arXiv subjects

Robbe Nooyens

Publications and source records attributed to Robbe Nooyens.

2 recordsLinked to original sources

arXiv2025

Agentic LLMs for REST API Test Amplification: A Comparative Study Across Cloud Applications

Representational State Transfer (REST) APIs are a cornerstone of modern cloud native systems. Ensuring their reliability demands automated test suites that exercise diverse and boundary level behaviors. Nevertheless, designing such test cases remains a challenging and resource intensive endeavor. This study extends prior work on Large Language Model (LLM) based test amplification by evaluating single agent and multi agent configurations across four additional cloud applications. The amplified test suites maintain semantic validity with minimal human intervention. The results demonstrate that agentic LLM systems can effectively generalize across heterogeneous API architectures, increasing endpoint and parameter coverage while revealing defects. Moreover, a detailed analysis of computational cost, runtime, and energy consumption highlights trade-offs between accuracy, scalability, and efficiency. These findings underscore the potential of LLM driven test amplification to advance the automation and sustainability of REST API testing in complex cloud environments.

cs.SE↗

arXiv2025

Test Amplification for REST APIs via Single and Multi-Agent LLM Systems

REST APIs (Representational State Transfer Application Programming Interfaces) play a vital role in modern cloud-native applications. As these APIs grow in complexity and scale, ensuring their correctness and robustness becomes increasingly important. Automated testing is essential for identifying hidden bugs, particularly those that appear in edge cases or under unexpected inputs. However, creating comprehensive and effective test suites for REST APIs is challenging and often demands significant effort. In this paper, we investigate the use of large language model (LLM) systems, both single-agent and multi-agent setups, for amplifying existing REST API test suites. These systems generate additional test cases that aim to push the boundaries of the API, uncovering behaviors that might otherwise go untested. We present a comparative evaluation of the two approaches across several dimensions, including test coverage, bug detection effectiveness, and practical considerations such as computational cost and energy usage. Our evaluation demonstrates increased API coverage, identification of numerous bugs in the API under test, and insights into the computational cost and energy consumption of both approaches.

cs.SE↗