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 Automatic Classification of Software Requirements in Vietnamese Based on Machine Learning Techniques
Tác giả hoặc Nhóm tác giả: Thi Nham Cao, Dai Tho Dang, Thi My Hanh Le, Nguyen Thanh Binh
Nơi đăng: Journal on Information Technologies & Communications; Số: 06/2022;Từ->đến trang: online;Năm: 2022
Lĩnh vực: Chưa xác định; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
Requirements engineering is often the first stage in the software process to understand the problem statement. Finding mistakes earlier in requirements helps reduce the development cost. One activity contributing to defining clear, complete and precise requirements is classifying requirement items in the specification. This paper presents a classification approach of functional and non-functional requirements in Vietnamese using different supervised machine learning techniques. Five supervised machine learning algorithms, including Na ıve Bayes (NB), Support Vector Machine (SVM),Logistics Regression (LR), Multi-layer Perceptron Neural Net-work (MLP), and FastText, are implemented, trained, tested and compared using a dataset. The experimental results show that NB is the best model in terms of accuracy.
ABSTRACT
Requirements engineering is often the first stage in the software process to understand the problem statement. Finding mistakes earlier in requirements helps reduce the development cost. One activity contributing to defining clear, complete and precise requirements is classifying requirement items in the specification. This paper presents a classification approach of functional and non-functional requirements in Vietnamese using different supervised machine learning techniques. Five supervised machine learning algorithms, including Na ıve Bayes (NB), Support Vector Machine (SVM),Logistics Regression (LR), Multi-layer Perceptron Neural Net-work (MLP), and FastText, are implemented, trained, tested and compared using a dataset. The experimental results show that NB is the best model in terms of accuracy.
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