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Improving Requirement Boilerplates Using Sequential Pattern Mining

Abstract : In the field of requirements engineering, the use of the so-called boilerplates (i.e. standard phrases and sentences containing gaps to be filled in) is a popular solution to reduce variation among requirements and writers, and thus to improve the clarity of technical specifications. However, the examples of boilerplates found in the literature are often very general, as they need to be applicable to projects as varied as computer software and aircraft or industrial machines. As a result, they only partially fulfill their role, leaving a lot of freedom to the writers in charge of filling in the gaps. Instead, we would like to propose a bottom-up approach for discovering more specific sequences that could constitute either boilerplates or elements to instantiate these boilerplates. To this end, we investigate whether sequential data mining techniques can be used on a small corpus of genuine requirements written in French at CNES (Centre National d'Études Spatiales), the French Space Agency.
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Contributor : Maxime Warnier <>
Submitted on : Sunday, December 24, 2017 - 1:49:48 AM
Last modification on : Wednesday, October 14, 2020 - 3:44:18 AM
Long-term archiving on: : Sunday, March 25, 2018 - 12:20:23 PM


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  • HAL Id : hal-01672313, version 1


Maxime Warnier, Anne Condamines. Improving Requirement Boilerplates Using Sequential Pattern Mining. Europhras 2017, Nov 2017, Londres, United Kingdom. ⟨hal-01672313⟩



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