TestGPT-Server: Automatically Testing Microservices with Large Language Models at ByteDance
Jue Wang, Shuxiang Chen, Yu Liu, Yuan Deng, Lei Zhang, Yuanchang Fu, Bo Liu · 2025
Despite the advantages of the microservice architecture, there is an urgent need for automated testing microservice APIs, ensuring that each API functions correctly and reliably without exhaustive manual effort. This paper introduces TestGPT-Server, a novel, fully automated testing system for microservice APIs developed and deployed at ByteDance, leveraging the capabilities of large language models (LLMs) to enhance test request generation and bug detection. TestGPT-Server utilizes an LLM-enhanced interface analyzer and a code analyzer to extract syntactic and semantic information of the API under test. Based on the extracted information, it employs a targeted request generator and a guided stochastic fuzzer to produce comprehensive test requests. After sending generated test requests to the API, an LLM-assisted response analyzer identifies potential service crashes, and an assertion miner automatically mines functional assertions to detect functional bugs. Deployed at ByteDance, the evaluation results demonstrate that TestGPT-Server is effective, achieving a 40.52% improvement over existing test requests and identifying 50 crash bugs and 2 functional bugs across 14,366 microservice APIs, with minimal manual effort.