TY - UNPB
T1 - You Can REST Now
T2 - Automated Specification Inference and Black-Box Testing of RESTful APIs with Large Language Models
AU - Decrop, Alix
AU - Perrouin, Gilles
AU - Papadakis, Mike
AU - Devroey, Xavier
AU - Schobbens, Pierre-Yves
PY - 2024/2/7
Y1 - 2024/2/7
N2 - RESTful APIs are popular web services, requiring documentation to ease their comprehension, reusability and testing practices. The OpenAPI Specification (OAS) is a widely adopted and machine-readable format used to document such APIs. However, manually documenting RESTful APIs is a time-consuming and error-prone task, resulting in unavailable, incomplete, or imprecise documentation. As RESTful API testing tools require an OpenAPI specification as input, insufficient or informal documentation hampers testing quality. Recently, Large Language Models (LLMs) have demonstrated exceptional abilities to automate tasks based on their colossal training data. Accordingly, such capabilities could be utilized to assist the documentation and testing process of RESTful APIs. In this paper, we present RESTSpecIT, the first automated RESTful API specification inference and black-box testing approach leveraging LLMs. The approach requires minimal user input compared to state-of-the-art RESTful API inference and testing tools; Given an API name and an LLM key, HTTP requests are generated and mutated with data returned by the LLM. By sending the requests to the API endpoint, HTTP responses can be analyzed for inference and testing purposes. RESTSpecIT utilizes an in-context prompt masking strategy, requiring no model fine-tuning. Our evaluation demonstrates that RESTSpecIT is capable of: (1) inferring specifications with 85.05% of GET routes and 81.05% of query parameters found on average, (2) discovering undocumented and valid routes and parameters, and (3) uncovering server errors in RESTful APIs. Inferred specifications can also be used as testing tool inputs.
AB - RESTful APIs are popular web services, requiring documentation to ease their comprehension, reusability and testing practices. The OpenAPI Specification (OAS) is a widely adopted and machine-readable format used to document such APIs. However, manually documenting RESTful APIs is a time-consuming and error-prone task, resulting in unavailable, incomplete, or imprecise documentation. As RESTful API testing tools require an OpenAPI specification as input, insufficient or informal documentation hampers testing quality. Recently, Large Language Models (LLMs) have demonstrated exceptional abilities to automate tasks based on their colossal training data. Accordingly, such capabilities could be utilized to assist the documentation and testing process of RESTful APIs. In this paper, we present RESTSpecIT, the first automated RESTful API specification inference and black-box testing approach leveraging LLMs. The approach requires minimal user input compared to state-of-the-art RESTful API inference and testing tools; Given an API name and an LLM key, HTTP requests are generated and mutated with data returned by the LLM. By sending the requests to the API endpoint, HTTP responses can be analyzed for inference and testing purposes. RESTSpecIT utilizes an in-context prompt masking strategy, requiring no model fine-tuning. Our evaluation demonstrates that RESTSpecIT is capable of: (1) inferring specifications with 85.05% of GET routes and 81.05% of query parameters found on average, (2) discovering undocumented and valid routes and parameters, and (3) uncovering server errors in RESTful APIs. Inferred specifications can also be used as testing tool inputs.
KW - cs.SE
M3 - Preprint
BT - You Can REST Now
ER -