Large Language Models in Software Documentation and Modeling: A Literature Review and Findings

TL;DR

This paper reviews how large language models are used in software documentation and modeling, highlighting their capabilities, common techniques, and evaluation methods in recent literature.

Contribution

It provides a comprehensive literature review on the application of large language models in software engineering tasks related to documentation and modeling, including analysis of techniques and datasets.

Findings

Large language models are effectively used for processing software documentation.

They demonstrate potential in understanding and generating structured software artifacts.

The review identifies common prompt techniques and evaluation metrics.

Abstract

Generative artificial intelligence attracts significant attention, especially with the introduction of large language models. Its capabilities are being exploited to solve various software engineering tasks. Thanks to their ability to understand natural language and generate natural language responses, large language models are great for processing various software documentation artifacts. At the same time, large language models excel at understanding structured languages, having the potential for working with software programs and models. We conduct a literature review on the usage of large language models for software engineering tasks related to documentation and modeling. We analyze articles from four major venues in the area, organize them per tasks they solve, and provide an overview of used prompt techniques, metrics, approaches to human-based evaluation, and major datasets.