Large Language Models (LLMs) are increasingly used to support software testing tasks, yet there is little evidence of their effectiveness for REST API testing in industrial settings. To address this gap, we replicate our earlier work on LLM-based REST API test amplification within an industrial context at one of the largest logistics companies in Belgium. We apply LLM-based test amplification to six representative endpoints of a production microservice embedded in a large-scale, security-sensitive system, where there is in-depth complexity in authentication, stateful behavior, and organizational constraints. Our experience shows that LLM-based test amplification remains practically useful in industry by increasing coverage and revealing various observations and anomalies.

翻译：大型语言模型（LLM）正日益广泛地应用于支持软件测试任务，然而其在工业环境下针对REST API测试的有效性尚缺乏充分证据。为填补这一空白，我们在比利时最大的物流公司之一的工业环境中，复现了我们先前关于基于LLM的REST API测试扩增的研究。我们将基于LLM的测试扩增技术应用于一个嵌入大规模、高安全性系统的生产微服务中的六个代表性端点，这些端点在身份验证、有状态行为和组织约束方面具有深层复杂性。我们的经验表明，基于LLM的测试扩增在工业实践中仍具有实用价值，能够有效提升测试覆盖率并揭示各类观测结果与异常现象。