COTS: Connected OpenAPI Test Synthesis for RESTful Applications
- URL: http://arxiv.org/abs/2404.19614v2
- Date: Sun, 4 Aug 2024 09:21:58 GMT
- Title: COTS: Connected OpenAPI Test Synthesis for RESTful Applications
- Authors: Christian Bartolo Burlò, Adrian Francalanza, Alceste Scalas, Emilio Tuosto,
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We present a novel model-driven approach for testing RESTful applications. We introduce a (i) domain-specific language for OpenAPI specifications and (ii) a tool to support our methodology. Our DSL is inspired by session types and enables the modelling of communication protocols between a REST client and server. Our tool, dubbed COTS, generates (randomised) model-based test executions and reports software defects. We evaluate the effectiveness of our approach by applying it to test several open source applications. Our findings indicate that our methodology can identify nuanced defects in REST APIs and achieve comparable or superior code coverage when compared to much larger handcrafted test suites.
Related papers
- Utilizing API Response for Test Refinement [2.8002188463519944]
This paper proposes a dynamic test refinement approach that leverages the response message.
Using an intelligent agent, the approach adds constraints to the API specification that are further used to generate a test scenario.
The proposed approach led to a decrease in the number of 4xx responses, taking a step closer to generating more realistic test cases.
arXiv Detail & Related papers (2025-01-30T05:26:32Z) - LlamaRestTest: Effective REST API Testing with Small Language Models [50.058600784556816]
We present LlamaRestTest, a novel approach that employs two custom LLMs to generate realistic test inputs.
LlamaRestTest surpasses state-of-the-art tools in code coverage and error detection, even with RESTGPT-enhanced specifications.
arXiv Detail & Related papers (2025-01-15T05:51:20Z) - Commit0: Library Generation from Scratch [77.38414688148006]
Commit0 is a benchmark that challenges AI agents to write libraries from scratch.
Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests.
Commit0 also offers an interactive environment where models receive static analysis and execution feedback on the code they generate.
arXiv Detail & Related papers (2024-12-02T18:11:30Z) - A Multi-Agent Approach for REST API Testing with Semantic Graphs and LLM-Driven Inputs [46.65963514391019]
We present AutoRestTest, the first black-box tool to adopt a dependency-embedded multi-agent approach for REST API testing.
Our approach treats REST API testing as a separable problem, where four agents collaborate to optimize API exploration.
Our evaluation of AutoRestTest on 12 real-world REST services shows that it outperforms the four leading black-box REST API testing tools.
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) - 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) - 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) - Nirikshak: A Clustering Based Autonomous API Testing Framework [0.0]
Nirikshak is a self-reliant testing framework for REST API testing.
It achieves level 2 of autonomy in executing REST API testing procedures.
Nirikshak is publicly available as an open-source software for the community at https://github.com/yashmahalwal/nirikshak.
arXiv Detail & Related papers (2021-12-15T18:05:27Z)
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.