RESTberus: Trace-Aware Benchmarking of Automated REST API Testing Tools Across Microservice Dependencies

Abstract

Existing REST API testing benchmarks focus on isolated services, overlooking failures arising from interactions with downstream dependencies. In microservice systems, response bodies and entry-point operations alone cannot reliably distinguish failures, compromising tool comparisons. We present RESTberus, a Kubernetes-based benchmarking platform for black-box REST API testing tools on deployed microservice systems. By complementing API-level evidence with distributed traces, RESTberus can distinguish failures manifesting in the target service from those manifesting in downstream dependencies. Across six tools on two microservice benchmarks, our trace-aware deduplication strategy achieves 87.1% accuracy, outperforming baselines from prior work by 10-13 percentage points. Our empirical study shows that all tools expose downstream failures, accounting for 11.7% of estimated unique failures on average, which isolated-service benchmarks cannot expose. On single-service benchmarks, RESTberus produces statistically equivalent results to RESTgym, demonstrating that integration-level assessments can preserve compatibility with existing tools and comparability with prior evaluations.