Just-in-Time Catching Test Generation at Meta

TL;DR

This paper presents a novel Just-in-Time catching test generation approach at Meta that proactively detects bugs in large-scale backend systems before code deployment, significantly reducing false positives and preventing serious failures.

Contribution

The paper introduces code-change-aware test generation and assessors that effectively reduce false positives, demonstrating scalable bug prevention in industrial-scale systems.

Findings

Code-change-aware methods improve candidate catch generation 4x over traditional methods.

Rule-based and LLM-based assessors reduce human review load by 70%.

8 out of 41 reported catches were confirmed as true positives, preventing serious failures.

Abstract

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 hardening tests, which pass at generation time, catching tests are meant to fail, surfacing bugs before code lands. The primary challenge is to reduce development drag from false positive test failures. Analyzing 22,126 generated tests, we show code-change-aware methods improve candidate catch generation 4x over hardening tests and 20x over coincidentally failing tests. To address false positives, we use rule-based and LLM-based assessors. These assessors reduce human review load by 70%. Inferential statistical analysis showed that human-accepted code changes are assessed to have significantly more false positives, while human-rejected changes have significantly more true positives. We reported 41 candidate catches to engineers; 8 were confirmed to be true positives, 4 of which would have led to serious failures had they remained uncaught. Overall, our results show that Just-in-Time catching is scalable, industrially applicable, and that it prevents serious failures from reaching production.