Test smells in LLM-Generated Unit Tests

• URL: http://arxiv.org/abs/2410.10628v2
• Date: Thu, 06 Nov 2025 10:08:44 GMT
• Title: Test smells in LLM-Generated Unit Tests
• Authors: Wendkûuni C. Ouédraogo, Yinghua Li, Xueqi Dang, Xunzhu Tang, Anil Koyuncu, Jacques Klein, David Lo, Tegawendé F. Bissyandé,
• Abstract summary: This paper presents the first multi-benchmark, large-scale analysis of test smell diffusion in LLM-generated unit tests.<n>Our study draws on 20,505 class-level suites from four LLMs (GPT-3.5, GPT-4, Mistral 7B, Mixtral 8x7B), 972 method-level cases from TestBench, 14,469 EvoSuite tests, and 779,585 human-written tests from 34,635 open-source Java projects.
• Score: 16.061139428298986
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: LLMs promise to transform unit test generation from a manual burden into an automated solution. Yet, beyond metrics such as compilability or coverage, little is known about the quality of LLM-generated tests, particularly their susceptibility to test smells, design flaws that undermine readability and maintainability. This paper presents the first multi-benchmark, large-scale analysis of test smell diffusion in LLM-generated unit tests. We contrast LLM outputs with human-written suites (as the reference for real-world practices) and SBST-generated tests from EvoSuite (as the automated baseline), disentangling whether LLMs reproduce human-like flaws or artifacts of synthetic generation. Our study draws on 20,505 class-level suites from four LLMs (GPT-3.5, GPT-4, Mistral 7B, Mixtral 8x7B), 972 method-level cases from TestBench, 14,469 EvoSuite tests, and 779,585 human-written tests from 34,635 open-source Java projects. Using two complementary detection tools (TsDetect and JNose), we analyze prevalence, co-occurrence, and correlations with software attributes and generation parameters. Results show that LLM-generated tests consistently manifest smells such as Assertion Roulette and Magic Number Test, with patterns strongly influenced by prompting strategy, context length, and model scale. Comparisons reveal overlaps with human-written tests, raising concerns of potential data leakage from training corpora while EvoSuite exhibits distinct, generator-specific flaws. These findings highlight both the promise and the risks of LLM-based test generation, and call for the design of smell-aware generation frameworks, prompt engineering strategies, and enhanced detection tools to ensure maintainable, high-quality test code.

