RESTestBench: A Benchmark for Evaluating the Effectiveness of LLM-Generated REST API Test Cases from NL Requirements
- URL: http://arxiv.org/abs/2604.25862v1
- Date: Tue, 28 Apr 2026 16:59:08 GMT
- Title: RESTestBench: A Benchmark for Evaluating the Effectiveness of LLM-Generated REST API Test Cases from NL Requirements
- Abstract summary: RESTestBench is a benchmark comprising three REST services paired with manually verified NL requirements.<n>We evaluate two approaches across multiple state-of-the-art LLMs: (i) non-refinement-based generation, and (ii) refinement-based generation guided by interaction with the running SUT.
- Score: 0.13681174239726607
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: Existing REST API testing tools are typically evaluated using code coverage and crash-based fault metrics. However, recent LLM-based approaches increasingly generate tests from NL requirements to validate functional behaviour, making traditional metrics weak proxies for whether generated tests validate intended behaviour. To address this gap, we present RESTestBench, a benchmark comprising three REST services paired with manually verified NL requirements in both precise and vague variants, enabling controlled and reproducible evaluation of requirement-based test generation. RESTestBench further introduces a requirements-based mutation testing metric that measures the fault-detection effectiveness of a generated test case with respect to a specific requirement, extending the property-based approach of Bartocci et al. . Using RESTestBench, we evaluate two approaches across multiple state-of-the-art LLMs: (i) non-refinement-based generation, and (ii) refinement-based generation guided by interaction with the running SUT. In the refinement experiments, RESTestBench assesses how exposure to the actual implementation, valid or mutated, affects test effectiveness. Our results show that test effectiveness drops considerably when the generator interacts with faulty or mutated code, especially for vague requirements, sometimes negating the benefit of refinement and indicating that incorporating actual SUT behaviour is unnecessary when requirement detail is high.
Related papers
- Do Coverage and Mutation Scores of LLM-Generated Test Suites Correlate with Their Effectiveness? (Replicability Study) [6.228248513679207]
Studies show that correlations among coverage, mutation, and real-bug detection can largely vanish once test suite size is controlled.<n>We show that the usefulness of coverage and mutation is highly context-dependent.<n>We also find little evidence that test suite size is a dominant confounder for correlations among coverage, mutation, and real-bug detection.
arXiv Detail & Related papers (2026-07-24T19:46:30Z) - Multi-Agent LLM Collaboration for Unit Test Generation via Human-Testing-Inspired Workflows [18.32804849870551]
Large Language Models (LLMs) have spurred a surge of research into automated unit test generation.<n>We propose TestAgent, an LLM-based test generation approach that emulates human testing practices via a multi-agent collaboration mechanism.<n>TestAgent achieves 97.46% execution rate, 92.34% line coverage, 90.24% branch coverage, and 83.69% mutation score on six Java projects.
arXiv Detail & Related papers (2026-07-10T05:16:54Z) - LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
arXiv Detail & Related papers (2026-05-02T11:31:33Z) - LLMCFG-TGen: Using LLM-Generated Control Flow Graphs to Automatically Create Test Cases from Use Cases [11.173694789846435]
Appropriate test case generation is critical in software testing.<n>Use-case descriptions are a popular method for capturing functional behaviors and interaction flows in a structured form.<n>We propose a new approach that automatically generates test cases from NL use-case descriptions.
arXiv Detail & Related papers (2025-12-06T11:19:37Z) - Use Property-Based Testing to Bridge LLM Code Generation and Validation [38.25155484701058]
Large Language Models (LLMs) excel at code generation, but ensuring their outputs to be functionally correct is a persistent challenge.<n>This paper introduces Property-Generated Solver, a novel framework that leverages Property-Based Testing (PBT) to validate high-level program properties.<n>Property-Generated Solver employs two collaborative LLM-based agents: a Generator dedicated to code generation and iterative refinement, and a Tester that manages the PBT life-cycle.
arXiv Detail & Related papers (2025-06-23T06:01:12Z) - 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) - Toward Automated Validation of Language Model Synthesized Test Cases using Semantic Entropy [0.5057850174013127]
Modern Large Language Model (LLM)-based programming agents often rely on test execution feedback to refine their generated code.<n>This paper introduces VALTEST, a novel framework that leverages semantic entropy to automatically validate test cases generated by LLMs.<n>Experiments show that VALTEST boosts test validity by up to 29% and improves code generation performance, as evidenced by significant increases in pass@1 scores.
arXiv Detail & Related papers (2024-11-13T00:07:32Z) - Self-Evaluation Improves Selective Generation in Large Language Models [54.003992911447696]
We reformulate open-ended generation tasks into token-level prediction tasks.
We instruct an LLM to self-evaluate its answers.
We benchmark a range of scoring methods based on self-evaluation.
arXiv Detail & Related papers (2023-12-14T19:09:22Z) - Deep anytime-valid hypothesis testing [29.273915933729057]
We propose a general framework for constructing powerful, sequential hypothesis tests for nonparametric testing problems.
We develop a principled approach of leveraging the representation capability of machine learning models within the testing-by-betting framework.
Empirical results on synthetic and real-world datasets demonstrate that tests instantiated using our general framework are competitive against specialized baselines.
arXiv Detail & Related papers (2023-10-30T09:46:19Z) - Towards Automatic Generation of Amplified Regression Test Oracles [44.45138073080198]
We propose a test oracle derivation approach to amplify regression test oracles.
The approach monitors the object state during test execution and compares it to the previous version to detect any changes in relation to the SUT's intended behaviour.
arXiv Detail & Related papers (2023-07-28T12:38:44Z) - LLMs as Factual Reasoners: Insights from Existing Benchmarks and Beyond [135.8013388183257]
We propose a new protocol for inconsistency detection benchmark creation and implement it in a 10-domain benchmark called SummEdits.
Most LLMs struggle on SummEdits, with performance close to random chance.
The best-performing model, GPT-4, is still 8% below estimated human performance.
arXiv Detail & Related papers (2023-05-23T21:50:06Z) - On the Limits of Evaluating Embodied Agent Model Generalization Using
Validation Sets [101.28658250723804]
This paper experiments with augmenting a transformer model with modules that effectively utilize a wider field of view and learn to choose whether the next step requires a navigation or manipulation action.
We observe that the proposed modules resulted in improved, and in fact state-of-the-art performance on an unseen validation set of a popular benchmark dataset, ALFRED.
We highlight this result as we believe it may be a wider phenomenon in machine learning tasks but primarily noticeable only in benchmarks that limit evaluations on test splits.
arXiv Detail & Related papers (2022-05-18T23:52:21Z)
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.