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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title What Works Better? A Study of Classifying Requirements
Organization Unit
  • Parisa Ghazi
  • Martin Glinz
  • Zahra Shakeri Hossein abad
  • Oliver Karras
  • Guenther Ruhe
  • Kurt Schneider
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published electronically before print/final form (Epub ahead of print)
  • English
Event Title 25th IEEE International Requirements Engineering Conference (RE'17)
Event Type conference
Event Location Lisbon, Portugal
Event Start Date September 4 - 2017
Event End Date September 8 - 2017
Place of Publication USA
Publisher IEEE
Abstract Text Classifying requirements into functional requirements (FR) and non-functional ones (NFR) is an important task in requirements engineering. However, automated classification of requirements written in natural language is not straightforward, due to the variability of natural language and the absence of a controlled vocabulary. This paper investigates how automated classification of requirements into FR and NFR can be improved and how well several machine learning approaches work in this context. We contribute an approach for preprocessing requirements that standardizes and normalizes requirements before applying classification algorithms. Further, we report on how well several existing machine learning methods perform for automated classification of NFRs into sub-categories such as usability, availability, or performance. Our study is performed on 625 requirements provided by the OpenScience tera-PROMISE repository. We found that our preprocessing improved the performance of an existing classification method. We further found significant differences in the performance of approaches such as Latent Dirichlet Allocation, Biterm Topic Modeling, or Na¨ıve Bayes for the sub-classification of NFRs.
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