Abstract
Requirements engineering is one of critical activities in systems development for Automotive Industry. Its outcome is most often represented by a set of documents capturing requirements specifications in natural language. For quality assurance and maturity support of the final products, the requirements must be verified and validated at different testing levels. To achieve this, the requirements are manually labelled to indicate the corresponding testing level. The number of requirements can vary from few hundreds in smaller projects to several thousands in larger projects. Their manual labeling is time consuming and error-prone, thus sometimes incurring an unacceptable high cost. In this paper we report our initial results on the automated classification of requirements in two classes: “Integration Test” and “Software Test” using Machine Learning approaches. Our solution could help the requirements engineers by speeding up the classification of requirements and thus reducing the time to market of final products.
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Notes
- 1.
Data sets are publicly available at https://nlp.stanford.edu/projects/glove/.
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Petcuşin, F., Stănescu, L., Bădică, C. (2020). An Experiment on Automated Requirements Mapping Using Deep Learning Methods. In: Kotenko, I., Badica, C., Desnitsky, V., El Baz, D., Ivanovic, M. (eds) Intelligent Distributed Computing XIII. IDC 2019. Studies in Computational Intelligence, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-32258-8_10
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DOI: https://doi.org/10.1007/978-3-030-32258-8_10
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