No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation

• URL: http://arxiv.org/abs/2305.04207v3
• Date: Sun, 19 May 2024 08:51:14 GMT
• Title: No More Manual Tests? Evaluating and Improving ChatGPT for Unit Test Generation
• Authors: Zhiqiang Yuan, Yiling Lou, Mingwei Liu, Shiji Ding, Kaixin Wang, Yixuan Chen, Xin Peng,
• Abstract summary: Unit testing is essential in detecting bugs in functionally-discrete program units. Recent work has shown the large potential of large language models (LLMs) in unit test generation. It remains unclear how effective ChatGPT is in unit test generation.
• Score: 11.009117714870527
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unit testing is essential in detecting bugs in functionally-discrete program units. Manually writing high-quality unit tests is time-consuming and laborious. Although traditional techniques can generate tests with reasonable coverage, they exhibit low readability and cannot be directly adopted by developers. Recent work has shown the large potential of large language models (LLMs) in unit test generation, which can generate more human-like and meaningful test code. ChatGPT, the latest LLM incorporating instruction tuning and reinforcement learning, has performed well in various domains. However, It remains unclear how effective ChatGPT is in unit test generation. In this work, we perform the first empirical study to evaluate ChatGPT's capability of unit test generation. Specifically, we conduct a quantitative analysis and a user study to systematically investigate the quality of its generated tests regarding the correctness, sufficiency, readability, and usability. The tests generated by ChatGPT still suffer from correctness issues, including diverse compilation errors and execution failures. Still, the passing tests generated by ChatGPT resemble manually-written tests by achieving comparable coverage, readability, and even sometimes developers' preference. Our findings indicate that generating unit tests with ChatGPT could be very promising if the correctness of its generated tests could be further improved. Inspired by our findings above, we propose ChatTESTER, a novel ChatGPT-based unit test generation approach, which leverages ChatGPT itself to improve the quality of its generated tests. ChatTESTER incorporates an initial test generator and an iterative test refiner. Our evaluation demonstrates the effectiveness of ChatTESTER by generating 34.3% more compilable tests and 18.7% more tests with correct assertions than the default ChatGPT.

Related papers

• 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)
• 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)
• ChatGPT and Human Synergy in Black-Box Testing: A Comparative Analysis [0.0]
  ChatGPT can generate test cases that generally match or slightly surpass those created by human participants. When ChatGPT cooperates with humans, it can cover considerably more test viewpoints than each can achieve alone.
  arXiv  Detail & Related papers  (2024-01-25T03:42:17Z)
• Unit Test Generation using Generative AI : A Comparative Performance Analysis of Autogeneration Tools [2.0686733932673604]
  This research aims to experimentally investigate the effectiveness of Large Language Models (LLMs) for generating unit test scripts for Python programs. For experiments, we consider three types of code units: 1) Procedural scripts, 2) Function-based modular code, and 3) Class-based code. Our results show that ChatGPT's performance is comparable with Pynguin in terms of coverage, though for some cases its performance is superior to Pynguin.
  arXiv  Detail & Related papers  (2023-12-17T06:38:11Z)
• 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)
• ChatUniTest: A Framework for LLM-Based Test Generation [17.296369651892228]
  This paper presents ChatUniTest, an automated unit test generation framework. ChatUniTest incorporates an adaptive focal context mechanism to encompass valuable context in prompts. Our effectiveness evaluation reveals that ChatUniTest outperforms TestSpark and EvoSuite in half of the evaluated projects.
  arXiv  Detail & Related papers  (2023-05-08T15:12:07Z)
• ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Time [54.18651663847874]
  ChatGPT has achieved great success and can be considered to have acquired an infrastructural status. Existing benchmarks encounter two challenges: (1) Disregard for periodical evaluation and (2) Lack of fine-grained features. We construct ChatLog, an ever-updating dataset with large-scale records of diverse long-form ChatGPT responses for 21 NLP benchmarks from March, 2023 to now.
  arXiv  Detail & Related papers  (2023-04-27T11:33:48Z)
• To ChatGPT, or not to ChatGPT: That is the question! [78.407861566006]
  This study provides a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection. We have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains. Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.
  arXiv  Detail & Related papers  (2023-04-04T03:04:28Z)
• Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT [103.57103957631067]
  ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. We evaluate ChatGPT's understanding ability by evaluating it on the most popular GLUE benchmark, and comparing it with 4 representative fine-tuned BERT-style models. We find that: 1) ChatGPT falls short in handling paraphrase and similarity tasks; 2) ChatGPT outperforms all BERT models on inference tasks by a large margin; 3) ChatGPT achieves comparable performance compared with BERT on sentiment analysis and question answering tasks.
  arXiv  Detail & Related papers  (2023-02-19T12:29:33Z)
• BiasTestGPT: Using ChatGPT for Social Bias Testing of Language Models [73.29106813131818]
  bias testing is currently cumbersome since the test sentences are generated from a limited set of manual templates or need expensive crowd-sourcing. We propose using ChatGPT for the controllable generation of test sentences, given any arbitrary user-specified combination of social groups and attributes. We present an open-source comprehensive bias testing framework (BiasTestGPT), hosted on HuggingFace, that can be plugged into any open-source PLM for bias testing.
  arXiv  Detail & Related papers  (2023-02-14T22:07:57Z)
• Is ChatGPT a General-Purpose Natural Language Processing Task Solver? [113.22611481694825]
  Large language models (LLMs) have demonstrated the ability to perform a variety of natural language processing (NLP) tasks zero-shot. Recently, the debut of ChatGPT has drawn a great deal of attention from the natural language processing (NLP) community. It is not yet known whether ChatGPT can serve as a generalist model that can perform many NLP tasks zero-shot.
  arXiv  Detail & Related papers  (2023-02-08T09:44:51Z)

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.