Abstract
Software requirement recommendation is one of the most important strategies that is required when first building a new system. It provides a baseline of what exactly the software will do and how it is expected to perform, along with this it also describes how the product will fulfil all the stakeholder’s needs. In this paper, a model to predict the requirement specifications has been proposed. The techniques used were micro requirement mapping using hybrid learning models such as K-Means Clustering hybridized with Support Vector Machines and Cosine Similarity which yielded 80.17% average precision, 82.69% average recall, 81.43% average accuracy, 81.41% of the average measure of harmonic means and 0.1983 false discovery rate and also the proposed KCReqRec system which yielded 94.27% average precision, 96.39% average recall, 95.33% average accuracy, 95.31% of the average measure of harmonic means and 0.0573 False Discovery Rate. These models were run on the Kaggle Software Requirement Dataset integrated with data from the PURE (PUblic REquirements) dataset.
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Nama, V., Deepak, G., Santhanavijayan, A. (2023). KCReqRec: A Knowledge Centric Approach for Semantically Inclined Requirement Recommendation with Micro Requirement Mapping Using Hybrid Learning Models. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 646. Springer, Cham. https://doi.org/10.1007/978-3-031-27440-4_2
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DOI: https://doi.org/10.1007/978-3-031-27440-4_2
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