Using LLMs in Software Requirements Specifications: An Empirical Evaluation

TL;DR

This study evaluates GPT-4 and CodeLlama's effectiveness in generating, validating, and improving Software Requirements Specifications, showing that LLMs can match entry-level engineers in quality and significantly reduce development time.

Contribution

It provides an empirical assessment of LLMs in SRS creation, validation, and correction, demonstrating their potential to enhance software engineering productivity.

Findings

LLMs can produce SRS comparable to entry-level engineers.

GPT-4 effectively identifies issues and offers constructive feedback.

Using LLMs reduces time required for SRS development.

Abstract

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.