Friday 21 March 2025
Software developers often struggle to preserve knowledge about how their code works, making it difficult for others to understand and maintain the system over time. This can lead to a loss of expertise as key team members leave or move on to other projects.
One approach to addressing this issue is to use architecture patterns, which are reusable designs that describe how software components interact with each other. However, manually documenting these patterns can be a time-consuming and laborious task. Now, researchers have developed an automated tool that uses large language models to detect microservice pattern instances in infrastructure-as-code artifacts.
The new approach relies on the idea that large language models have been trained on vast amounts of text data and can recognize patterns and relationships between words. By feeding these models with natural language descriptions of architecture patterns, they can learn to identify instances of those patterns in software code.
The researchers tested their tool, called MicroPAD, by analyzing 22 GitHub projects and comparing its results with manual verification. They found that MicroPAD was able to detect microservice pattern instances with high accuracy, and the costs of detecting these instances were minimal.
One of the key benefits of this approach is that it allows developers to focus on writing code rather than documenting their work. The tool can automatically generate documentation about the architecture patterns used in a project, making it easier for others to understand and maintain the system over time.
The researchers also noted that MicroPAD’s use of large language models could help democratize access to software architecture knowledge by reducing the technical expertise required to detect pattern instances. This could be particularly useful for smaller development teams or those with limited resources.
However, there are still challenges to overcome before this technology can be widely adopted. For example, the accuracy of MicroPAD’s results may depend on the quality of the infrastructure-as-code artifacts being analyzed, and further research is needed to understand how the tool performs on a larger scale.
Despite these limitations, the potential benefits of automated software architecture documentation are significant. As software systems become increasingly complex, it will be essential to have tools that can help developers preserve knowledge about their code and make it easier for others to maintain and extend their work over time.
Cite this article: “Automated Software Architecture Documentation Using Large Language Models”, The Science Archive, 2025.
Software Development, Architecture Patterns, Large Language Models, Automation, Documentation, Microservices, Infrastructure-As-Code, Github Projects, Technical Expertise, Democratization.
