The creation of a Software Requirements Specification (SRS) document is
important for any software development project. Given the recent prowess of
Large Language Models (LLMs) in answering natural language queries and
generating sophisticated textual outputs, our study explores their capability
to produce accurate, coherent, and structured drafts of these documents to
accelerate the software development lifecycle. We assess the performance of
GPT-4 and CodeLlama in drafting an SRS for a university club management system
and compare it against human benchmarks using eight distinct criteria. Our
results suggest that LLMs can match the output quality of an entry-level
software engineer to generate an SRS, delivering complete and consistent
drafts. We also evaluate the capabilities of LLMs to identify and rectify
problems in a given requirements document. Our experiments indicate that GPT-4
is capable of identifying issues and giving constructive feedback for
rectifying them, while CodeLlama's results for validation were not as
encouraging. We repeated the generation exercise for four distinct use cases to
study the time saved by employing LLMs for SRS generation. The experiment
demonstrates that LLMs may facilitate a significant reduction in development
time for entry-level software engineers. Hence, we conclude that the LLMs can
be gainfully used by software engineers to increase productivity by saving time
and effort in generating, validating and rectifying software requirements.

使用大型语言模型（LLMs）如 GPT-4 和 CodeLlama 来加快软件开发生命周期，通过比较人工标准，评估其在生成软件需求规范（SRS）文档方面的性能；结果表明，LLMs 的输出质量可以与初级软件工程师相媲美，生成完整一致的 SRS 文档，并且还能识别和纠正问题，从而节省开发时间。