Reference : Automated Recommendation of Templates for Legal Requirements
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/43561
Automated Recommendation of Templates for Legal Requirements
English
Sleimi, Amin mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Ceci, Marcello mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Sabetzadeh, Mehrdad mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Dann, John []
2020
Proceedings of the 28th IEEE International Requirements Engineering Conference (RE'20)
Yes
International
28th IEEE International Requirements Engineering Conference (RE'20)
from 31-08-2010 to 04-09-2020
[en] Legal Requirements ; AI-assisted RE ; Natural Language Processing ; Requirements Templates
[en] [Context] In legal requirements elicitation, requirements analysts need to extract obligations from legal texts. However, legal texts often express obligations only indirectly, for example, by attributing a right to the counterpart. This phenomenon has already been described in the Requirements Engineering (RE) literature. [Objectives] We investigate the use of requirements templates for the systematic elicitation of legal requirements. Our work is motivated by two observations: (1) The existing literature does not provide a harmonized view on the requirements templates that are useful for legal RE; (2) Despite the promising recent advancements in natural language processing (NLP), automated support for legal RE through the suggestion of requirements templates has not been achieved yet. Our objective is to take steps toward addressing these limitations. [Methods] We review and reconcile the legal requirement templates proposed in RE. Subsequently, we conduct a qualitative study to define NLP rules for template recommendation. [Results and Conclusions] Our contributions consist of (a) a harmonized list of requirements templates pertinent to legal RE, and (b) rules for the automatic recommendation of such templates. We evaluate our rules through a case study on 400 statements from two legal domains. The results indicate a recall and precision of 82,3% and 79,8%, respectively. We show that introducing some limited interaction with the analyst considerably improves accuracy. Specifically, our human-feedback strategy increases recall by 12% and precision by 10,8%, thus yielding an overall recall of 94,3% and overall precision of 90,6%.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Fonds National de la Recherche - FnR
http://hdl.handle.net/10993/43561
FnR ; FNR11801776 > Lionel Briand > SCARLET > Semantic Metadata and Compliance Rule Extraction from Legal Texts > 01/01/2018 > 31/12/2020 > 2017

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