5 papers
A Preliminary Study on Explaining Risk of Code Changes using LLM-Based Prediction Models
Yalin Liu, Kosay Jabre, Rui Abreu +10
Predictions by machine learning (ML) and artificial intelligence (AI) models are often received skeptically unless they are paired with intelligible explanations. In the context of…
REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage
Smriti Jha, Matteo Paltenghi, Chandra Maddila +3
Production deployment of AI coding agents requires fast, reproducible evaluation signals. Existing industrial practices trade off speed and fidelity: online A/B testing takes weeks…
Agentic Program Repair from Test Failures at Scale: A Neuro-symbolic approach with static analysis and test execution feedback
Chandra Maddila, Adam Tait, Claire Chang +21
Aim: With the advent of LLMs, sophisticated agentic program repair has become viable at large organizations with large codebases. In this work, we develop an Engineering Agent that…
AI-Assisted Fixes to Code Review Comments at Scale
Chandra Maddila, Negar Ghorbani, James Saindon +7
Aim. There are 10s of thousands of code review comments each week at Meta. We developed Metamate for Code Review (MetaMateCR) that provides AI-assisted fixes for reviewer comments…
Moving Faster and Reducing Risk: Using LLMs in Release Deployment
Rui Abreu, Vijayaraghavan Murali, Peter C Rigby +7
Release engineering has traditionally focused on continuously delivering features and bug fixes to users, but at a certain scale, it becomes impossible for a release engineering te…