大型语言模型在测试用例构建中的应用案例研究：揭示实践见解与挑战
A Case Study on Test Case Construction with Large Language Models: Unveiling Practical Insights and Challenges
AI总结：
本研究通过案例研究，系统评估了大型语言模型（LLMs）在软件测试用例构建中的实际效能，揭示了其在提升全面性、准确性和效率方面的潜力，同时指出了可解释性和适应性等挑战，为软件质量保证实践提供了见解。
摘要：
本文通过详细的案例研究，探讨了在软件工程背景下，大型语言模型（LLMs）在测试用例构建中的应用。LLMs以其先进的自然语言处理能力为特点，作为自动化和增强软件开发生命周期各个方面的工具，正日益受到关注。采用案例研究方法，我们系统地探索了LLMs在测试用例构建过程中的集成，旨在揭示其实际效能、遇到的挑战以及对软件质量保证的影响。研究涵盖了代表性软件应用程序的选择、采用LLMs的测试用例构建方法的制定以及后续结果的评估。通过定性与定量分析的结合，本研究评估了LLMs对测试用例全面性、准确性和效率的影响。此外，深入探讨了模型可解释性和对不同软件环境的适应性等挑战。本案例研究的发现为LLMs在测试用例构建领域的实际效用提供了细致入微的见解，阐明了其潜在优势和局限性。通过解决现实世界的场景和复杂性，本研究旨在让软件从业者和研究人员了解将LLMs纳入软件测试领域的实际影响，促进对其在优化软件开发过程中作用的更全面理解。
英文摘要：
This paper presents a detailed case study examining the application of Large Language Models (LLMs) in the construction of test cases within the context of software engineering. LLMs, characterized by their advanced natural language processing capabilities, are increasingly garnering attention as tools to automate and enhance various aspects of the software development life cycle. Leveraging a case study methodology, we systematically explore the integration of LLMs in the test case construction process, aiming to shed light on their practical efficacy, challenges encountered, and implications for software quality assurance. The study encompasses the selection of a representative software application, the formulation of test case construction methodologies employing LLMs, and the subsequent evaluation of outcomes. Through a blend of qualitative and quantitative analyses, this study assesses the impact of LLMs on test case comprehensiveness, accuracy, and efficiency. Additionally, delves into challenges such as model interpretability and adaptation to diverse software contexts. The findings from this case study contributes with nuanced insights into the practical utility of LLMs in the domain of test case construction, elucidating their potential benefits and limitations. By addressing real-world scenarios and complexities, this research aims to inform software practitioners and researchers alike about the tangible implications of incorporating LLMs into the software testing landscape, fostering a more comprehensive understanding of their role in optimizing the software development process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
