API testing has increasing demands for software companies. Prior API testing tools were aware of certain types of dependencies that needed to be concise between operations and parameters. However, their approaches, wh...
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ISBN:
(纸本)9798350308198;9798350308181
API testing has increasing demands for software companies. Prior API testing tools were aware of certain types of dependencies that needed to be concise between operations and parameters. However, their approaches, which are mostly done manually or using heuristic-based algorithms, have limitations due to the complexity of these dependencies. In this paper, we present KAT (Katalon API testing), a novel AI-driven approach that leverages the largelanguage model GPT in conjunction with advanced prompting techniques to autonomously generate test cases to validate RESTful APIs. Our comprehensive strategy encompasses various processes to construct an operation dependency graph from an OpenAPI specification and to generate test scripts, constraint validation scripts, test cases, and test data. Our evaluation of KAT using 12 real-world RESTful services shows that it can improve test coverage, detect more undocumented status codes, and reduce false positives in these services in comparison with a state-of-the-art automated test generation tool. These results indicate the effectiveness of using the largelanguage model for generating test scripts and data for API testing.
The widespread adoption of REST APIs, coupled with their growing complexity and size, has led to the need for automated REST API testing tools. Current tools focus on the structured data in REST API specifications but...
详细信息
ISBN:
(纸本)9798400705007
The widespread adoption of REST APIs, coupled with their growing complexity and size, has led to the need for automated REST API testing tools. Current tools focus on the structured data in REST API specifications but often neglect valuable insights available in unstructured natural-language descriptions in the specifications, which leads to suboptimal test coverage. Recently, to address this gap, researchers have developed techniques that extract rules from these human-readable descriptions and query knowledge bases to derive meaningful input values. However, these techniques are limited in the types of rules they can extract and prone to produce inaccurate results. This paper presents RESTGPT, an innovative approach that leverages the power and intrinsic context-awareness of largelanguagemodels (LLMs) to improve REST API testing. RESTGPT takes as input an API specification, extracts machine-interpretable rules, and generates example parameter values from natural-language descriptions in the specification. It then augments the original specification with these rules and values. Our evaluations indicate that RESTGPT outperforms existing techniques in both rule extraction and value generation. Given these promising results, we outline future research directions for advancing REST API testing through LLMs.
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