|Authors||D. Falessi, G. Cantone and G. Canfora|
|Editors||G. Succi, M. Morisio and N. Nagappan|
|Title||A Comprehensive Characterization of NLP Techniques for Identifying Equivalent Requirements|
|Afilliation||Software Engineering, Software Engineering|
|Publication Type||Proceedings, refereed|
|Year of Publication||2010|
|Conference Name||International Symposium on Empirical Software Engineering and Measurement (ESEM 2010)|
[Context] 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. [Objective] 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. [Method] The characterization consists on a case study, featuring real requirements, in the context of an Italian company in the defense and aerospace domain. [Results] The major result from the case study is that simple NLP are more precise than complex ones.