AI DevAssistant
Smart CI/CD optimization and failure prediction
CI/CD Pipeline Optimization
AI analyzes your CI/CD pipelines and automatically optimizes build times, resource usage, and deployment efficiency
Build Failure Prediction
Predict build failures before they occur using machine learning analysis of code changes and historical patterns
Performance Enhancement
Intelligent suggestions for improving pipeline performance, reducing build times, and optimizing resource allocation
Automated DevOps Tasks
Automate common development tasks using intelligent pattern recognition and workflow optimization
AI DevAssistant Dashboard
47%
Build Time Reduction
92%
Prediction Accuracy
234
Pipelines Optimized
89%
Success Rate
AI-powered CI/CD optimization and intelligent build prediction
Installation
Deploy AI DevAssistant to start optimizing your CI/CD pipelines and predicting build failures.
System Requirements
- Node.js 18 or higher
- Python 3.9+ (for AI analysis components)
- Docker 20.0+ (for containerized builds)
- Git 2.20+ with repository access
- Access to CI/CD platforms (Jenkins, GitHub Actions, GitLab CI, etc.)
Install via Package Manager
Installationbash
# Install via npm
npm install -g @augment/dev-assistant
# Install via pip for Python components
pip install augment-dev-assistant
# Install from source
git clone https://github.com/augment-ai/dev-assistant
cd dev-assistant
npm install && npm run build
# Install CI/CD plugins
dev-assistant plugins install jenkins github-actions gitlab-ci
# Verify installation
dev-assistant --versionCI/CD Platform Integration
Configure integration with your CI/CD platforms and version control systems:
Platform Integrationbash
# Set Augment API key
export AUGMENT_API_KEY=your_api_key_here
# Configure GitHub integration
export GITHUB_TOKEN=your_github_token
dev-assistant connect github --org your-org
# Configure Jenkins integration
export JENKINS_URL=https://jenkins.company.com
export JENKINS_TOKEN=your_jenkins_token
dev-assistant connect jenkins
# Configure GitLab integration
export GITLAB_TOKEN=your_gitlab_token
dev-assistant connect gitlab --group your-group
# Initialize dev assistant
dev-assistant init --scan-repositories
# Verify integrations
dev-assistant health-checkQuick Start
Start optimizing your development workflows and predicting build issues in minutes.
1. Analyze Current Pipelines
Pipeline Analysisbash
# Scan and analyze existing CI/CD pipelines
dev-assistant analyze --repositories all --timeframe 30d
# Analyze specific repository
dev-assistant analyze --repo your-org/your-repo --detailed
# Generate baseline performance metrics
dev-assistant baseline --pipelines all --metrics build-time,success-rate
# Identify optimization opportunities
dev-assistant opportunities --min-improvement 20% --focus build-time2. Enable Build Prediction
Build Prediction Setupbash
# Enable build failure prediction
dev-assistant predict enable --models failure,performance --confidence 0.8
# Configure prediction triggers
dev-assistant predict configure --on-commit --on-pr --on-merge
# Set up prediction notifications
dev-assistant notifications add slack --webhook "https://hooks.slack.com/..."
dev-assistant notifications add email --recipients "dev-team@company.com"
# Train prediction models
dev-assistant predict train --historical-data 90d --repositories all3. Optimize Pipelines
Pipeline Optimizationbash
# Start pipeline optimization
dev-assistant optimize --repositories all --auto-implement-safe
# Optimize specific aspects
dev-assistant optimize --focus build-cache,parallelization,resource-allocation
# Generate optimization report
dev-assistant report --type optimization --output optimization-report.html
# Monitor optimization results
dev-assistant monitor --live --metrics build-time,success-rate,resource-usageConfiguration
Configure AI DevAssistant to align with your development workflows and optimization goals.
Basic Configuration
.dev-assistant.yamlyaml
version: "1.0"
organization: "your-company"
environment: "production"
repositories:
scan_scope:
- "your-org/*"
- "platform/*"
exclude_patterns:
- "*-archive"
- "test-*"
languages: ["javascript", "python", "java", "go"]
ci_platforms:
github_actions:
enabled: true
workflows_path: ".github/workflows"
analyze_runs: true
jenkins:
enabled: true
url: "https://jenkins.company.com"
analyze_jobs: true
gitlab_ci:
enabled: true
analyze_pipelines: true
optimization_targets:
build_time_reduction: 40
success_rate_improvement: 10
resource_efficiency: 30
cost_reduction: 25
prediction_models:
build_failure:
enabled: true
confidence_threshold: 0.8
features: ["code_changes", "test_coverage", "dependencies", "author"]
performance_issues:
enabled: true
predict_slow_builds: true
threshold_minutes: 15
resource_consumption:
enabled: true
predict_resource_spikes: true
automation:
auto_optimize: true
safe_changes_only: true
require_approval:
- "pipeline_structure_changes"
- "security_related_changes"
rollback_on_failure: true
notification_channels: ["slack", "email"]
analytics:
collect_metrics: true
retention_days: 180
anonymize_data: true
share_insights: falseCI/CD Optimization
AI DevAssistant provides comprehensive CI/CD pipeline optimization across multiple dimensions.
