Software Requirement Classification Using Machine Learning Algorithms

Vrutik Patel, P. Mehta, Kruti Lavingia
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Abstract

Every software contains numerous processes for building a program, and each step is significant for software requirements. As the globe expands and develops quickly, so does the demand for software. Categorization of requirements can be done manually however doing so requires a lot of human effort, time, money, and risk of inaccurate results. As a result, numerous earlier studies have suggested automating the classification process but consumes lot of time. Here several ways are introduced such that this time taking process can be automated and software requirements can be classified using several machine learning algorithms into various categories. In the process of achieving this there were several algorithms that were taken into consideration which includes KNN, SVM, DT, Naïve Bayes to train dataset and their evaluation metrics were studied.
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使用机器学习算法的软件需求分类
每个软件都包含许多构建程序的过程,每个步骤对软件需求都很重要。随着全球的迅速扩张和发展,对软件的需求也在不断增长。需求的分类可以手工完成,但是这样做需要大量的人力、时间、金钱和不准确结果的风险。因此,许多早期的研究都建议将分类过程自动化,但需要花费大量时间。这里介绍了几种方法,以便这个耗时的过程可以自动化,并且可以使用几种机器学习算法将软件需求分类为不同的类别。在实现这一目标的过程中,考虑了几种算法,包括KNN、SVM、DT、Naïve贝叶斯来训练数据集,并研究了它们的评价指标。
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