Related papers

• Automated Test Suite Enhancement Using Large Language Models with Few-shot Prompting [0.0]
  Unit testing is essential for verifying the functional correctness of code modules.<n>Unit tests generated by tools that employ traditional approaches, such as search-based software testing (SBST), lack readability, naturalness, and practical usability.<n>Software repositories now include a mix of human-written tests, LLM-generated tests, and those from tools employing traditional approaches such as SBST.
  arXiv  Detail & Related papers  (2026-02-12T18:42:49Z)
• YATE: The Role of Test Repair in LLM-Based Unit Test Generation [22.67442101368384]
  We propose a technique for repairing some of these incorrect tests through a combination of rule-based static analysis and re-prompting.<n>We evaluate this simple approach, named YATE, on a set of 6 open-source projects.<n>YATE achieves 22% higher line coverage, 20% higher branch coverage and kill 20% more mutants at a comparable cost.
  arXiv  Detail & Related papers  (2025-07-24T11:32:31Z)
• PALM: Synergizing Program Analysis and LLMs to Enhance Rust Unit Test Coverage [14.702182387149547]
  This paper presents PALM, an approach that leverages large language models (LLMs) to enhance the generation of high-coverage unit tests.<n> PALM performs program analysis to identify branching conditions within functions, which are then combined into path constraints.<n>We implement the approach and evaluate it in 15 open-source Rust crates.
  arXiv  Detail & Related papers  (2025-06-10T17:21:21Z)
• Scoring Verifiers: Evaluating Synthetic Verification for Code and Reasoning [59.25951947621526]
  We propose an approach which can transform existing coding benchmarks into scoring and ranking datasets to evaluate the effectiveness of synthetic verifiers.<n>We release four new benchmarks (HE-R, HE-R+, MBPP-R, and MBPP-R+), and analyzed synthetic verification methods with standard, reasoning-based, and reward-based LLMs.<n>Our experiments show that reasoning can significantly improve test case generation and that scaling the number of test cases enhances the verification accuracy.
  arXiv  Detail & Related papers  (2025-02-19T15:32:11Z)
• ProjectTest: A Project-level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms [48.43237545197775]
  Unit test generation has become a promising and important use case of LLMs. ProjectTest is a project-level benchmark for unit test generation covering Python, Java, and JavaScript.
  arXiv  Detail & Related papers  (2025-02-10T15:24:30Z)
• 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. We evaluate it against several state-of-the-art REST API testing tools, including RESTGPT, a GPT-powered specification-enhancement tool. 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)
• Improving the Readability of Automatically Generated Tests using Large Language Models [7.7149881834358345]
  We propose to combine the effectiveness of search-based generators with the readability of LLM generated tests. Our approach focuses on improving test and variable names produced by search-based tools, while keeping their semantics unchanged.
  arXiv  Detail & Related papers  (2024-12-25T09:08:53Z)
• AntiLeak-Bench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge [68.39683427262335]
  Existing studies fail to guarantee contamination-free evaluation as newly collected data may contain pre-existing knowledge. We propose AntiLeak-Bench, an automated anti-leakage benchmarking framework.
  arXiv  Detail & Related papers  (2024-12-18T09:53:12Z)
• SpecTool: A Benchmark for Characterizing Errors in Tool-Use LLMs [77.79172008184415]
  SpecTool is a new benchmark to identify error patterns in LLM output on tool-use tasks. We show that even the most prominent LLMs exhibit these error patterns in their outputs. Researchers can use the analysis and insights from SPECTOOL to guide their error mitigation strategies.
  arXiv  Detail & Related papers  (2024-11-20T18:56:22Z)
• 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)
• SYNTHEVAL: Hybrid Behavioral Testing of NLP Models with Synthetic CheckLists [59.08999823652293]
  We propose SYNTHEVAL to generate a wide range of test types for a comprehensive evaluation of NLP models. In the last stage, human experts investigate the challenging examples, manually design templates, and identify the types of failures the taskspecific models consistently exhibit. We apply SYNTHEVAL to two classification tasks, sentiment analysis and toxic language detection, and show that our framework is effective in identifying weaknesses of strong models on these tasks.
  arXiv  Detail & Related papers  (2024-08-30T17:41:30Z)
• Improving LLM-based Unit test generation via Template-based Repair [8.22619177301814]
  Unit test is crucial for detecting bugs in individual program units but consumes time and effort. Large language models (LLMs) have demonstrated remarkable reasoning and generation capabilities. In this paper, we propose TestART, a novel unit test generation method.
  arXiv  Detail & Related papers  (2024-08-06T10:52:41Z)
• Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation [11.056044348209483]
  Unit testing, crucial for identifying bugs in code modules like classes and methods, is often neglected by developers due to time constraints. Large Language Models (LLMs), like GPT and Mistral, show promise in software engineering, including in test generation.
  arXiv  Detail & Related papers  (2024-06-28T20:38:41Z)
• Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph [83.90988015005934]
  Uncertainty quantification (UQ) is a critical component of machine learning (ML) applications. We introduce a novel benchmark that implements a collection of state-of-the-art UQ baselines. We conduct a large-scale empirical investigation of UQ and normalization techniques across nine tasks, and identify the most promising approaches.
  arXiv  Detail & Related papers  (2024-06-21T20:06:31Z)
• DALD: Improving Logits-based Detector without Logits from Black-box LLMs [56.234109491884126]
  Large Language Models (LLMs) have revolutionized text generation, producing outputs that closely mimic human writing. We present Distribution-Aligned LLMs Detection (DALD), an innovative framework that redefines the state-of-the-art performance in black-box text detection. DALD is designed to align the surrogate model's distribution with that of unknown target LLMs, ensuring enhanced detection capability and resilience against rapid model iterations.
  arXiv  Detail & Related papers  (2024-06-07T19:38:05Z)
• Test Oracle Automation in the era of LLMs [52.69509240442899]
  Large Language Models (LLMs) have demonstrated remarkable proficiency in tackling diverse software testing tasks. This paper aims to enable discussions on the potential of using LLMs for test oracle automation, along with the challenges that may emerge during the generation of various types of oracles.
  arXiv  Detail & Related papers  (2024-05-21T13:19:10Z)
• Large Language Models as Test Case Generators: Performance Evaluation and Enhancement [3.5398126682962587]
  We study how well Large Language Models can generate high-quality test cases. We propose a multi-agent framework called emphTestChain that decouples the generation of test inputs and test outputs. Our results indicate that TestChain outperforms the baseline by a large margin.
  arXiv  Detail & Related papers  (2024-04-20T10:27:01Z)
• Observation-based unit test generation at Meta [52.4716552057909]
  TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution. TestGen has landed 518 tests into production, which have been executed 9,617,349 times in continuous integration, finding 5,702 faults. Our evaluation reveals that, when carving its observations from 4,361 reliable end-to-end tests, TestGen was able to generate tests for at least 86% of the classes covered by end-to-end tests.
  arXiv  Detail & Related papers  (2024-02-09T00:34:39Z)
• Code-Aware Prompting: A study of Coverage Guided Test Generation in Regression Setting using LLM [32.44432906540792]
  We present SymPrompt, a code-aware prompting strategy for large language models in test generation. SymPrompt enhances correct test generations by a factor of 5 and bolsters relative coverage by 26% for CodeGen2. Notably, when applied to GPT-4, SymPrompt improves coverage by over 2x compared to baseline prompting strategies.
  arXiv  Detail & Related papers  (2024-01-31T18:21:49Z)
• Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing [13.743062498008555]
  We introduce MuTAP for improving the effectiveness of test cases generated by Large Language Models (LLMs) in terms of revealing bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs) Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets.
  arXiv  Detail & Related papers  (2023-08-31T08:48:31Z)
• 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)

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.