A deep learning-based approach to similarity calculation for UML use case models

Unified Modeling Language (UML) models are main artifacts of software applications and valuable assets of software projects. The similarity calculation of UML models is an effective way to model reuse, model validation, model retrieval and pattern detection, which can help developers reduce development cost. Existing studies mainly focus on UML design models such as class diagrams and object diagrams. Yet, there is little research on calculating similarity for use case models despite their importance for software requirements reuse and validation. Thus, this paper proposes a similarity calculation approach for UML use case models. This approach first transforms the use case models into use case graphs and then adopts the deep learning technique – Similarity Graph Neural Network (SimGNN) to calculate the structural similarity between use case graphs. Then it calculates the semantic similarity of use case models with Term Frequency-Inverse Document Frequency (TF-IDF) and Cosine similarity. Finally it syntheses the results of semantic similarity and structural similarity of use case graphs. The effectiveness and efficiency of this approach was evaluated by comparing to two other approaches: (1) Cosine similarity+TF-IDF and (2) Cosine similarity+edit distance. The results showed that the our approach has better performance in terms of both effectiveness and efficiency.