Build Performance
- • Parallel execution optimization
- • Build cache strategy improvement
- • Dependency management optimization
- • Resource allocation tuning
Test Optimization
- • Test suite parallelization
- • Flaky test identification
- • Test selection optimization
- • Coverage analysis improvement
Deployment Efficiency
- • Deployment strategy optimization
- • Rollback mechanism improvement
- • Environment provisioning
- • Release automation
Resource Management
- • Runner/agent optimization
- • Queue management
- • Resource scheduling
- • Cost optimization
Environment Variables
Configure AI DevAssistant behavior using environment variables for different deployment scenarios.
| Variable | Description | Default |
|---|---|---|
| AUGMENT_API_KEY | Your Augment API key | Required |
| DEV_ASSISTANT_CONFIG | Path to configuration file | .dev-assistant.yaml |
| DEV_ASSISTANT_LOG_LEVEL | Logging level (debug/info/warn/error) | info |
| DEV_ASSISTANT_WORKERS | Number of analysis worker processes | 4 |
Basic Usage
Learn the fundamental development assistance patterns and CI/CD optimization workflows.
Development Commands
Development Assistancebash
# Analyze repository for optimization opportunities
dev-assistant analyze --repo your-org/repo --full-analysis
# Predict build outcome before committing
dev-assistant predict --changes staged --model failure,performance
# Optimize pipeline configuration
dev-assistant optimize --pipeline .github/workflows/ci.yml --improve build-time
# Monitor ongoing builds and predictions
dev-assistant monitor --live --show-predictionsCLI Commands Reference
Complete reference for all development assistance and CI/CD optimization commands.
optimize
Optimize CI/CD pipelines with AI-powered recommendations
dev-assistant optimize [options]
Options:
--repository <repo> Target repository (org/repo format)
--pipeline <file> Specific pipeline file to optimize
--focus <areas> Focus areas (build-time|success-rate|resource-usage)
--target <percentage> Target improvement percentage
--safe-only Apply only safe optimizations
--dry-run Preview optimizations without applying
--auto-implement Automatically implement approved changes
--rollback-plan Generate rollback instructions
--output <file> Save optimization reportpredict
Predict build outcomes and performance issues
dev-assistant predict [options]
Options:
--changes <scope> Scope of changes (staged|committed|branch)
--model <models> Prediction models (failure|performance|resource)
--confidence <level> Minimum confidence threshold
--repository <repo> Target repository
--branch <branch> Specific branch to analyze
--compare-with <ref> Compare against reference commit
--detailed Include detailed prediction analysis
--suggestions Include improvement suggestionsBest Practices
Development workflow best practices to maximize the effectiveness of AI-powered assistance.
CI/CD Optimization Strategy
- Start with baseline analysis to understand current performance
- Implement safe optimizations first to build confidence
- Use prediction models to prevent issues before they occur
- Monitor pipeline performance continuously after optimizations
- Gradually implement more aggressive optimizations based on results
- Maintain rollback plans for all automated changes
Build Prediction
Advanced machine learning models predict build failures and performance issues before they occur.
Prediction Models
Failure Prediction
Predict builds likely to fail based on code changes
- • Code change analysis
- • Test coverage impact
- • Historical failure patterns
Performance Prediction
Predict build time and resource usage
- • Build duration estimation
- • Resource consumption forecast
- • Queue time prediction
Quality Prediction
Predict code quality and test issues
- • Test flakiness detection
- • Code quality degradation
- • Coverage impact analysis
Prediction Configuration
Build Prediction Setupbash
# Configure failure prediction
dev-assistant predict config --model failure \
--features code-changes,test-coverage,dependencies \
--confidence-threshold 0.8
# Set up performance prediction
dev-assistant predict config --model performance \
--predict build-time,resource-usage \
--historical-window 30d
# Enable proactive notifications
dev-assistant predict notifications --on-high-risk \
--channels slack,email \
--include-suggestionsPerformance Optimization
Comprehensive performance optimization for builds, tests, and deployments.
Optimization Techniques
Performance Optimizationbash
# Optimize build caching strategy
dev-assistant optimize cache --analyze-dependencies \
--strategy layered-cache \
--cache-size optimal
# Optimize test execution
dev-assistant optimize tests --parallel-execution \
--smart-selection \
--flaky-test-handling skip
# Optimize resource allocation
dev-assistant optimize resources --auto-scale \
--resource-prediction \
--cost-efficiency highAPI Integration
Integrate AI DevAssistant into your development tools and CI/CD platforms.
