使用 GPT-3.5 的软件需求工程自动化模型

Jie Sh'ng Yeow, Muhammad Ehsan Rana, Nur Amira Abdul Majid
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引用次数: 0

摘要

虽然人工智能在软件开发中的潜力毋庸置疑,但将 GPT-3.5 等先进模型集成到需求工程等核心流程中,在很大程度上仍有待探索。本研究调查了 GPT-3.5 在自动化软件需求工程关键任务中的有效性。主要目标是全面探索 GPT-3.5 在软件需求工程中的功能、局限性和潜在应用。随后,研究将进行全面分析和评估,以深入了解 GPT-3.5 在需求收集过程中的优势和局限性。研究最后指出了局限性,并为今后将 GPT-3.5 整合到软件需求工程流程中的研究工作提出了建议。虽然 GPT-3.5 在创造性原型设计和问题生成等方面表现出色,但在领域理解和上下文感知等方面的局限性也很明显。通过概述这些局限性,作者为今后的研究提出了具体建议,重点是将 GPT-3.5 和类似模型无缝集成到更广泛的软件需求工程框架中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Automated Model of Software Requirement Engineering Using GPT-3.5
While the potential of AI in software development is undeniable, integrating advanced models like GPT-3.5 into its core processes like requirements engineering remains largely unexplored. This research investigates the effectiveness of GPT-3.5 in automating key tasks within software requirements engineering. The primary objective is to comprehensively explore the capabilities, limitations, and potential applications of GPT-3.5 in software requirements engineering. Subsequently, the research undergoes thorough analysis and evaluation to gather insights into the strengths and limitations of GPT-3.5 in the requirement-gathering process. The research concludes by identifying the limitations and putting forth recommendations for future research endeavours aimed at integrating GPT-3.5 into software requirement engineering processes. While GPT-3.5 demonstrates proficiency in aspects like creative prototyping and question generation, limitations in areas like domain understanding and context awareness become evident. By outlining these limitations, the authors offer concrete recommendations for future research focusing on the seamless integration of GPT-3.5 and similar models into the broader framework of software requirements engineering.
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