Research · Artificial Intelligence · DevOps
Self-Healing CI/CD Pipelines Using AI
My Bachelor's Engineering research project, and one of the strongest demonstrations of my software engineering and AI capabilities. It explores how AI can make CI/CD pipelines autonomous by detecting failures, identifying root causes, and automatically recommending or executing recovery actions, reducing developer intervention and increasing deployment reliability.
Problem
Modern CI/CD pipelines regularly fail because of dependency conflicts, infrastructure failures, configuration mistakes, flaky tests, build failures, and deployment issues. These failures consume engineering time because developers have to manually inspect logs and work out the right fix.
Solution
Designed a self-healing pipeline framework capable of detecting failures automatically, collecting and analyzing build logs, identifying probable root causes, selecting an appropriate recovery strategy, validating that recovery worked, and returning the pipeline to a healthy state.
Tech stack
Key features
- Automated failure detection
- Intelligent log analysis
- Root cause classification
- AI-assisted recovery
- Rule-based remediation
- Recovery validation
- Modular architecture
Engineering highlights
Impact
Forms the core of my B.Eng. research work: a framework for reducing manual log inspection and mean-time-to-recovery in CI/CD pipelines through AI-assisted automation.