REST API
API Usagebash
# Trigger pipeline analysis via API
curl -X POST https://api.augment.cfd/v1/devops/analyze \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"repository": "your-org/your-repo",
"branch": "main",
"analysis_type": "optimization",
"focus_areas": ["build_time", "success_rate"]
}'JavaScript SDK
JavaScript Integrationjavascript
const { DevAssistant } = require('@augment/dev-assistant-sdk');
// Initialize dev assistant
const assistant = new DevAssistant({
apiKey: process.env.AUGMENT_API_KEY
});
// Analyze pipeline performance
const analysis = await assistant.analyzePipeline({
repository: 'your-org/your-repo',
pipeline: '.github/workflows/ci.yml',
timeframe: '30d'
});
// Get build predictions
const prediction = await assistant.predictBuild({
repository: 'your-org/your-repo',
changes: 'staged',
models: ['failure', 'performance']
});
console.log(`Build failure probability: ${prediction.failure_probability}`);
// Optimize pipeline
const optimization = await assistant.optimizePipeline({
repository: 'your-org/your-repo',
targetImprovement: 30,
safeOnly: true
});API Reference
Complete API documentation for integrating development assistance into your applications.
Pipeline Analysis Endpoint
POST /v1/devops/analyze
Analyze CI/CD pipelines and generate optimization recommendations.
Request Body:
{
"repository": "your-org/your-repo",
"branch": "main",
"analysis_scope": {
"pipelines": [".github/workflows/ci.yml", ".github/workflows/deploy.yml"],
"timeframe": "30d",
"include_costs": true
},
"optimization_goals": {
"build_time_reduction": 40,
"success_rate_improvement": 10,
"cost_reduction": 25
},
"analysis_options": {
"include_predictions": true,
"generate_recommendations": true,
"safe_optimizations_only": false
}
}Response:
{
"analysis_id": "dev-analysis-123",
"status": "completed",
"repository": "your-org/your-repo",
"summary": {
"current_performance": {
"average_build_time": "12m 34s",
"success_rate": 87.3,
"monthly_cost": 1240.50
},
"optimization_potential": {
"build_time_reduction": 47,
"success_rate_improvement": 8.2,
"cost_savings": 312.75
}
},
"recommendations": [
{
"id": "opt-001",
"type": "caching_optimization",
"priority": "high",
"title": "Implement Docker layer caching",
"description": "Add Docker layer caching to reduce build times by 35%",
"estimated_impact": {
"build_time_reduction": "4m 20s",
"success_rate_change": 0,
"cost_impact": -89.50
},
"implementation": {
"difficulty": "low",
"estimated_time": "30 minutes",
"rollback_plan": "Remove cache configuration",
"files_to_modify": [".github/workflows/ci.yml"]
},
"code_changes": {
"file": ".github/workflows/ci.yml",
"additions": [
"- name: Cache Docker layers",
" uses: actions/cache@v3",
" with:",
" path: /tmp/.buildx-cache",
" key: buildx-cache"
]
}
}
],
"predictions": {
"failure_risk": {
"next_build": 0.12,
"confidence": 0.89,
"risk_factors": ["dependency_update", "test_coverage_decrease"]
},
"performance_forecast": {
"next_build_time": "11m 45s",
"confidence": 0.92,
"factors": ["code_change_size", "cache_effectiveness"]
}
}
}Troubleshooting
Common issues and solutions when implementing development assistance and CI/CD optimization.
Common Issues
Optimization Regressions
Applied optimizations causing build failures or performance issues
Solutions:
- Use rollback plans to quickly revert problematic changes
- Start with safe-only optimizations to build confidence
- Implement gradual rollout of optimizations
- Monitor key metrics closely after applying changes
Prediction Accuracy Issues
Build predictions not matching actual outcomes
Solutions:
- Increase historical data collection period for training
- Adjust confidence thresholds based on accuracy metrics
- Include more features in prediction models
- Retrain models with recent build data regularly
CI/CD Platform Integration
Issues connecting to or analyzing CI/CD platforms
Solutions:
- Verify API tokens and permissions for platform access
- Check network connectivity and firewall rules
- Ensure webhook configurations are properly set up
- Test integration with health-check command
DevOps Documentation Complete!
You now have comprehensive knowledge to implement AI DevAssistant in your development workflow. From CI/CD optimization to build failure prediction, you're equipped to enhance developer productivity with AI-powered assistance.
Ready to supercharge your development workflow? Start your free analysis today and discover how AI can optimize your CI/CD pipelines and prevent build failures.