DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2408.08594v1
• Date: Fri, 16 Aug 2024 08:03:55 GMT
• Title: DeepREST: Automated Test Case Generation for REST APIs Exploiting Deep Reinforcement Learning
• Authors: Davide Corradini, Zeno Montolli, Michele Pasqua, Mariano Ceccato,
• Abstract summary: 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.
• Score: 5.756036843502232
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automatically crafting test scenarios for REST APIs helps deliver more reliable and trustworthy web-oriented systems. However, current black-box testing approaches rely heavily on the information available in the API's formal documentation, i.e., the OpenAPI Specification (OAS for short). While useful, the OAS mostly covers syntactic aspects of the API (e.g., producer-consumer relations between operations, input value properties, and additional constraints in natural language), and it lacks a deeper understanding of the API business logic. Missing semantics include implicit ordering (logic dependency) between operations and implicit input-value constraints. These limitations hinder the ability of black-box testing tools to generate truly effective test cases automatically. 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. Curiosity-driven learning guides an agent in the exploration of the API and learns an effective order to test its operations. This helps identify which operations to test first to take the API in a testable state and avoid failing API interactions later. At the same time, experience gained on successful API interactions is leveraged to drive accurate input data generation (i.e., what parameters to use and how to pick their values). Additionally, DeepREST alternates exploration with exploitation by mutating successful API interactions to improve test coverage and collect further experience. Our empirical validation suggests that the proposed approach is very effective in achieving high test coverage and fault detection and superior to a state-of-the-art baseline.

Related papers

• A Systematic Evaluation of Large Code Models in API Suggestion: When, Which, and How [53.65636914757381]
  API suggestion is a critical task in modern software development. Recent advancements in large code models (LCMs) have shown promise in the API suggestion task.
  arXiv  Detail & Related papers  (2024-09-20T03:12:35Z)
• FuzzTheREST: An Intelligent Automated Black-box RESTful API Fuzzer [0.0]
  This work introduces a black-box API of fuzzy testing tool that employs Reinforcement Learning (RL) for vulnerability detection. The tool found a total of six unique vulnerabilities and achieved 55% code coverage.
  arXiv  Detail & Related papers  (2024-07-19T14:43:35Z)
• 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)
• 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)
• WorldAPIs: The World Is Worth How Many APIs? A Thought Experiment [49.00213183302225]
  We propose a framework to induce new APIs by grounding wikiHow instruction to situated agent policies. Inspired by recent successes in large language models (LLMs) for embodied planning, we propose a few-shot prompting to steer GPT-4.
  arXiv  Detail & Related papers  (2024-07-10T15:52:44Z)
• A Solution-based LLM API-using Methodology for Academic Information Seeking [49.096714812902576]
  SoAy is a solution-based LLM API-using methodology for academic information seeking. It uses code with a solution as the reasoning method, where a solution is a pre-constructed API calling sequence. Results show a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines.
  arXiv  Detail & Related papers  (2024-05-24T02:44:14Z)
• You Can REST Now: Automated Specification Inference and Black-Box Testing of RESTful APIs with Large Language Models [8.753312212588371]
  manually documenting APIs is a time-consuming and error-prone task, resulting in unavailable, incomplete, or imprecise documentation. Recently, Large Language Models (LLMs) have demonstrated exceptional abilities to automate tasks based on their colossal training data. We present RESTSpecIT, the first automated API specification inference and black-box testing approach.
  arXiv  Detail & Related papers  (2024-02-07T18:55:41Z)
• 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)
• Exploring Behaviours of RESTful APIs in an Industrial Setting [0.43012765978447565]
  We propose a set of behavioural properties, common to REST APIs, which are used to generate examples of behaviours that these APIs exhibit. These examples can be used both (i) to further the understanding of the API and (ii) as a source of automatic test cases. Our approach can generate examples deemed relevant for understanding the system and for a source of test generation by practitioners.
  arXiv  Detail & Related papers  (2023-10-26T11:33:11Z)
• 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)
• Carving UI Tests to Generate API Tests and API Specification [8.743426215048451]
  API-level testing can play an important role, in-between unit-level testing and UI-level (or end-to-end) testing. Existing API testing tools require API specifications, which often may not be available or, when available, be inconsistent with the API implementation. We present an approach that leverages UI testing to enable API-level testing for web applications.
  arXiv  Detail & Related papers  (2023-05-24T03:53:34Z)

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.