Reference : Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/50211
Automated Handling of Anaphoric Ambiguity in Requirements: A Multi-solution Study
English
Ezzini, Saad mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Abualhaija, Sallam mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Arora, Chetan []
Sabetzadeh, Mehrdad []
In press
In Proceedings of the 44th International Conference on Software Engineering (ICSE'22), Pittsburgh, PA, USA 22-27 May 2022
Yes
44th International Conference on Software Engineering
from 22-05-2022 to 27-05-2022
[en] Requirements Engineering ; Natural-language Requirements ; Ambiguity ; Natural Language Processing (NLP) ; Machine Learning (ML) ; Language Models ; BERT
[en] Ambiguity is a pervasive issue in natural-language requirements. A common source of ambiguity in requirements is when a pronoun is anaphoric. In requirements engineering, anaphoric ambiguity occurs when a pronoun can plausibly refer to different entities and thus be interpreted differently by different readers. In this paper, we develop an accurate and practical automated approach for handling anaphoric ambiguity in requirements, addressing both ambiguity detection and anaphora interpretation. In view of the multiple competing natural language processing (NLP) and machine learning (ML) technologies that one can utilize, we simultaneously pursue six alternative solutions, empirically assessing each using a collection of ~1,350 industrial requirements. The alternative solution strategies that we consider are natural choices induced by the existing technologies; these choices frequently arise in other automation tasks involving natural-language requirements. A side-by-side empirical examination of these choices helps develop insights about the usefulness of different state-of-the-art NLP and ML technologies for addressing requirements engineering problems. For the ambiguity detection task, we observe that supervised ML outperforms both a large-scale language model, SpanBERT (a variant of BERT), as well as a solution assembled from off-the-shelf NLP coreference resolvers. In contrast, for anaphora interpretation, SpanBERT yields the most accurate solution. In our evaluation, (1) the best solution for anaphoric ambiguity detection has an average precision of ~60% and a recall of 100%, and (2) the best solution for anaphora interpretation (resolution) has an average success rate of ~98%.
http://hdl.handle.net/10993/50211
FnR ; FNR12632261 > Mehrdad Sabetzadeh > EQUACS > Early Quality Assurance Of Critical Systems > 01/01/2019 > 31/12/2021 > 2018

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
icse22-main-1094.pdfAuthor preprint1.12 MBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.