Abstract
Software engineering forms a well-structured application of engineering methodologies for software development. It forms a branch under engineering discipline which is associated with all the facets of software production. The entire information regarding a particular software is documented and stored in configuration management repositories of a particular organization in the form of software requirement specifications (SRSs). Information about several such software requirements, software paradigms, etc., are then tweeted on developer’s Twitter handles, especially during the release of particular software. Through this paper, a novel approach has been put forth for the recommendation of tweets adhering to the developer’s community and SRS documents to the users in correspondence to their queries. Various machine learning and Web mining techniques have been explored and implemented for tweets classification, grou**, ranking, and prioritization. The results obtained advocate for the proposed methodology to be classified as a highly effective approach.
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Srivastava, R.A., Deepak, G. (2022). Semantically Driven Machine Learning-Infused Approach for Tracing Evolution on Software Requirements. In: Shukla, S., Gao, XZ., Kureethara, J.V., Mishra, D. (eds) Data Science and Security. Lecture Notes in Networks and Systems, vol 462. Springer, Singapore. https://doi.org/10.1007/978-981-19-2211-4_3
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DOI: https://doi.org/10.1007/978-981-19-2211-4_3
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