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AI-based Question Answering Assistance for Analyzing Natural-language Requirements
Ezzini, Saad; Abualhaija, Sallam; Arora, Chetan et al.
2023In Proceedings of the 45th International Conference on Software Engineering (ICSE'23), Melbourne 14-20 May 2023
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Keywords :
Natural-language Requirements; Question Answering (QA); Language Models; Natural Language Processing (NLP); Natural Language Generation (NLG); BERT; T5
Abstract :
[en] Abstract—By virtue of being prevalently written in natural language (NL), requirements are prone to various defects, e.g., inconsistency and incompleteness. As such, requirements are frequently subject to quality assurance processes. These processes, when carried out entirely manually, are tedious and may further overlook important quality issues due to time and budget pressures. In this paper, we propose QAssist – a question-answering (QA) approach that provides automated assistance to stakeholders, including requirements engineers, during the analysis of NL requirements. Posing a question and getting an instant answer is beneficial in various quality-assurance scenarios, e.g., incompleteness detection. Answering requirements-related questions automatically is challenging since the scope of the search for answers can go beyond the given requirements specification. To that end, QAssist provides support for mining external domain-knowledge resources. Our work is one of the first initiatives to bring together QA and external domain knowledge for addressing requirements engineering challenges. We evaluate QAssist on a dataset covering three application domains and containing a total of 387 question-answer pairs. We experiment with state-of-the-art QA methods, based primarily on recent large-scale language models. In our empirical study, QAssist localizes the answer to a question to three passages within the requirements specification and within the external domain-knowledge resource with an average recall of 90.1% and 96.5%, respectively. QAssist extracts the actual answer to the posed question with an average accuracy of 84.2%. Index Terms—Natural-language Requirements, Question Answering (QA), Language Models, Natural Language Processing (NLP), Natural Language Generation (NLG), BERT, T5.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SVV - Software Verification and Validation
Disciplines :
Computer science
Author, co-author :
Ezzini, Saad ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV
Abualhaija, Sallam  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV
Arora, Chetan ;  Deakin University ; Monash University
Sabetzadeh, Mehrdad ;  University of Ottawa > School of Electrical Engineering and Computer Science
External co-authors :
yes
Language :
English
Title :
AI-based Question Answering Assistance for Analyzing Natural-language Requirements
Publication date :
May 2023
Event name :
45th International Conference on Software Engineering
Event date :
from 14-05-2023 to 20-05-2022
Audience :
International
Main work title :
Proceedings of the 45th International Conference on Software Engineering (ICSE'23), Melbourne 14-20 May 2023
Publisher :
IEEE Press
Peer reviewed :
Peer reviewed
FnR Project :
FNR12632261 - Early Quality Assurance Of Critical Systems, 2018 (01/01/2019-31/12/2021) - Mehrdad Sabetzadeh
Funders :
FNR - Fonds National de la Recherche [LU]
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since 13 January 2023

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