10 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…
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…
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…