Showing cs.SEShow all
2 papers · 1 filter
cs.SE2026
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…
cs.SE2025
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…