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100 Million Lines of Code, Millions of $ Saved, 0 Wasted Builds; AI-Powered CI @ Atlassian Scale
Originally aired:
About the Session
Managing a monorepo with 100 million lines of code presents immense challenges in infrastructure costs, scalability, and build efficiency. At Atlassian, our high-volume CI/CD pipeline executes hundreds of thousands of tests daily, leading to significant redundancy and inefficiencies in the development workflow.
By leveraging AI and machine learning, we have transformed our Continuous Integration (CI) pipeline to dramatically optimize build times and resource consumption. Through predictive test selection, powered by static code analysis and ML models, we skip 50-95% of redundant tests per change—without compromising quality. This innovation has resulted in thousands of engineering hours saved and annual infrastructure cost reductions of over $4 million.
Beyond test selection, AI-powered tooling has also reduced manual effort in code migrations by 50%, allowing teams to focus on innovation rather than repetitive maintenance tasks. This session will provide an inside look at how AI/ML advancements in developer tooling can streamline CI/CD processes, cut costs, and enhance engineering productivity at an unprecedented scale.
Key Takeaways
- AI-Powered CI/CD Optimization – How Atlassian scaled Continuous Integration across a massive monorepo with AI/ML-driven efficiency.
- Predictive Test Selection – Learn how machine learning models analyze code changes to skip 50-95% of unnecessary tests.
- Cost & Resource Optimization – Discover how these innovations led to $4M+ annual savings in infrastructure costs.
- AI-Driven Code Migrations – See how AI-assisted tools reduced manual migration efforts by 50%, accelerating large-scale refactors.
- Scaling Developer Productivity – Insights into how AI and automation improve developer experience, efficiency, and CI/CD scalability.
Target Audience
- DevOps & CI/CD Engineers – Professionals managing large-scale build systems, looking to enhance efficiency and automation.
- Engineering Leaders & CTOs – Decision-makers seeking cost-saving AI innovations in software engineering at scale.
- Software Developers & Architects – Developers working in large monorepos who want to optimize build and test cycles.
- AI/ML Engineers – Experts exploring real-world applications of AI-driven automation in developer tooling.
100 Million Lines of Code, Millions of $ Saved, 0 Wasted Builds; AI-Powered CI @ Atlassian Scale
Originally aired:
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