Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing

• URL: http://arxiv.org/abs/2308.16557v1
• Date: Thu, 31 Aug 2023 08:48:31 GMT
• Title: Effective Test Generation Using Pre-trained Large Language Models and Mutation Testing
• Authors: Arghavan Moradi Dakhel, Amin Nikanjam, Vahid Majdinasab, Foutse Khomh, Michel C. Desmarais
• Abstract summary: 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.
• Score: 13.743062498008555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the critical phases in software development is software testing. Testing helps with identifying potential bugs and reducing maintenance costs. The goal of automated test generation tools is to ease the development of tests by suggesting efficient bug-revealing tests. Recently, researchers have leveraged Large Language Models (LLMs) of code to generate unit tests. While the code coverage of generated tests was usually assessed, the literature has acknowledged that the coverage is weakly correlated with the efficiency of tests in bug detection. To improve over this limitation, in this paper, we introduce MuTAP for improving the effectiveness of test cases generated by LLMs in terms of revealing bugs by leveraging mutation testing. Our goal is achieved by augmenting prompts with surviving mutants, as those mutants highlight the limitations of test cases in detecting bugs. MuTAP is capable of generating effective test cases in the absence of natural language descriptions of the Program Under Test (PUTs). We employ different LLMs within MuTAP and evaluate their performance on different benchmarks. Our results show that our proposed method is able to detect up to 28% more faulty human-written code snippets. Among these, 17% remained undetected by both the current state-of-the-art fully automated test generation tool (i.e., Pynguin) and zero-shot/few-shot learning approaches on LLMs. Furthermore, MuTAP achieves a Mutation Score (MS) of 93.57% on synthetic buggy code, outperforming all other approaches in our evaluation. Our findings suggest that although LLMs can serve as a useful tool to generate test cases, they require specific post-processing steps to enhance the effectiveness of the generated test cases which may suffer from syntactic or functional errors and may be ineffective in detecting certain types of bugs and testing corner cases PUTs.

Related papers

• Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests [4.574205608859157]
  We introduce UTGen, which combines search-based software testing and large language models to enhance the understandability of automatically generated test cases. We observe that participants working on assignments with UTGen test cases fix up to 33% more bugs and use up to 20% less time when compared to baseline test cases.
  arXiv  Detail & Related papers  (2024-08-21T15:35:34Z)
• 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 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)
• 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)
• 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)
• Automatic Generation of Test Cases based on Bug Reports: a Feasibility Study with Large Language Models [4.318319522015101]
  Existing approaches produce test cases that either can be qualified as simple (e.g. unit tests) or that require precise specifications. Most testing procedures still rely on test cases written by humans to form test suites. We investigate the feasibility of performing this generation by leveraging large language models (LLMs) and using bug reports as inputs.
  arXiv  Detail & Related papers  (2023-10-10T05:30:12Z)
• 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)
• Large Language Models are Few-shot Testers: Exploring LLM-based General Bug Reproduction [14.444294152595429]
  The number of tests added in open source repositories due to issues was about 28% of the corresponding project test suite size. We propose LIBRO, a framework that uses Large Language Models (LLMs), which have been shown to be capable of performing code-related tasks. Our evaluation of LIBRO shows that, on the widely studied Defects4J benchmark, LIBRO can generate failure reproducing test cases for 33% of all studied cases.
  arXiv  Detail & Related papers  (2022-09-23T10:50:47Z)
• Beyond Accuracy: Behavioral Testing of NLP models with CheckList [66.42971817954806]
  CheckList is a task-agnostic methodology for testing NLP models. CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation. In a user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.
  arXiv  Detail & Related papers  (2020-05-08T15:48:31Z)
• Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design [63.48989885374238]
  When the infection prevalence of a disease is low, Dorfman showed 80 years ago that testing groups of people can prove more efficient than testing people individually. Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting.
  arXiv  Detail & Related papers  (2020-04-26T23:41:33Z)

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.