Requirements Similarity and Retrieval

Requirement Engineering (RE) is crucial for identifying, analyzing, and documenting stakeholders' needs and constraints for developing software systems. In most safety-critical domains, maintaining requirements and their links to other artifacts is also often required by regulatory bodies. Furthermore, in such contexts, requirements for new products often share similarities with previous existing projects performed by the company. Therefore, similar requirements can be retrieved to facilitate the feasibility analysis of new projects. In addition, when a new customer requests a new product, retrieval of similar requirements can enable requirements-driven software reuse and avoid redundant development efforts. Manually retrieving similar requirements for reuse is typically dependent on the engineer's experience and is not scalable, as the set could be quite large. In this regard, applying Natural Language Processing (NLP) techniques for automated similarity computation and retrieval ensures the independence of the process from the human experience and makes the process scalable. This chapter introduces linguistic similarity and several NLP-based similarity computation techniques that leverage linguistic features for similarity computation. Specifically, we cover techniques for computing similarity ranging from lexical to state-of-the-art deep neural network-based methods. We demonstrate their application in two example cases: a) requirements reuse and b) requirements-driven software retrieval. The practical guidance and example cases presented in the chapter can help practitioners apply the concepts to improve their processes where similarity computation is relevant.