APITestGenie: Automated API Test Generation through Generative AI
- URL: http://arxiv.org/abs/2409.03838v1
- Date: Thu, 5 Sep 2024 18:02:41 GMT
- Title: APITestGenie: Automated API Test Generation through Generative AI
- Authors: André Pereira, Bruno Lima, João Pascoal Faria,
- Abstract summary: 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.
- Score: 2.0716352593701277
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Intelligent assistants powered by Large Language Models (LLMs) can generate program and test code with high accuracy, boosting developers' and testers' productivity. However, there is a lack of studies exploring LLMs for testing Web APIs, which constitute fundamental building blocks of modern software systems and pose significant test challenges. Hence, in this article, we introduce APITestGenie, an approach and tool that leverages LLMs to generate 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. With three generation attempts per task, this success rate increased to 80%. Human intervention is recommended to validate or refine generated scripts before integration into CI/CD pipelines, positioning our tool as a productivity assistant rather than a replacement for testers. Feedback from industry specialists indicated a strong interest in adopting our tool for improving the API test process.
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) - AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs.
Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
arXiv Detail & Related papers (2024-11-02T13:24:30Z) - Multi-language Unit Test Generation using LLMs [6.259245181881262]
We describe a generic pipeline that incorporates static analysis to guide LLMs in generating compilable and high-coverage test cases.
We show how the pipeline can be applied to different programming languages, specifically Java and Python, and to complex software requiring environment mocking.
Our results demonstrate that LLM-based test generation, when guided by static analysis, can be competitive with, and even outperform, state-of-the-art test-generation techniques in coverage achieved.
arXiv Detail & Related papers (2024-09-04T21:46:18Z) - A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites [1.4563527353943984]
Large Language Models (LLMs) have been applied to various aspects of software development.
We present AgoneTest: an automated system for generating test suites for Java projects.
arXiv Detail & Related papers (2024-08-14T23:02:16Z) - KAT: Dependency-aware Automated API Testing with Large Language Models [1.7264233311359707]
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.
arXiv Detail & Related papers (2024-07-14T14:48:18Z) - BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions [72.56339136017759]
We introduce BigCodeBench, a benchmark that challenges Large Language Models (LLMs) to invoke multiple function calls as tools from 139 libraries and 7 domains for 1,140 fine-grained tasks.
Our evaluation shows that LLMs are not yet capable of following complex instructions to use function calls precisely, with scores up to 60%, significantly lower than the human performance of 97%.
We propose a natural-language-oriented variant of BigCodeBench, BigCodeBench-Instruct, that automatically transforms the original docstrings into short instructions only with essential information.
arXiv Detail & Related papers (2024-06-22T15:52:04Z) - 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) - 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) - LLM for Test Script Generation and Migration: Challenges, Capabilities,
and Opportunities [8.504639288314063]
Test script generation is a vital component of software testing, enabling efficient and reliable automation of repetitive test tasks.
Existing generation approaches often encounter limitations, such as difficulties in accurately capturing and reproducing test scripts across diverse devices, platforms, and applications.
This paper investigates the application of large language models (LLM) in the domain of mobile application test script generation.
arXiv Detail & Related papers (2023-09-24T07:58:57Z) - ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world
APIs [104.37772295581088]
Open-source large language models (LLMs), e.g., LLaMA, remain significantly limited in tool-use capabilities.
We introduce ToolLLM, a general tool-usetuning encompassing data construction, model training, and evaluation.
We first present ToolBench, an instruction-tuning framework for tool use, which is constructed automatically using ChatGPT.
arXiv Detail & Related papers (2023-07-31T15:56:53Z) - API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs [84.45284695156771]
API-Bank is a groundbreaking benchmark for tool-augmented Large Language Models.
We develop a run evaluation system consisting of 73 API tools.
We construct a comprehensive training set containing 1,888 tool-use dialogues from 2,138 APIs spanning 1,000 distinct domains.
arXiv Detail & Related papers (2023-04-14T14:05:32Z)
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.