Automated Program Repair Based on REST API Specifications Using Large Language Models

• URL: http://arxiv.org/abs/2510.25148v1
• Date: Wed, 29 Oct 2025 04:04:40 GMT
• Title: Automated Program Repair Based on REST API Specifications Using Large Language Models
• Abstract summary: dcFix is a method for detecting and automatically repairing REST API misuses in client programs.<n>Our evaluation demonstrates that dcFix accurately detects misuse and outperforms the baseline approach.
• Score: 0.48414873775965206
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Many cloud services provide REST API accessible to client applications. However, developers often identify specification violations only during testing, as error messages typically lack the detail necessary for effective diagnosis. Consequently, debugging requires trial and error. This study proposes dcFix, a method for detecting and automatically repairing REST API misuses in client programs. In particular, dcFix identifies non-conforming code fragments, integrates them with the relevant API specifications into prompts, and leverages a Large Language Model (LLM) to produce the corrected code. Our evaluation demonstrates that dcFix accurately detects misuse and outperforms the baseline approach, in which prompts to the LLM omit any indication of code fragments non conforming to REST API specifications.

Related papers

• Database-Augmented RAG for Automated Repair of REST API Misuses [2.339620547243152]
  Retrieval-Augmented Generation (RAG) is a promising approach for providing large language models with external knowledge.<n>This study evaluates how different configurations for organizing API specifications affect RAG-based repair of REST API misuse.
  arXiv  Detail & Related papers  (2026-08-29T14:27:22Z)
• Multi-Agent LLM-based Metamorphic Testing for REST APIs [1.7616042687330637]
  We present ARMeta, a tool-supported approach that uses an LLM-based multi-agent workflow to support metamorphic testing of REST APIs.<n>We evaluate ARMeta on two publicly available web applications that expose REST interfaces and compare its performance with a scenario-based testing baseline.
  arXiv  Detail & Related papers  (2026-05-27T11:24:46Z)
• APITestGenie: Generating Web API Tests from Requirements and API Specifications with LLMs [0.6445087473595952]
  APITestGenie is a novel tool that automatically generates API integration tests directly from business requirements and OpenAPI specifications.<n>The tool was able to generate syntactically and semantically valid test scripts for 89% of the business requirements under test after at most three attempts.<n>Some generated tests revealed previously unknown defects in the APIs, including integration issues between endpoints.
  arXiv  Detail & Related papers  (2026-04-02T13:43:56Z)
• LAB-Det: Language as a Domain-Invariant Bridge for Training-Free One-Shot Domain Generalization in Object Detection [53.988759250627425]
  We introduce training-free one-shot domain generalization for object detection.<n>We project each exemplar into a descriptive text that conditions and guides a frozen detector.<n>We evaluate on UODD (underwater) and NEU-DET (industrial defects)
  arXiv  Detail & Related papers  (2026-02-06T08:03:04Z)
• Repairing Regex Vulnerabilities via Localization-Guided Instructions [6.033257307910245]
  Regular expressions (regexes) expose systems to regular expression denial of service (ReDoS)<n>Current approaches, however, are hampered by a trade-off.<n>We introduce a hybrid framework, localized repair (LRR), designed to harness generalization while enforcing reliability.
  arXiv  Detail & Related papers  (2025-10-10T06:15:43Z)
• Towards Automated Error Discovery: A Study in Conversational AI [48.735443116662026]
  We introduce Automated Error Discovery, a framework for detecting and defining errors in conversational AI.<n>We also propose SEEED (Soft Clustering Extended-Based Error Detection), as an encoder-based approach to its implementation.
  arXiv  Detail & Related papers  (2025-09-13T14:53:22Z)
• Specification-Guided Repair of Arithmetic Errors in Dafny Programs using LLMs [79.74676890436174]
  We present an APR tool for Dafny that uses formal specifications as oracles for fault localization and repair.<n>We localize faults through a series of steps, which include using Hoare logic to determine the state of each statement within the program.<n>Our tool achieves 89.6% fault localization coverage and GPT-4o mini yields the highest repair success rate of 74.18%.
  arXiv  Detail & Related papers  (2025-07-04T15:36:12Z)
• A Framework for Testing and Adapting REST APIs as LLM Tools [11.757827071584737]
  Large Language Models (LLMs) are increasingly used to build autonomous agents that perform complex tasks with external tools.<n>Current benchmarks overlook these challenges, leaving a gap in assessing API readiness for agent-driven automation.<n>We present a testing framework that systematically evaluates enterprise APIs when wrapped as Python tools for LLM-based agents.
  arXiv  Detail & Related papers  (2025-04-22T02:52:08Z)
• AutoRestTest: A Tool for Automated REST API Testing Using LLMs and MARL [46.65963514391019]
  AutoRestTest is a novel tool that integrates the Semantic Property Dependency Graph (SPDG) with Multi-Agent Reinforcement Learning (MARL) and large language models (LLMs) for effective REST API testing.
  arXiv  Detail & Related papers  (2025-01-15T05:54:33Z)
• LlamaRestTest: Effective REST API Testing with Small Language Models [50.058600784556816]
  We present LlamaRestTest, a novel approach that employs two custom Large Language Models (LLMs) to generate realistic test inputs.<n>We evaluate it against several state-of-the-art REST API testing tools, including RESTGPT, a GPT-powered specification-enhancement tool.<n>Our study shows that small language models can perform as well as, or better than, large language models in REST API testing.
  arXiv  Detail & Related papers  (2025-01-15T05:51:20Z)
• 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.<n>Our approach treats REST API testing as a separable problem, where four agents collaborate to optimize API exploration.<n>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)
• The Midas Touch: Triggering the Capability of LLMs for RM-API Misuse Detection [26.28337534131051]
  ChatDetector fully automates documentation understanding for RM-API misuse detection. ChatDetector identifies 165 pairs of RM-APIs with a precision of 98.21% compared with the state-of-the-art API detectors.
  arXiv  Detail & Related papers  (2024-09-14T09:11:18Z)

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.