Abstract
Writing effective requirement specification documents in the face of dynamic user needs is a significant challenge in modern software development. Keyword extraction algorithms can help to retrieve relevant information from functional requirements provided by users. The process of identifying a concise set of words that capture the essence of user stories without losing important information is accomplished through automatic keyword extraction. This paper presents a comparative study of four popular algorithms for keyword extraction: Rapid Automatic Keyword Extraction (RAKE), Yet Another Keyword Extraction (YAKE), TextRank, and KeyBERT. The algorithms are analyzed based on their ability to extract keywords and provide corresponding scores. Additionally, N-gram analysis is performed using the extracted keywords. The study concludes that the RAKE algorithm exhibits better performance in extracting relevant keywords from user stories compared to the other algorithms.
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Jaagruthi, A., Varshitha, M., Vinaya, K.S., Gupta, V.N., Arunkumar, C., Sabarish, B.A. (2023). User Story-Based Automatic Keyword Extraction Using Algorithms and Analysis. In: Bhateja, V., Carroll, F., Tavares, J.M.R.S., Sengar, S.S., Peer, P. (eds) Intelligent Data Engineering and Analytics. FICTA 2023. Smart Innovation, Systems and Technologies, vol 371. Springer, Singapore. https://doi.org/10.1007/978-981-99-6706-3_30
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DOI: https://doi.org/10.1007/978-981-99-6706-3_30
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