3 papers
cs.LG2026
Reducing Hallucination in Enterprise AI Workflows via Hybrid Utility Minimum Bayes Risk (HUMBR)
Chenhao Fang, Jordi Mola, Mark Harman +10
Although LLMs drive automation, it is critical to ensure immense consideration for high-stakes enterprise workflows such as those involving legal matters, risk management, and priv…
cs.SE2026
Just-in-Time Catching Test Generation at Meta
Matthew Becker, Yifei Chen, Nicholas Cochran +12
We report on Just-in-Time catching test generation at Meta, designed to prevent bugs in large scale backend systems of hundreds of millions of line of code. Unlike traditional hard…
cs.SE2025
Mutation-Guided LLM-based Test Generation at Meta
Christopher Foster, Abhishek Gulati, Mark Harman +5
This paper describes Meta's ACH system for mutation-guided LLM-based test generation. ACH generates relatively few mutants (aka simulated faults), compared to traditional mutation…