This document discusses self-healing automation as a solution to productivity challenges in test engineering. Self-healing automation uses application health checks, monitoring, alerts and notifications to automatically identify issues, restart failed components and services, and notify teams via email with logs. This approach can significantly reduce manual debugging time and improve uptime by restarting components without human intervention. It provides visibility into dependencies and points of failure. Teams see benefits like consistent test results, improved productivity, and more time to work on higher priority problems.

1. 1
Self Healing
Bringing Intelligence into Automation
Mohan Babu Nellore, Director of Engineering,
Visa
Arup Datta, Principal Engineer, Swiggy

2. 2
• Slide for legal disclaimer

3. 3
Test Engineering…
Productivity Challenges?
Quick list…

4. 4
Employee
Satisfactio
n
Troubleshootin
g
E2E
Integration
Environment
Effective
Communicatio
n Tools
Automation
Tools Failed Tests
Analysis
KPI
Metrics/Data
Requirement
s

5. 5
Environments!
Any Challenges?
Small story…
Dev/QA
Regression
Pre-Prod

6. 6
How do we solve?
Self Healing Automation
What is it?

7. 7
Application
Health
Check• Logs
• Monitoring
• Alerts
• Email Notification
System
PING
Database
Queues
• Listeners
• CRUD Operations
• Messaging Queues
Stages in the workflow:
1. Ping test
2. Application health-check
3. Listeners health-check
4. E2E flow with database-
check
5. Mutual API response check
6. Log collection
7. Mail Notification

8. 8
Self-healing Automation
• Dependency graph
• Health-check APIs.
• APIs to start/stop/restart
services & applications

9. 9
Functionalities
Identify dependencies – producers and consumers.
Ensure server and application health monitoring/alerts are set
up.
Ensure all the dependent components are up and running.
Restart services applications automatically, if not working as
desired.
If not, send out an email notification with logs attached.

10. 10
Sample Effort Savings Calculations

11. 11
Key pointers
Use Human-centered design approach as to solve the
problem
Identify multiple metrics to assess training and monitoring
When possible, directly examine your raw data
Understand the limitations of your dataset and model
Test, test and test.
Continue to monitor and update the system after deployment

12. 12
Impact on the team
• Improved Up-time, no manual intervention/debugging at large.
• Know the exact point of failure for downtime.
• Consistent test results without false positives.
• Drastic productivity boost by avoiding manual debugging.
• Extendibility for reliability/failover testing -> graceful
handling.
• We have time for solving bigger problems!

13. 13
Thank You!
@mohanbn
Q & A