Your First Prediction
Understanding how Autonoma predicts and prevents issues before they impact your users
How Predictions Work
Autonoma continuously analyzes your application using machine learning models trained on your codebase, runtime behavior, and historical patterns. When it detects conditions that historically lead to issues, it generates a prediction with recommended actions.
1. Analyze
Continuous monitoring of code, performance, and system metrics
2. Predict
ML models identify patterns that lead to issues
3. Prevent
Take action before issues impact users
Example: Memory Leak Prediction
Memory Leak Detected in UserService
Predicted Impact
- •Memory Usage: Will increase from 2.1GB to 4.8GB in 2 hours
- •Response Time: Will degrade from 150ms to 800ms
- •Users Affected: Approximately 15,000 active users
- •Revenue Impact: Estimated $12,500 in lost transactions
Root Cause Analysis
Event listeners in UserService.js:142 are not being properly removed when components unmount.
// Problem code
componentDidMount() {
window.addEventListener('resize', this.handleResize);
// Missing cleanup in componentWillUnmount
}Recommended Fix
// Fixed code
componentDidMount() {
window.addEventListener('resize', this.handleResize);
}
componentWillUnmount() {
window.removeEventListener('resize', this.handleResize);
}Additional Context
Historical Data
Similar pattern detected 3 times in the past month. Previous fixes reduced memory usage by 65%.
Related Issues
2 similar patterns found in AuthService.js and ProfileManager.js
Types of Predictions
Performance Issues
- • Memory leaks and resource exhaustion
- • CPU spikes and processing bottlenecks
- • Database query performance degradation
- • Network latency and timeout issues
Security Vulnerabilities
- • Dependency vulnerabilities
- • SQL injection risks
- • XSS and CSRF vulnerabilities
- • Authentication weaknesses
Stability Issues
- • Null pointer exceptions
- • Race conditions
- • Deadlocks and thread issues
- • Unhandled promise rejections
Scalability Concerns
- • Connection pool exhaustion
- • Rate limiting threshold breaches
- • Cache invalidation storms
- • Load balancing inefficiencies
Understanding Confidence Scores
Autonoma assigns confidence scores to predictions based on multiple factors:
Very High Confidence
Clear pattern match with extensive historical data. Auto-fix recommended.
High Confidence
Strong indicators with good historical correlation. Review recommended fixes.
Medium Confidence
Potential issue detected. Manual investigation recommended.
Low Confidence
Anomaly detected but uncertain. Monitor for additional signals.
Acting on Predictions
Automatic Remediation
For high-confidence predictions, enable auto-fix to resolve issues without manual intervention.
Manual Review
Review suggested fixes, understand the impact, and apply changes through your normal workflow.
Schedule Fixes
Schedule remediation during maintenance windows or low-traffic periods.
Pro Tip
Start with manual review for your first few predictions to build confidence in Autonoma's recommendations. As you see the accuracy, gradually enable auto-fix for specific categories of issues.
Ready to Enable Auto-Fix?
Let Autonoma automatically resolve issues before they impact your users