From word embeddings to text similarities for improved semantic clustering of functional requirements

Abstract

Requirements engineering starts by requirements elicitation which consists in gathering software requirements from stakeholders. Then, the elicited requirements are usually manually recorded in a requirements specification document. In recent years, modern software projects are becoming more complex than projects of the past due to the increase in the number of requirements and stakeholders involved in a project. Thus, manually managing requirements becomes a tedious, time consuming and error-prone task. One historical strategy to manage this kind of complexity is “divide to conquer”, meaning to categorize them into groups in order to breakdown the system into a set of smallest sub-systems at early stages. In this paper, we propose an approach to automatically cluster functional requirements based on their semantic similarity which is the usual strategy used by system architects to define sub-systems candidate to simplification of the original problem. First, we use word2vec, as a predictive word embedding model to compute the word-level similarity. Second, we derive the requirement-level similarity using a scoring function for text similarity. Third, we adopt hierarchical clustering to group the requirements. Experimental results performed on four open-access software projects show that our approach succeeded to improve the results of clusters identification compared with existing studies.