ] Implement machine learning for pattern detection
- [ ] Add predictive analysis for code quality trends
- [ ] Create anomaly detection for security and performance
- [ ] Add intelligent prioritization of issues

**Stage 50: Community & Ecosystem**
- [ ] Launch open-source community platform
- [ ] Create plugin marketplace and ecosystem
- [ ] Implement community rule sharing
- [ ] Add integration with major development platforms and tools

## 🚀 Quick Start

### Installation

```bash
# Install from PyPI (coming soon)
pip install ecoguard-ai

# Or install from source
git clone https://github.com/ecoguard-ai/ecoguard-ai.git
cd ecoguard-ai
pip install -e .
```

### Basic Usage

```bash
# Analyze a Python file
ecoguard analyze myfile.py

# Analyze a directory with JSON output
ecoguard analyze src/ --format json --output results.json

# Get help
ecoguard --help
```

## 🛠️ Development Setup

### Quick Setup

```bash
# Clone the repository
git clone https://github.com/ecoguard-ai/ecoguard-ai.git
cd ecoguard-ai

# Automated setup (recommended)
python scripts/setup_dev.py

# Or manual setup
make dev-install
```

### Development Commands

```bash
# Run all tests
make test

# Run tests across all Python versions
make test-all

# Format code
make format

# Run linting checks
make lint

# Type checking
make type-check

# Security scans
make security

# Run EcoGuard AI on itself
make self-analyze

# Run all CI checks locally
make ci

# Clean build artifacts
make clean
```

### Using Tox

```bash
# Run tests across all Python versions
tox

# Run specific environment
tox -e lint      # Linting
tox -e type      # Type checking
tox -e security  # Security scans
tox -e format    # Code formatting
```

### Pre-commit Hooks

EcoGuard AI uses pre-commit hooks to ensure code quality:

```bash
# Install hooks (done automatically with dev setup)
pre-commit install

# Run hooks manually
pre-commit run --all-files
```

### Development Dependencies

- **Code Quality**: black, isort, flake8, ruff, mypy
- **Testing**: pytest, pytest-cov, pytest-xdist, pytest-mock
- **Security**: bandit, safety, pip-audit, detect-secrets, semgrep
- **Documentation**: sphinx, sphinx-rtd-theme, myst-parser
- **Automation**: pre-commit, tox, twine

### Project Structure

```
ecoguard-ai/
├── src/ecoguard_ai/         # Main package
│   ├── cli/                 # Command-line interface
│   ├── core/                # Core analysis engine
│   ├── analyzers/           # Specific analyzers
│   └── utils/               # Utilities
├── tests/                   # Test suite
├── examples/                # Usage examples
├── scripts/                 # Development scripts
├── .github/workflows/       # CI/CD pipelines
└── docs/                    # Documentation
```

## 📦 Publishing & Releases

EcoGuard AI uses automated CI/CD for secure publishing to PyPI with trusted publishing.

### Version Management

```bash
# Check current version
./scripts/manage-version.sh current

# Bump version (patch/minor/major)
./scripts/manage-version.sh bump patch

# Set specific version
./scripts/manage-version.sh set 0.2.0

# Create full release (recommended)
./scripts/manage-version.sh release 0.2.0
```

### Automated Publishing

- **Test PyPI**: Automatic publishing on `main` branch pushes
- **Production PyPI**: Automatic publishing on version tags (`v*`)
- **Trusted Publishing**: Secure, token-free publishing via OIDC

### Manual Release Workflow

Use GitHub Actions for controlled releases:

1. Go to **Actions** → **Release workflow**
2. Click **Run workflow**
3. Enter version number (e.g., `0.2.0`)
4. Optionally mark as pre-release

This will:
- Update version in all files
- Run full test suite
- Build and verify package
- Create git tag and GitHub release
- Publish to PyPI automatically

### Setup for Maintainers

See detailed setup instructions in:
- [`docs/PUBLISHING.md`](docs/PUBLISHING.md) - Complete publishing guide
- [`docs/PYPI_SETUP.md`](docs/PYPI_SETUP.md) - PyPI trusted publisher configuration

## 🤝 Contributing

We welcome contributions from the community! Please read our [Contributing Guidelines](CONTRIBUTING.md) for details on how to get started.

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

- CodeCarbon project for carbon footprint measurement inspiration
- Bandit project for security analysis patterns
- The broader green software and sustainable computing community

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**EcoGuard AI** - Building a sustainable future, one line of code at a time 🌱