Morest: Model-based RESTful API Testing with Execution Feedback
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Abstract (arXiv)
RESTful APIs are arguably the most popular endpoints for accessing Web services. Blackbox testing is one of the emerging techniques for ensuring the reliability of RESTful APIs. The major challenge in testing RESTful APIs is the need for correct sequences of API operation calls for in-depth testing. To build meaningful operation call sequences, researchers have proposed techniques to learn and utilize the API dependencies based on OpenAPI specifications. However, these techniques either lack the overall awareness of how all the APIs are connected or the flexibility of adaptively fixing the learned knowledge. In this paper, we propose Morest, a model-based RESTful API testing technique that builds and maintains a dynamically updating RESTful-service Property Graph (RPG) to model the behaviors of RESTful-services and guide the call sequence generation. We empirically evaluated Morest and the results demonstrate that Morest can successfully request an average of 152.66%-232.45% more API operations, cover 26.16%-103.24% more lines of code, and detect 40.64%-215.94% more bugs than state-of-the-art techniques. In total, we applied Morest to 6 real-world projects and found 44 bugs (13 of them cannot be detected by existing approaches). Specifically, 2 of the confirmed bugs are from Bitbucket, a famous code management service with more than 6 million users.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Apr 2022.
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Citing papers per year: 2024: 3; 2025: 5; 2026: 2.
10 papers.
- 2024A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputscited by 9
- 2025LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agentscited by 5
- 2024APIRL: Deep Reinforcement Learning for REST API Fuzzingcited by 4
- 2025LlamaRestTest: Effective REST API Testing with Small Language Modelscited by 4
- 2025AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARLcited by 3
- 2024FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzercited by 2
- 2025Evaluating LLMs on Sequential API Call Through Automated Test Generationcited by 2
- 2026Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generationcited by 1
- 2025MASTEST: A LLM-Based Multi-Agent System For RESTful API Testscited by 0
- 2026Mining Workflow Graphs for Black-Box Boundary Testing of Conversational LLM Agentscited by 0
- 2026Mining Workflow Graphs for Black-Box Boundary Testing of Conversational LLM Agents8 Jul 2026
- 2026Hawk: Harnessing Hardware-Aware Knowledge for High-Performance NPU Kernel Generation2 Jul 2026
- 2025MASTEST: A LLM-Based Multi-Agent System For RESTful API Tests22 Nov 2025
- 2025Evaluating LLMs on Sequential API Call Through Automated Test Generation13 Jul 2025
- 2025LogiAgent: Automated Logical Testing for REST Systems with LLM-Based Multi-Agents19 Mar 2025
- 2025AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL15 Jan 2025
- 2025LlamaRestTest: Effective REST API Testing with Small Language Models15 Jan 2025
- 2024APIRL: Deep Reinforcement Learning for REST API Fuzzing20 Dec 2024
- 2024A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs11 Nov 2024
- 2024FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer19 Jul 2024