AI RESEARCH
LLM-Based Robustness Testing of Microservice Applications: An Empirical Study
arXiv CS.AI
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ArXi:2605.14202v1 Announce Type: cross Malformed, missing, or boundary-value inputs in microservice APIs can cascade across dependent services, threatening reliability. Robustness testing systematically exercises such inputs to expose server-side failures, but generating diverse, effective tests remains challenging. Large Language Models can generate such tests from API specifications; however, it is unknown whether different models and prompt strategies produce diverse failure sets or converge on the same failures.