Abstract
Requirements are usually “hand-written” and suffers from several problems like redundancy and inconsistency. These problems between requirements or sets of requirements impact negatively the success of final products. Manually processing these issues requires too much time and it is very costly. We propose in this paper to automatically handle redundancy and inconsistency issues in a classification approach. The main contribution of this paper is the use of k-means algorithm for redundancy and inconsistency detection in a new context, which is Requirements Engineering context. Also, we introduce a preprocessing step based on the Natural Language Processing techniques in order to see the impact of this latter to the k-means results. We use Part-Of-Speech (POS) tagging and noun chunking in order to detect technical business terms associated with the requirements documents that we analyze. We experiment this approach on real industrial datasets. The results show the efficiency of the k-means clustering algorithm, especially with the preprocessing.
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Notes
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They studied 50,000 projects around the world, ranging from tiny enhancements to massive systems re-engineering implementations.
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A noun chunk is a noun plus the words describing the noun.
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Acknowledgements
This work is financially supported by the Occitanie region of France in the framework of CLE (Contrat de recherche Laboratoires-Entreprises)-ELENAA (des Exigences en LanguEs Naturelles à leurs Analyses Automatiques) project.
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Mezghani, M., Kang, J., Sèdes, F. (2018). Using k-Means for Redundancy and Inconsistency Detection: Application to Industrial Requirements. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_52
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