Nachi Nagappan

Software development in the era of AI is fraught with risk, especially in rapidly evolving large enterprise software organizations. In this talk Rui and Nachi share the tools Meta has implemented to mitigate risk. Specifically, Meta has developed, deployed, and enforced Diff Risk Score (DRS) and other code health metrics to tackle production risk. Equipped with a model that predicts if a code change might cause a product customer disruption, Meta developers can build features and workflows to improve almost every aspect of writing and pushing code. Today, DRS powers many risk-aware features that optimize product quality, developer productivity, and computational capacity efficiency. Notably, DRS has helped us eliminate major code freezes, letting developers ship code when they historically could not with minimal impact to customer experience and the business.

Topics and outline

• Meta’s tools that identify if a diff is developed by Gen AI, human, or human AI assisted
• Which chunks of the diff is AI or humans in their PR tools
• When diff lands, it does DRS then things will be triggered? Is it enforced across all of Meta today? What are the results/ROI?
• What are the DRS use cases?
• How does DRS reviewer work? How does code centrality graph/dashboard help with that? When do you land a diff without review when it's low risk?
• Risk at Meta: automatic rollback plans
• Test coverage based DRS results: Give more time to run more tests if its high risk
• For high risk diffs, how does Meta use AI models with automated code review to help reviewers?