Automated structural testing of LLM-based agents: methods, framework, and case studies
cs.SE, cs.AI
Submitted: 2026-01-25
Updated: 2026-10-18
Comments: 10 pages, 5 figures. Accepted for publication in the 2025 IEEE International Conference on BigData 2025 (main track). Copyright (c) 2025 IEEE. Source code for the introduced methods and framework available at https://github.com/awslabs/generative-ai-toolkit
Journal ref: Proceedings of the 2025 IEEE International Conference on Big Data (BigData), 2025, pp. 1847-1856
DOI: 10.1109/BigData66926.2025.11401679
Code: https://github.com/awslabs/generative-ai-toolkit
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future
- Universal and Transferable Adversarial Attacks on Aligned Language Models
- LLMs Get Lost In Multi-Turn Conversation
- A Survey on LLM-as-a-Judge
- One Token to Fool LLM-as-a-Judge
- Large Language Model based Multi-Agents: A Survey of Progress and Challenges
- Cognitive Mirage: A Review of Hallucinations in Large Language Models
- A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
- Hallucination is Inevitable: An Innate Limitation of Large Language Models
- A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models
- Why Do Multi-Agent LLM Systems Fail?
- Defining and Detecting the Defects of the Large Language Model-based Autonomous Agents
- Evil Geniuses: Delving into the Safety of LLM-based Agents
- Survey on Evaluation of LLM-based Agents
- TrustAgent: Towards Safe and Trustworthy LLM-based Agents
- Generative AI Toolkit -- a framework for increasing the quality of LLM-based applications over their whole life cycle
- Software Test Automation Maturity -- A Survey of the State of the Practice
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