Rui Abreu

Software Research Engineer · META

Rui Abreu is a Research Software Engineer at Meta. He holds a Ph.D. in Computer Science - Software Engineering from the Delft University of Technology, The Netherlands, and a M.Sc. in Computer and Systems Engineering from the University of Minho, Portugal. His research revolves around software quality, with emphasis in automating the testing and debugging phases of the software development life-cycle as well as self-adaptation. He has extensive expertise in both static and dynamic analysis algorithms for improving software quality. He is the recipient of 5 Best Paper Awards, and his work has attracted considerable attention.

PRESENTATIONS

Diff Risk Score: AI-driven risk-aware software development

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?

AI-Powered Code Refactorings at Scale

Maintaining large-scale distributed systems poses significant challenges due to their complexity, scale, and the risks of live changes. This talk presents a case study of a system which processes vast volumes of items in real time for billions of users. Over nine months, this system underwent a live architectural refactoring to improve maintainability, developer efficiency, and reliability. Key strategies included staged rollouts, automated testing, and impact validation, resulting in a 42% boost in developer efficiency, 87% reliability improvement, and notable gains in system performance and resource savings.

Looking forward, in this talk, we will explore the growing role of AI-driven refactoring techniques in accelerating development, enhancing reliability, and optimising performance in complex systems. This talk offers an overview of our current efforts, practical insights, and future directors for code maintenance and refactoring empowered by AI.