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I-Way: A Cloud-Based Recommendation System for Software Requirement Reusability
Abstract
This study addresses the problem of effectively searching and selecting relevant requirements for reuse meeting stakeholders' objectives through knowledge discovery and data mining techniques maintained over a cloud platform. Knowledge extraction of similar requirement(s) is performed on data and meta-data stored in central repository using a novel intersective way method (i-way), which uses intersection results of two machine learning algorithm namely, K-nearest neighbors (KNN) and term frequency-inverse document frequency (TF-IDF). I-way is a two-level extraction framework which represents win-win situation by considering intersective results of two different approaches to ensure that selection is progressing towards desired requirement for reuse consideration. The validity and effectiveness of results of proposed framework are evaluated on requirement dataset, which show that proposed approach can significantly help in reducing effort by selecting similar requirements of interest for reuse.
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