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

2026-02-04

AI Summary

This study addresses the lack of a systematic review on the application of large language models (LLMs) in software engineering documentation and modeling tasks. Through a comprehensive literature survey, it establishes a multi-dimensional taxonomy that categorizes existing research by task type, offering an in-depth analysis of key technical approaches—including prompt engineering, natural language understanding, and structured language processing. The work further synthesizes the distribution of tasks, evaluation metrics, human assessment methodologies, and commonly used datasets across major conferences in the field. By systematically mapping the research landscape and identifying prevailing technical trends, this paper provides a thorough reference and strategic guidance for future investigations at the intersection of LLMs and software engineering.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsCognitive Modeling & Cognitive Systems: Applications

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactionsSearch and Retrieval-Augmented AI: Large language models for search

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.

Problem

• Large Language Models
• Software Documentation
• Software Modeling
• Generative AI
• Software Engineering

Innovation

• Large Language Models
• Software Documentation
• Software Modeling
• Prompt Engineering
• Literature Review