Yang, Hui; De Roeck, Anne; Gervasi, Vincenzo; Willis, Alistair and Nuseibeh, Bashar
(2012).
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| URL: | http://crisys.cs.umn.edu/re2012/index.shtml |
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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 lan-guage, 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 lin-guistic 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.
| Item Type: | Conference Item |
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| Copyright Holders: | 2012 IEEE |
| Funders: | UK Engineering and Physical Sciences Research Council (EPSRC) as part of the MaTREx project (EP/F068859/1), Science Foundation Ireland (SFI grant 10/CE/I1855), The European Research Council (ERC). |
| Keywords: | uncertainty; natural language requirements; speculative requirements; uncertainty cues; machine learning; uncertainty scopes; rule-based approach |
| Academic Unit/Department: | Mathematics, Computing and Technology > Computing Mathematics, Computing and Technology |
| Interdisciplinary Research Centre: | Centre for Research in Computing (CRC) |
| Item ID: | 34479 |
| Depositing User: | Hui Yang |
| Date Deposited: | 09 Oct 2012 14:55 |
| Last Modified: | 30 Nov 2012 16:58 |
| URI: | http://oro.open.ac.uk/id/eprint/34479 |
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