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…
VisDocSketcher: Towards Scalable Visual Documentation with Agentic Systems
LuÃs F. Gomes, Xin Zhou, David Lo +1
Visual documentation is an effective tool for reducing the cognitive barrier developers face when understanding unfamiliar code, enabling more intuitive comprehension. Compared to…
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…