KAT: Dependency-aware Automated API Testing with Large Language Models

• URL: http://arxiv.org/abs/2407.10227v1
• Date: Sun, 14 Jul 2024 14:48:18 GMT
• Title: KAT: Dependency-aware Automated API Testing with Large Language Models
• Authors: Tri Le, Thien Tran, Duy Cao, Vy Le, Tien Nguyen, Vu Nguyen,
• Abstract summary: KAT (Katalon API Testing) is a novel AI-driven approach that autonomously generates test cases to validate APIs. Our evaluation of KAT using 12 real-world services shows that it can improve validation coverage, detect more undocumented status codes, and reduce false positives in these services.
• Score: 1.7264233311359707
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 large language 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 large language model for generating test scripts and data for API testing.

Related papers

• A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs [46.65963514391019]
  We present AutoRestTest, the first black-box framework to adopt a dependency-embedded multi-agent approach for REST API testing. We integrate Multi-Agent Reinforcement Learning (MARL) with a Semantic Property Dependency Graph (SPDG) and Large Language Models (LLMs) Our approach treats REST API testing as a separable problem, where four agents -- API, dependency, parameter, and value -- collaborate to optimize API exploration.
  arXiv  Detail & Related papers  (2024-11-11T16:20:27Z)
• Model Equality Testing: Which Model Is This API Serving? [59.005869726179455]
  We formalize detecting such distortions as Model Equality Testing, a two-sample testing problem. A test built on a simple string kernel achieves a median of 77.4% power against a range of distortions. We then apply this test to commercial inference APIs for four Llama models, finding that 11 out of 31 endpoints serve different distributions than reference weights released by Meta.
  arXiv  Detail & Related papers  (2024-10-26T18:34:53Z)
• APITestGenie: Automated API Test Generation through Generative AI [2.0716352593701277]
  APITestGenie generates executable API test scripts from business requirements and API specifications. In experiments with 10 real-world APIs, the tool generated valid test scripts 57% of the time. Human intervention is recommended to validate or refine generated scripts before integration into CI/CD pipelines.
  arXiv  Detail & Related papers  (2024-09-05T18:02:41Z)
• DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning [5.756036843502232]
  This paper introduces DeepREST, a novel black-box approach for automatically testing REST APIs. It leverages deep reinforcement learning to uncover implicit API constraints, that is, constraints hidden from API documentation. Our empirical validation suggests that the proposed approach is very effective in achieving high test coverage and fault detection.
  arXiv  Detail & Related papers  (2024-08-16T08:03:55Z)
• FANTAstic SEquences and Where to Find Them: Faithful and Efficient API Call Generation through State-tracked Constrained Decoding and Reranking [57.53742155914176]
  API call generation is the cornerstone of large language models' tool-using ability. Existing supervised and in-context learning approaches suffer from high training costs, poor data efficiency, and generated API calls that can be unfaithful to the API documentation and the user's request. We propose an output-side optimization approach called FANTASE to address these limitations.
  arXiv  Detail & Related papers  (2024-07-18T23:44:02Z)
• COTS: Connected OpenAPI Test Synthesis for RESTful Applications [0.0]
  We introduce a (i) domain-specific language for OpenAPI specifications and (ii) a tool to support our methodology. Our tool, dubbed COTS, generates (randomised) model-based test executions and reports software defects.
  arXiv  Detail & Related papers  (2024-04-30T15:12:31Z)
• Automating REST API Postman Test Cases Using LLM [0.0]
  This research paper is dedicated to the exploration and implementation of an automated approach to generate test cases using Large Language Models. The methodology integrates the use of Open AI to enhance the efficiency and effectiveness of test case generation. The model that is developed during the research is trained using manually collected postman test cases or instances for various Rest APIs.
  arXiv  Detail & Related papers  (2024-04-16T15:53:41Z)
• GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection? [50.53312866647302]
  HateCheck is a suite for testing fine-grained model functionalities on synthesized data. We propose GPT-HateCheck, a framework to generate more diverse and realistic functional tests from scratch. Crowd-sourced annotation demonstrates that the generated test cases are of high quality.
  arXiv  Detail & Related papers  (2024-02-23T10:02:01Z)
• Leveraging Large Language Models to Improve REST API Testing [51.284096009803406]
  RESTGPT takes as input an API specification, extracts machine-interpretable rules, and generates example parameter values from natural-language descriptions in the specification. Our evaluations indicate that RESTGPT outperforms existing techniques in both rule extraction and value generation.
  arXiv  Detail & Related papers  (2023-12-01T19:53:23Z)
• Adaptive REST API Testing with Reinforcement Learning [54.68542517176757]
  Current testing tools lack efficient exploration mechanisms, treating all operations and parameters equally. Current tools struggle when response schemas are absent in the specification or exhibit variants. We present an adaptive REST API testing technique incorporates reinforcement learning to prioritize operations during exploration.
  arXiv  Detail & Related papers  (2023-09-08T20:27:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.