Yang, H. orcid.org/0000-0002-3372-4801, De Roeck, A., Gervasi, V. et al. (2 more authors) (2012) Speculative requirements: Automatic detection of uncertainty in natural language requirements. In: 2012 20th IEEE International Requirements Engineering Conference (RE). 2012 20th IEEE International Requirements Engineering Conference, September 24–28, 2012, Chicago, Illinois, USA. IEEE , pp. 11-20. ISBN 978-1-4673-2783-1
Abstract
Stakeholders frequently use speculative language when they need to convey their requirements with some degree of uncertainty. Due to the intrinsic vagueness of speculative language, speculative requirements risk being misunderstood, and related uncertainty overlooked, and may benefit from careful treatment in the requirements engineering process. In this paper, we present a linguistically-oriented approach to automatic detection of uncertainty in natural language (NL) requirements. Our approach comprises two stages. First we identify speculative sentences by applying a machine learning algorithm called Conditional Random Fields (CRFs) to identify uncertainty cues. The algorithm exploits a rich set of lexical and syntactic features extracted from requirements sentences. Second, we try to determine the scope of uncertainty. We use a rule-based approach that draws on a set of hand-crafted linguistic heuristics to determine the uncertainty scope with the help of dependency structures present in the sentence parse tree. We report on a series of experiments we conducted to evaluate the performance and usefulness of our system.
Metadata
Item Type: | Proceedings Paper |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2012 IEEE. This is an author produced version of a paper subsequently published in 2012 20th IEEE International Requirements Engineering Conference. Uploaded in accordance with the publisher's self-archiving policy. |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 22 May 2017 10:01 |
Last Modified: | 04 Aug 2017 07:44 |
Published Version: | https://doi.org/10.1109/RE.2012.6345795 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.1109/RE.2012.6345795 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:110478 |