Graph-based learning for automated code checking – Exploring the application of graph neural networks for design review

Although automated code checking (ACC) has been a subject of interest for many years, we have not yet seen significant breakthroughs in the field that may lead to the development of generic, comprehensive tools for ACC. Hard-coded rules are the backbone of all emerging platforms for ACC. These rules require a significant amount of engineering, which often requires manual labor; and the resulting rule sets are strict and difficult to scale to other building models. On the other hand, approaches relying purely on classic machine learning (e.g. SVM) are too coarse and unable to accurately express building information. In our hope to come up with a more scalable solution, we investigate here a novel workflow that relies on graph-based learning algorithms instead of processing rule sets. We illustrate the suggested workflow by checking accessibility requirements in residential houses, which we believe is one of the more promising rule sets that can be checked using graph-based learning methods. The high accuracy of the obtained results is encouraging to continue exploring Graph Neural Networks (GNN) for this type of ACC, yet rule-based and classic ML-based approaches show other advantages as well (rigor and speed, respectively). The main contribution of this work is therefore its identification of meaningful limitations and directions for future research, including alternative graph structures and GNN architectures.