<p align="left">Risk management is vital to a product’s lifecycle. The current practice of reducing risks relies on domain experts or management tools to identify unexpected engineering changes, where such approaches are prone to human errors and laborious operations. However, This study presents a framework to contribute to requirements management by implementing a generative probabilistic model, the latent Dirichlet allocation (LDA) with collapsed Gibbs sampling (CGS), to discover topics from three heterogeneous industrial requirements. The goal of this study is to explore the optimal number of topics and develop interpretable visualizations for generalizing given requirements documents. Therefore, designers can improve design efficiency by understanding, organizing, and analyzing high-volume requirements documents based on topics across different domains during a reengineering process. As finding the number of topics remains an open-ended question, a case study estimates an appropriate number of topics to represent each requirements document based on perplexity values. Further, a relevance measurement provides the flexibility to improve the quality of topics. The results demonstrate that the LDA can capture a different level of design details by varying the number of topics. Based on domain knowledge and purposive interpretation of topics, designers can make informed decisions on product evolution and mitigate the risks of unexpected engineering changes across domains.</p>

A Topic Modeling Approach to Study Design Requirements

Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2021/volume/85369

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