SupportingProactive Reuse by Identifying EquivalentRequirements

Abstract

Though very important in software engineering, linking artifacts of the same type (clone detection) or of different types (traceability recovery) is extremely tedious, error-prone and requires significant effort. Past research focused on supporting analysts with mechanisms based on Natural Language Processing (NLP) to identify candidate links. Because a plethora of NLP techniques exists, and their performances vary among contexts, it is important to characterize them according to the provided level of support. The aim of this paper is to characterize a comprehensive set of NLP techniques according to the provided level of support to the human analyst in detecting equivalent requirements. The characterization consists on a case study, featuring real requirements, in the context in which SELEX Sistemi Integrati is involved, i.e. the defense and aerospace domain. The major result from the case study is that simple NLP are more precise than complex ones