# From Untestable to Testable: Metamorphic Testing in the Age of LLMs

arXiv: https://arxiv.org/abs/2603.24774 · HTML: https://mlsanity.com/paper/2603.24774/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 25 Mar 2026.

## Builds on (most cited first, 4 of 4)

- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [The Future of AI-Driven Software Engineering](https://mlsanity.com/paper/2406.07737/) (2024)
- [Metamorphic Testing of Large Language Models for Natural Language Processing](https://mlsanity.com/paper/2511.02108/) (2025)
- [LLMORPH: Automated Metamorphic Testing of Large Language Models](https://mlsanity.com/paper/2603.23611/) (2026)

## Abstract (arXiv)

This article discusses the challenges of testing software systems with increasingly integrated AI and LLM functionalities. LLMs are powerful but unreliable, and labeled ground truth for testing rarely scales. Metamorphic Testing solves this by turning relations among multiple test executions into executable test oracles.