Unit Testing Past vs. Present: Examining LLMs' Impact on Defect Detection and Efficiency

• URL: http://arxiv.org/abs/2502.09801v1
• Date: Thu, 13 Feb 2025 22:27:55 GMT
• Title: Unit Testing Past vs. Present: Examining LLMs' Impact on Defect Detection and Efficiency
• Authors: Rudolf Ramler, Philipp Straubinger, Reinhold Plösch, Dietmar Winkler,
• Abstract summary: The integration of Large Language Models (LLMs) into software engineering has shown potential to enhance productivity. This paper investigates whether LLM support improves defect detection effectiveness during unit testing.
• Score: 2.4936576553283283
• License:
• Abstract: The integration of Large Language Models (LLMs), such as ChatGPT and GitHub Copilot, into software engineering workflows has shown potential to enhance productivity, particularly in software testing. This paper investigates whether LLM support improves defect detection effectiveness during unit testing. Building on prior studies comparing manual and tool-supported testing, we replicated and extended an experiment where participants wrote unit tests for a Java-based system with seeded defects within a time-boxed session, supported by LLMs. Comparing LLM supported and manual testing, results show that LLM support significantly increases the number of unit tests generated, defect detection rates, and overall testing efficiency. These findings highlight the potential of LLMs to improve testing and defect detection outcomes, providing empirical insights into their practical application in software testing.

Related papers

• 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)
• A Large-scale Empirical Study on Fine-tuning Large Language Models for Unit Testing [8.22619177301814]
  Large Language Models (LLMs) have shown potential in various unit testing tasks. We present a large-scale empirical study on fine-tuning LLMs for unit testing.
  arXiv  Detail & Related papers  (2024-12-21T13:28:11Z)
• 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)
• AutoPT: How Far Are We from the End2End Automated Web Penetration Testing? [54.65079443902714]
  We introduce AutoPT, an automated penetration testing agent based on the principle of PSM driven by LLMs. Our results show that AutoPT outperforms the baseline framework ReAct on the GPT-4o mini model.
  arXiv  Detail & Related papers  (2024-11-02T13:24:30Z)
• Learning to Ask: When LLM Agents Meet Unclear Instruction [55.65312637965779]
  Large language models (LLMs) can leverage external tools for addressing a range of tasks unattainable through language skills alone. We evaluate the performance of LLMs tool-use under imperfect instructions, analyze the error patterns, and build a challenging tool-use benchmark called Noisy ToolBench. We propose a novel framework, Ask-when-Needed (AwN), which prompts LLMs to ask questions to users whenever they encounter obstacles due to unclear instructions.
  arXiv  Detail & Related papers  (2024-08-31T23:06:12Z)
• 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)
• 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)
• 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)
• ChatGPT vs SBST: A Comparative Assessment of Unit Test Suite Generation [25.200080365022153]
  We present a systematic comparison of test suites generated by the ChatGPT LLM and the state-of-the-art SBST tool EvoSuite. Our comparison is based on several critical factors, including correctness, readability, code coverage, and bug detection capability.
  arXiv  Detail & Related papers  (2023-07-02T15:09:40Z)
• 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.