Last updated on Monday, October 06, 2025
@inproceedings{Nooyens2025ICTSS,
author = {Robbe Nooyens and Tolghahan Bardakci and
Mutlu Beyaz{\i}t and Serge Demeyer},
booktitle = {Proceedings {ICTSS 2025} (37th International
Conference on Testing Software and Systems)},
title = {Test Amplification for {REST APIs} via Single and
Multi-Agent LLM Systems},
year = {2025},
abstract = {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.},
annote = {internationalconference},
doi = {unknown},
}