Academic Journal
Test Amplification for REST APIs: Using 'Out-of-the-Box' Large Language Models
| Title: | Test Amplification for REST APIs: Using 'Out-of-the-Box' Large Language Models |
|---|---|
| Authors: | Tolgahan Bardakci, Serge Demeyer, Mutlu Beyazit |
| Source: | IEEE software |
| Publication Status: | Preprint |
| Publisher Information: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Computer. Automation, Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering |
| Description: | REST APIs (Representational State Transfer Application Programming Interfaces) are an indispensable building block in today's cloud-native applications, so testing them is critically important. However, writing automated tests for such REST APIs is challenging because one needs strong and readable tests that exercise the boundary values of the protocol embedded in the REST API. In this paper, we report our experience with using "out of the box" large language models (ChatGPT and GitHub's Copilot) to amplify REST API test suites. We compare the resulting tests based on coverage and understandability, and we derive a series of guidelines and lessons learned concerning the prompts that result in the strongest test suite. |
| Document Type: | Article |
| ISSN: | 1937-4194 0740-7459 |
| DOI: | 10.1109/ms.2025.3559664 |
| DOI: | 10.48550/arxiv.2503.10306 |
| Access URL: | http://arxiv.org/abs/2503.10306 https://hdl.handle.net/10067/2150540151162165141 https://repository.uantwerpen.be/docstore/d:irua:29438 |
| Rights: | CC BY NC ND CC BY NC SA |
| Accession Number: | edsair.doi.dedup.....b4d1ca56d8b4a7ece3d94b2630cfce7d |
| Database: | OpenAIRE |
| ISSN: | 19374194 07407459 |
|---|---|
| DOI: | 10.1109/ms.2025.3559664 |