Formal Software Architecture Rule Learning: A Comparative Investigation between Large Language Models and Inductive Techniques

Abstract

This paper explores the application of inferring software architecture rules from examples using Machine Learning (ML). We investigate different methods from Inductive Rule Learning and utilize Large Language Models (LLMs). Traditional manual rule specification approaches are time-consuming and error-prone, motivating the need for automated rule discovery. Leveraging a dataset of software architecture instances and a meta-model capturing implementation facts, we used inductive learning algorithms and LLMs to extract meaningful rules. The induced rules are evaluated against a predefined hypothesis and their generalizability across different system subsets is investigated. The research highlights the capabilities and limitations of ML-based rule learning in the area of software architecture, aiming to inspire further innovation in data-driven rule discovery for more intelligent software architecture practices.