Quality Assessment of Python Tests Generated by Large Language Models

• URL: http://arxiv.org/abs/2506.14297v1
• Date: Tue, 17 Jun 2025 08:16:15 GMT
• Title: Quality Assessment of Python Tests Generated by Large Language Models
• Authors: Victor Alves, Carla Bezerra, Ivan Machado, Larissa Rocha, Tássio Virgínio, Publio Silva,
• Abstract summary: This study investigates the quality of Python test code generated by three Large Language Models: GPT-4o, Amazon Q, and LLama 3.3.<n>We evaluate the structural reliability of test suites generated under two distinct prompt contexts: Text2Code (T2C) and Code2Code (C2C)
• Score: 1.0845500038686533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The manual generation of test scripts is a time-intensive, costly, and error-prone process, indicating the value of automated solutions. Large Language Models (LLMs) have shown great promise in this domain, leveraging their extensive knowledge to produce test code more efficiently. This study investigates the quality of Python test code generated by three LLMs: GPT-4o, Amazon Q, and LLama 3.3. We evaluate the structural reliability of test suites generated under two distinct prompt contexts: Text2Code (T2C) and Code2Code (C2C). Our analysis includes the identification of errors and test smells, with a focus on correlating these issues to inadequate design patterns. Our findings reveal that most test suites generated by the LLMs contained at least one error or test smell. Assertion errors were the most common, comprising 64% of all identified errors, while the test smell Lack of Cohesion of Test Cases was the most frequently detected (41%). Prompt context significantly influenced test quality; textual prompts with detailed instructions often yielded tests with fewer errors but a higher incidence of test smells. Among the evaluated LLMs, GPT-4o produced the fewest errors in both contexts (10% in C2C and 6% in T2C), whereas Amazon Q had the highest error rates (19% in C2C and 28% in T2C). For test smells, Amazon Q had fewer detections in the C2C context (9%), while LLama 3.3 performed best in the T2C context (10%). Additionally, we observed a strong relationship between specific errors, such as assertion or indentation issues, and test case cohesion smells. These findings demonstrate opportunities for improving the quality of test generation by LLMs and highlight the need for future research to explore optimized generation scenarios and better prompt engineering strategies.

Related papers

• 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)
• Investigating the Performance of Small Language Models in Detecting Test Smells in Manual Test Cases [8.275680062883216]
  This study investigates the potential of Small Language Models (SLMs) for automatically detecting test smells.<n>We evaluate Gemma3, Llama3.2, and Phi-4 on 143 real-world Ubuntu test cases, covering seven types of test smells.
  arXiv  Detail & Related papers  (2025-07-17T12:06:29Z)
• Test smells in LLM-Generated Unit Tests [11.517293765116307]
  This study explores the diffusion of test smells in Large Language Models generated unit test suites. We analyze a benchmark of 20,500 LLM-generated test suites produced by four models across five prompt engineering techniques. We identify and analyze the prevalence and co-occurrence of various test smells in both human written and LLM-generated test suites.
  arXiv  Detail & Related papers  (2024-10-14T15:35:44Z)
• Training on the Benchmark Is Not All You Need [52.01920740114261]
  We propose a simple and effective data leakage detection method based on the contents of multiple-choice options.<n>Our method is able to work under gray-box conditions without access to model training data or weights.<n>We evaluate the degree of data leakage of 35 mainstream open-source LLMs on four benchmark datasets.
  arXiv  Detail & Related papers  (2024-09-03T11:09:44Z)
• TestART: Improving LLM-based Unit Testing via Co-evolution of Automated Generation and Repair Iteration [7.509927117191286]
  Large language models (LLMs) have demonstrated remarkable capabilities in generating unit test cases.<n>We propose TestART, a novel unit test generation method.<n>TestART improves LLM-based unit testing via co-evolution of automated generation and repair iteration.
  arXiv  Detail & Related papers  (2024-08-06T10:52:41Z)
• Large-scale, Independent and Comprehensive study of the power of LLMs for test case generation [11.517293765116307]
  Unit testing is essential for software reliability, yet manual test creation is time-consuming and often neglected.<n>This study presents the first large-scale empirical evaluation of LLM-generated unit tests at the class level.
  arXiv  Detail & Related papers  (2024-06-28T20:38:41Z)
• GPT-HateCheck: Can LLMs Write Better Functional Tests for Hate Speech Detection? [50.53312866647302]
  HateCheck is a suite for testing fine-grained model functionalities on synthesized data. We propose GPT-HateCheck, a framework to generate more diverse and realistic functional tests from scratch. Crowd-sourced annotation demonstrates that the generated test cases are of high quality.
  arXiv  Detail & Related papers  (2024-02-23T10:02:01Z)
• DebugBench: Evaluating Debugging Capability of Large Language Models [80.73121177868357]
  DebugBench is a benchmark for Large Language Models (LLMs) It covers four major bug categories and 18 minor types in C++, Java, and Python. We evaluate two commercial and four open-source models in a zero-shot scenario.
  arXiv  Detail & Related papers  (2024-01-09T15:46:38Z)
• The Earth is Flat? Unveiling Factual Errors in Large Language Models [89.94270049334479]
  Large Language Models (LLMs) like ChatGPT are in various applications due to their extensive knowledge from pre-training and fine-tuning. Despite this, they are prone to generating factual and commonsense errors, raising concerns in critical areas like healthcare, journalism, and education. We introduce a novel, automatic testing framework, FactChecker, aimed at uncovering factual inaccuracies in LLMs.
  arXiv  Detail & Related papers  (2024-01-01T14:02:27Z)
• 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)
• CodeT: Code Generation with Generated Tests [49.622590050797236]
  We explore the use of pre-trained language models to automatically generate test cases. CodeT executes the code solutions using the generated test cases, and then chooses the best solution. We evaluate CodeT on five different pre-trained models with both HumanEval and MBPP benchmarks.
  arXiv  Detail & Related papers  (2022-07-21T10:18:37Z)
• TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task [80.38130122127882]
  TACRED is one of the largest, most widely used crowdsourced datasets in Relation Extraction (RE) In this paper, we investigate the questions: Have we reached a performance ceiling or is there still room for improvement? We find that label errors account for 8% absolute F1 test error, and that more than 50% of the examples need to be relabeled.
  arXiv  Detail & Related papers  (2020-04-30T15:07:37Z)

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.