# Software Testing of Generative AI Systems: Challenges and Opportunities

arXiv: https://arxiv.org/abs/2309.03554 · HTML: https://mlsanity.com/paper/2309.03554/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2023.

## Built on by (most cited first, 7 of 7)

- [METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities](https://mlsanity.com/paper/2312.06056/) (2023)
- [MORTAR: Multi-turn Metamorphic Testing for LLM-based Dialogue Systems](https://mlsanity.com/paper/2412.15557/) (2024)
- [Benchmarking Generative AI Models for Deep Learning Test Input Generation](https://mlsanity.com/paper/2412.17652/) (2024)
- [On Simulation-Guided LLM-based Code Generation for Safe Autonomous Driving Software](https://mlsanity.com/paper/2504.02141/) (2025)
- [Automated structural testing of LLM-based agents: methods, framework, and case studies](https://mlsanity.com/paper/2601.18827/) (2026)
- [Automated Harmfulness Testing for Code Large Language Models](https://mlsanity.com/paper/2503.16740/) (2025)
- [Ethics Testing: Proactive Identification of Generative AI System Harms](https://mlsanity.com/paper/2604.22089/) (2026)

## Abstract (arXiv)

Software Testing is a well-established area in software engineering, encompassing various techniques and methodologies to ensure the quality and reliability of software systems. However, with the advent of generative artificial intelligence (GenAI) systems, new challenges arise in the testing domain. These systems, capable of generating novel and creative outputs, introduce unique complexities that require novel testing approaches. In this paper, I aim to explore the challenges posed by generative AI systems and discuss potential opportunities for future research in the field of testing. I will touch on the specific characteristics of GenAI systems that make traditional testing techniques inadequate or insufficient. By addressing these challenges and pursuing further research, we can enhance our understanding of how to safeguard GenAI and pave the way for improved quality assurance in this rapidly evolving domain.