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See detailAn AI-assisted Approach for Checking the Completeness of Privacy Policies Against GDPR
Torre, Damiano UL; Abualhaija, Sallam UL; Sabetzadeh, Mehrdad UL et al

in in Proceedings of the 28th IEEE International Requirements Engineering Conference (RE’20) (2020, September)

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See detailAutomated Demarcation of Requirements in Textual Specifications: A Machine Learning-Based Approach
Abualhaija, Sallam UL; Arora, Chetan; Sabetzadeh, Mehrdad UL et al

in Empirical Software Engineering (2020)

A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable ... [more ▼]

A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable writing and markup conventions, one can provide an immediate and unequivocal demarcation of requirements at the time a specification is being developed. However, neither the presence nor a fully accurate enforcement of such conventions is guaranteed. The result is that, in many practical situations, analysts end up resorting to after-the-fact reviews for sifting requirements from other material in a requirements specification. This is both tedious and time-consuming. We propose an automated approach for demarcating requirements in free-form requirements specifications. The approach, which is based on machine learning, can be applied to a wide variety of specifications in different domains and with different writing styles. %The approach is push-button, requiring no user-provided parameters before it can process a given specification. We train and evaluate our approach over an independently labeled dataset comprised of 33 industrial requirements specifications. Over this dataset, our approach yields an average precision of 81.2% and an average recall of 95.7%. Compared to simple baselines that demarcate requirements based on the presence of modal verbs and identifiers, our approach leads to an average gain of 16.4% in precision and 25.5% in recall. We collect and analyze expert feedback on the demarcations produced by our approach for industrial requirements specifications. The results indicate that experts find our approach useful and efficient in practice. We developed a prototype tool, named DemaRQ, in support of our approach. To facilitate replication, we make available to the research community this prototype tool alongside the non-proprietary portion of our training data. [less ▲]

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See detailA Machine Learning-Based Approach for Demarcating Requirements in Textual Specifications
Abualhaija, Sallam UL; Arora, Chetan UL; Sabetzadeh, Mehrdad UL et al

in 27th IEEE International Requirements Engineering Conference (RE'19) (2019)

A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable ... [more ▼]

A simple but important task during the analysis of a textual requirements specification is to determine which statements in the specification represent requirements. In principle, by following suitable writing and markup conventions, one can provide an immediate and unequivocal demarcation of requirements at the time a specification is being developed. However, neither the presence nor a fully accurate enforcement of such conventions is guaranteed. The result is that, in many practical situations, analysts end up resorting to after-the-fact reviews for sifting requirements from other material in a requirements specification. This is both tedious and time-consuming. We propose an automated approach for demarcating requirements in free-form requirements specifications. The approach, which is based on machine learning, can be applied to a wide variety of specifications in different domains and with different writing styles. We train and evaluate our approach over an independently labeled dataset comprised of 30 industrial requirements specifications. Over this dataset, our approach yields an average precision of 81.2% and an average recall of 95.7%. Compared to simple baselines that demarcate requirements based on the presence of modal verbs and identifiers, our approach leads to an average gain of 16.4% in precision and 25.5% in recall. [less ▲]

Detailed reference viewed: 529 (48 UL)