activity
20232026
most citedConfiguration Validation with Large Language Models

3 citations · 3 across the 5 of their papers we have counts for

collaborators

7 papers

cs.SE2026

ProgramBench: Can Language Models Rebuild Programs From Scratch?

John Yang, Kilian Lieret, Jeffrey Ma +9

Turning ideas into full software projects from scratch has become a popular use case for language models. Agents are being deployed to seed, maintain, and grow codebases over exten…

cs.CV2025

Finding Needles in Images: Can Multimodal LLMs Locate Fine Details?

Parth Thakkar, Ankush Agarwal, Prasad Kasu +2

While Multi-modal Large Language Models (MLLMs) have shown impressive capabilities in document understanding tasks, their ability to locate and reason about fine-grained details wi…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2024

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…

cs.SE2024

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…

cs.SE2023★ 3 cited

Configuration Validation with Large Language Models

Xinyu Lian, Yinfang Chen, Runxiang Cheng +4

Misconfigurations are major causes of software failures. Existing practices rely on developer-written rules or test cases to validate configurations, which are expensive. Machine l…