# A Software Engineering Perspective on Testing Large Language Models: Research, Practice, Tools and Benchmarks

arXiv: https://arxiv.org/abs/2406.08216 · HTML: https://mlsanity.com/paper/2406.08216/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2024.

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

- [Challenges in Testing Large Language Model Based Software: A Faceted Taxonomy](https://mlsanity.com/paper/2503.00481/) (2025)
- [Addressing Data Leakage in HumanEval Using Combinatorial Test Design](https://mlsanity.com/paper/2412.01526/) (2024)
- [Adaptive Testing for LLM-Based Applications: A Diversity-based Approach](https://mlsanity.com/paper/2501.13480/) (2025)
- [LeCov: Multi-level Testing Criteria for Large Language Models](https://mlsanity.com/paper/2408.10474/) (2024)
- [Quality Assurance for LLM-RAG Systems: Empirical Insights from Tourism Application Testing](https://mlsanity.com/paper/2502.05782/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) are rapidly becoming ubiquitous both as stand-alone tools and as components of current and future software systems. To enable usage of LLMs in the high-stake or safety-critical systems of 2030, they need to undergo rigorous testing. Software Engineering (SE) research on testing Machine Learning (ML) components and ML-based systems has systematically explored many topics such as test input generation and robustness. We believe knowledge about tools, benchmarks, research and practitioner views related to LLM testing needs to be similarly organized. To this end, we present a taxonomy of LLM testing topics and conduct preliminary studies of state of the art and practice approaches to research, open-source tools and benchmarks for LLM testing, mapping results onto this taxonomy. Our goal is to identify gaps requiring more research and engineering effort and inspire a clearer communication between LLM practitioners and the SE research community.