Extracting functional requirements from design documentation using machine learning

Abstract

Good design practice and digital tools have enabled industry to produce valuable products. Early-stage design research involves rigorous background study of large volumes of design documentation which designers must analyze manually, to extract functional requirements which are abstracted and prioritized to guide a design. Recent advances in Machine Learning, specifically Natural Language Processing (NLP), can be applied to enhance the time-consuming and difficult practice of the human designer by performing tasks such as extracting functional requirements from long-form written documentation. This work demonstrates how extractive question-answering by neural networks can be applied to design as a tool for automating this initial step in the design process. We applied the language model BERT, fine-tuned on question-answering, to identify functional requirements in written documentation. Limitations due to wording sensitivity are discussed and an outline for training a design-specific model is discussed with a MEMS product design case. This work presents how this application of AI to design could enhance the work of human designers using the power of computing, which will open the door for learning from big data of past product designs by allowing machines to "read" them.