Prompt-Based REST API Test Amplification in Industry: An Experience Report

TL;DR

This paper reports on applying large language model-based REST API test amplification in an industrial logistics setting, demonstrating its practical usefulness in increasing test coverage and revealing system anomalies.

Contribution

It provides the first industrial case study of LLM-based REST API test amplification, highlighting its effectiveness in complex, real-world microservice environments.

Findings

Increased test coverage in industrial REST APIs

Revealed system anomalies and security issues

Demonstrated practical utility in complex, real-world settings

Abstract

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.