7 papers
From Code Review to Code Critique: Intent, Drift, and Spotlight for AI-Generated Diffs at Scale
Chandra Maddila, Mashrur Rashik, Euna Mehnaz Khan +4
AI coding agents are generating code at volumes that exceed the capacity of traditional peer review. At the same time, existing AI code review tools over-index on low-value suggest…
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…
Wink: Recovering from Misbehaviors in Coding Agents
Rahul Nanda, Chandra Maddila, Smriti Jha +3
Autonomous coding agents, powered by large language models (LLMs), are increasingly being adopted in the software industry to automate complex engineering tasks. However, these age…
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…
Improving Code Reviewer Recommendation: Accuracy, Latency, Workload, and Bystanders
Peter C. Rigby, Seth Rogers, Sadruddin Saleem +5
The code review team at Meta is continuously improving the code review process. To evaluate the new recommenders, we conduct three A/B tests which are a type of randomized controll…