Abstract
The classification of software requirements is an essential task in software engineering. Manual classification requires a large amount of efforts, time and cost. Hence, automated techniques are required to classify software requirements. This work aims to develop requirement classification models based on extraction of relevant features from requirement documents and thereafter classifying requirement into functional and non-function requirements. In this paper, different word- embedding techniques to extract numerical features, feature selection to remove irrelevant feature, SMOTE to balance data, and six different classifiers for models training. The experiments have been conducted on PROMISE software engineering dataset. The experimental finding indicate that Word2vec is best way to extracting numerical features from requirement documents, RANK-SUM test is best way to find important features, and SVM-R was found as the best classifier.
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Kumar, L., Baldwa, S., Jambavalikar, S.M., Murthy, L.B., Krishna, A. (2022). Software Functional and Non-function Requirement Classification Using Word-Embedding. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 450. Springer, Cham. https://doi.org/10.1007/978-3-030-99587-4_15
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DOI: https://doi.org/10.1007/978-3-030-99587-4_15
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