# Automated Test Generation for REST APIs: No Time to Rest Yet

arXiv: https://arxiv.org/abs/2204.08348 · HTML: https://mlsanity.com/paper/2204.08348/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2022.

## Built on by (most cited first, 13 of 13)

- [Large Language Model (LLM) for Software Security: Code Analysis, Malware Analysis, Reverse Engineering](https://mlsanity.com/paper/2504.07137/) (2025)
- [A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs](https://mlsanity.com/paper/2411.07098/) (2024)
- [LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents](https://mlsanity.com/paper/2503.15079/) (2025)
- [CodeAssistBench (CAB): Dataset & Benchmarking for Multi-turn Chat-Based Code Assistance](https://mlsanity.com/paper/2507.10646/) (2025)
- [LlamaRestTest: Effective REST API Testing with Small Language Models](https://mlsanity.com/paper/2501.08598/) (2025)
- [AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL](https://mlsanity.com/paper/2501.08600/) (2025)
- [SpeCrawler: Generating OpenAPI Specifications from API Documentation Using Large Language Models](https://mlsanity.com/paper/2402.11625/) (2024)
- [Evaluating LLMs on Sequential API Call Through Automated Test Generation](https://mlsanity.com/paper/2507.09481/) (2025)
- [Test Amplification for REST APIs via Single and Multi-Agent LLM Systems](https://mlsanity.com/paper/2504.08113/) (2025)
- [OASBuilder: Generating OpenAPI Specifications from Online API Documentation with Large Language Models](https://mlsanity.com/paper/2507.05316/) (2025)
- [Agentic LLMs for REST API Test Amplification: A Comparative Study Across Cloud Applications](https://mlsanity.com/paper/2510.27417/) (2025)
- [Multi-Agent LLM-based Metamorphic Testing for REST APIs](https://mlsanity.com/paper/2605.28321/) (2026)
- [Automated Synthesis of Cloud Emulators](https://mlsanity.com/paper/2608.23842/) (2026)

## Abstract (arXiv)

Modern web services routinely provide REST APIs for clients to access their functionality. These APIs present unique challenges and opportunities for automated testing, driving the recent development of many techniques and tools that generate test cases for API endpoints using various strategies. Understanding how these techniques compare to one another is difficult, as they have been evaluated on different benchmarks and using different metrics. To fill this gap, we performed an empirical study aimed to understand the landscape in automated testing of REST APIs and guide future research in this area. We first identified, through a systematic selection process, a set of 10 state-of-the-art REST API testing tools that included tools developed by both researchers and practitioners. We then applied these tools to a benchmark of 20 real-world open-source RESTful services and analyzed their performance in terms of code coverage achieved and unique failures triggered. This analysis allowed us to identify strengths, weaknesses, and limitations of the tools considered and of their underlying strategies, as well as implications of our findings for future research in this area.