# Observation-based unit test generation at Meta

arXiv: https://arxiv.org/abs/2402.06111 · HTML: https://mlsanity.com/paper/2402.06111/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2024.

## Built on by (most cited first, 5 of 5)

- [Mutation-Guided LLM-based Test Generation at Meta](https://mlsanity.com/paper/2501.12862/) (2025)
- [AI-Driven Tools in Modern Software Quality Assurance: An Assessment of Benefits, Challenges, and Future Directions](https://mlsanity.com/paper/2506.16586/) (2025)
- [Harden and Catch for Just-in-Time Assured LLM-Based Software Testing: Open Research Challenges](https://mlsanity.com/paper/2504.16472/) (2025)
- [LLM Assisted Coding with Metamorphic Specification Mutation Agent](https://mlsanity.com/paper/2511.18249/) (2025)
- [Just-in-Time Catching Test Generation at Meta](https://mlsanity.com/paper/2601.22832/) (2026)

## Abstract (arXiv)

TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution. We describe the development and deployment of TestGen at Meta. In particular, we focus on the scalability challenges overcome during development in order to deploy observation-based test carving at scale in industry. So far, TestGen has landed 518 tests into production, which have been executed 9,617,349 times in continuous integration, finding 5,702 faults. Meta is currently in the process of more widespread deployment. Our evaluation reveals that, when carving its observations from 4,361 reliable end-to-end tests, TestGen was able to generate tests for at least 86% of the classes covered by end-to-end tests. Testing on 16 Kotlin Instagram app-launch-blocking tasks demonstrated that the TestGen tests would have trapped 13 of these before they became launch blocking.