9 citations · 11 across the 13 of their papers we have counts for
15 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…
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…
Automating Low-Risk Code Review at Meta: RADAR, Risk Calibration, and Review Efficiency
Chris Adams, Arjun Singh Banga, Parveen Bansal +28
AI-assisted coding tools have altered software production. At Meta, significant lines of code per human-landed diff grew by 105.9% year over year and per-developer diff volume rose…
AI-Generated Smells: An Analysis of Code and Architecture in LLM and Agent-Driven Development
Yuecai Zhu, Nikolaos Tsantalis, Peter C. Rigby
The promise of Large Language Models in automated software engineering is often measured by functional correctness, overlooking the critical issue of long term maintainability. Thi…
Risk-Aware Batch Testing for Performance Regression Detection
Ali Sayedsalehi, Peter C. Rigby, Gregory Mierzwinski
Performance regression testing is essential in large-scale continuous-integration (CI) systems, yet executing full performance suites for every commit is prohibitively expensive. P…
DRS-OSS: A Diff-Risk Scoring Tool for Continuous Integration Workflows
Ali Sayedsalehi, Peter C. Rigby, Audris Mockus
Software teams need change-risk scores that can guide continuous integration decisions such as review prioritization, test scheduling, and downstream validation before risky change…
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…
Code Improvement Practices at Meta
Audris Mockus, Peter C Rigby, Rui Abreu +20
The focus on rapid software delivery inevitably results in the accumulation of technical debt, which, in turn, affects quality and slows future development. Yet, companies with a l…
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…
AI-Assisted SQL Authoring at Industry Scale
Chandra Maddila, Negar Ghorbani, Kosay Jabre +8
SqlCompose brings generative AI into the data analytics domain. SQL is declarative, has formal table schemas, and is often written in a non-linear manner. We address each of these…
Multi-line AI-assisted Code Authoring
Omer Dunay, Daniel Cheng, Adam Tait +9
CodeCompose is an AI-assisted code authoring tool powered by large language models (LLMs) that provides inline suggestions to 10's of thousands of developers at Meta. In this paper…
Factoring Expertise, Workload, and Turnover into Code Review Recommendation
Fahimeh Hajari, Samaneh Malmir, Ehsan Mirsaeedi +1
Developer turnover is inevitable on software projects and leads to knowledge loss, a reduction in productivity, and an increase in defects. Mitigation strategies to deal with turno…
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…
AI-assisted Code Authoring at Scale: Fine-tuning, deploying, and mixed methods evaluation
Vijayaraghavan Murali, Chandra Maddila, Imad Ahmad +6
Generative LLMs have been shown to effectively power AI-based code authoring tools that can suggest entire statements or blocks of code during code authoring. In this paper we pres…